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  Step 6: Record Data   Now that we have a place to permanently store the values coming from the EMS engine simulator, we'll write a Service to take samples and place them within the Info Table.   Part of that Service, though, will be incrementing the identifier, so we'll need to create one last Property.   Ensure that you're on the Properties and Alerts tab of EdgeThing. At the top-left, click + Add.     In the Name field of the slide-out on the right, type identifier. Change the Base Type to NUMBER. Click Persistent. Click Has Default Value. In the Has Default Value field, type 0.   At the top-right, click the "Check" button for Done. At the top, click Save.   Store the Property Values   With all the pieces in place, we can now create our Service to add entries to our Info Table Property.   At the top of EdgeThing, click Services.   At the top-left, click + Add.   On the "+ Add" drop-down, select Local (JavaScript). In the Service Info > Name field, type recordService.     Expand Me/Entities > Properties.     Click the arrow beside infoTableProperty.     Type .AddRow({ after me.infoTableProperty to being the process of calling the "AddRow()" function.     We now have called the function which will add a row of information to the Info Table Property, one entry for each column of the formatting Data Shape.   We just need to specify which values go into which column.   Add the following lines to store the individual Identifier count into the first column of the Info Table Property: identifier:me.identifier, Because we want the identifier in the stored data to increment on each run, and we want to start the count at 1 (and the Default Value is 0), add the following line to the top of the Service: me.identifier=me.identifier+1;       Add the low_grease value with the following line: low_grease:me.low_grease, Add the following lines to store the five frequency bands of the first sensor: s1_fb1:me.s1_fb1, s1_fb2:me.s1_fb2, s1_fb3:me.s1_fb3, s1_fb4:me.s1_fb4, s1_fb5:me.s1_fb5, Add the final lines to store the five frequency bands of the second sensor and close out the AddRow() function: s2_fb1:me.s2_fb1, s2_fb2:me.s2_fb2, s2_fb3:me.s2_fb3, s2_fb4:me.s2_fb4, s2_fb5:me.s2_fb5 }); You completed Service should look like the following: me.identifier=me.identifier+1; me.infoTableProperty.AddRow({ identifier:me.identifier, low_grease:me.low_grease, s1_fb1:me.s1_fb1, s1_fb2:me.s1_fb2, s1_fb3:me.s1_fb3, s1_fb4:me.s1_fb4, s1_fb5:me.s1_fb5, s2_fb1:me.s2_fb1, s2_fb2:me.s2_fb2, s2_fb3:me.s2_fb3, s2_fb4:me.s2_fb4, s2_fb5:me.s2_fb5 });     At the top, click Done. At the top, click Save.       Run the Service   With our Service completed, let's run it to store a sampling of the data coming from our EMS Engine Simulator.   Under the Execute column in the center, on the recordService row, click the "Play" icon for Execute service. At the bottom-right, click Execute.     At the bottom-right, click Done. At the top, click Properties and Alerts.   Under the Value column, on the infoTableProperty row, click the "Pencil" icon for Set value of property.   Note that the Service has captured a snap-shot of the vibration data and grease condition and permanently stored it within the Info Table Property. You now have not only an Engine Simulator that is constantly sending data from a remote EMS, but a way to permanently record data at points that you deem significant.   Feel free to return to the Service and call it several more times. Each time, the values coming from the Engine Simulator will be stored in another entry in the Info Table Property.       Step 7: Next Steps   Congratulations! You've successfully completed the Use the EMS to Create an Engine Simulator guide, and learned how to:   Modify an EMS Template Provision Thing Properties and Values from an EMS rather than Foundation Send information from an EMS to Foundation Store large amounts of data in an InfoTable Property Create a simulator for testing   The next guide in the Vehicle Predictive Pre-Failure Detection with ThingWorx Platform learning path is Engine Simulator Data Storage. Learn More We recommend the following resources to continue your learning experience: Capability Guide Build Engine Simulator Data Storage Build Implement Services, Events and Subscriptions Additional Resources If you have questions, issues, or need additional information, refer to: Resource Link Community Developer Community Forum Support Analytics Builder Help Center  

    Step 5: Create InfoTable   Now that we have connected values coming from our EMS engine simulator, we want a method of permanent storage whenever we feel it's appropriate to take a sample.   From repeated sampling, we'll be able to build up a historical record usable for both manual inspection, as well as automatic analysis via ThingWorx Analytics (though ThingWorx Analytics is beyond the scope of this guide).   To hold these records, we'll use an Info Table Property.   But any time that you create an Info Table, you first need a Data Shape to format the columns.   Click Browse > MODELING > Data Shapes.     At the top-left, click + New.   In the Name field, type esimDataShape.     If Project is not already set, search for and select PTCDefaultProject. At the top, click Field Definitions.     We now want to add a separate Field Definition for each entry of our engine simulator data, i.e. low_grease, s1_fb1 through s1_fb5, and s2_fb1 through s2_fb5.   In addition, we'll add an additional field named identifier which simply keeps a rolling count of the current log entry number.   Click + Add.     In the Name field on the right slide-out, type identifier Change the Base Type to NUMBER. Check Is Primary Key   At the top-right, click the "Check with a +" button for Done and Add.     Repeatedly add additional definitions as per the chart below: Note that you will NOT check the "Is Primary Key" box, as you only need one, i.e. identifier. Name Base type low_grease NUMBER s1_fb1 NUMBER s1_fb2 NUMBER s1_fb3 NUMBER s1_fb4 NUMBER s1_fb5 NUMBER s2_fb1 NUMBER s2_fb2 NUMBER s2_fb3 NUMBER s2_fb4 NUMBER Create one additional entry for s2_fb5 and NUMBER, but click the "Check" button for DONE. At the top, click Save.     Create Info Table   Now that we have a Data Shape we can add an Info Table Property to EdgeThing. Return to the Properties and Alerts tab of EdgeThing.   At the top-left, click + Add.   In the Name field of the slide-out on the right, type infoTableProperty.   Change the Base Type to INFOTABLE.   In the new Data Shape field, search for and select esimDataShape.   Check the Persistent checkbox.   At the top-right, click the "Check" button for Done. At the top, click Save.     Click here for Part 4 of this guide.

  Use the Edge MicroServer (EMS) to simulate an engine with vibration sensors.   GUIDE CONCEPT   The Edge MicroServer (EMS) facilitates connectivity from Edge devices to ThingWorx Foundation.   It’s often easier, though, to start development with simulated Edge values rather than hooking up sensors.   This guide will show you how to simulate vibration values of an engine using the EMS.     YOU'LL LEARN HOW TO   Modify an EMS Template Provision Thing Properties and Values from an EMS rather than Foundation Send information from an EMS to Foundation Store large amounts of data in an InfoTable Property Create a simulator for testing   NOTE:  The estimated time to complete all parts of this guide is 30 minutes.     Step 1: Scenario   MotorCo manufactures, sells, and services commercial motors.   Recently, MotorCo has been developing a new motor, and they already have a working prototype.   However, they’ve noticed that the motor has a chance to FAIL CATASTROPHICALLY if it’s not properly serviced to replace lost grease on a key moving part.     In order to prevent this type of failure in the field, MotorCo has decided to instrument their motors with sensors which record vibration.   The hope is that these sensors can detect certain vibrations which indicate required maintenance before a failure occurs.   In this guide, you’ll begin this monitoring process by using ThingWorx Foundation to monitor and record vibration data from the prototype motor. In particular, you will learn how to provision Thing Properties and Values from an EMS, as well as how to permanently store these values for analysis in an Info Table Property.   These types of modifications to an EMS can be extremely helpful for the automotive segment in particular. For instance, each car that comes off the factory line could have custom, auto-generated EMS scripting that would dynamically create Foundation information for each car in the fleet. This could be a massive time-savings versus manually generating Thing Properties directly within Foundation.   Because the motor is still in the process of being instrumented with sensors, you’ll get all the functionality in-place beforehand by constructing a motor simulator using the Edge MicroServer (EMS).     Step 2: Modify config.lua   In the previous Use the Edge MicroServer (EMS) to Connect to ThingWorx  guide, you installed the EMS on a Windows PC, configured it to talk to ThingWorx Foundation, and then created an EdgeThing on Foundation to complete the connectivity.   This guide assumes that you have already completed that Windows EMS guide and have an active EMS connection to the EdgeThing.   Perform the following steps to modify this connection to increase the task rate of the EMS, which we'll use in the following steps to update Properties more quickly.   On your Windows PC, select the Windows PowerShell window where the luaScriptResource.exe program is running.   Type Ctrl-C to close the luaScriptResource.exe operation, i.e. hold the Control key and hit the C key.   Minimize the luaScriptResource.exe PowerShell window, and activate the wsemse.exe PowerShell window.   Type Ctrl-C to close the wsems.exe operation.   Return to Foundation, and note that EdgeThing is not connected.   Navigate to the C:\CDEMO_EMSE\etc directory.   Open config.lua in your prefered text-editor.   Change scanRate to 1000. Add the following line below the scanRate line: taskRate = 1000,   The final code of config.lua should be the following Note that the EMS may have slightly modified your config.lua file, such as adding a data_security_key line. Leave these EMS-generated modifications alone. scripts.log_level = "WARN" scripts.script_resource_ssl = false scripts.script_resource_authenticate = false scripts.EdgeThing = { file = "thing.lua", template = "YourEdgeThingTemplate", scanRate = 1000, taskRate = 1000, sw_update_dir = "C:\\CDEMO_EMS\\updates" } Save the config.lua file.     Click here to view Part 2 of this guide.

  Step 5: Format Timed Values   At the top, click the Services section of scts_thing.   In the Ihnerited Services section, you will see the built-in services of the Statistical Calculation Thing Shape. These services can perform a variety of analytical calculations.   Timed Values Service   The Statistical Calculation Thing Shape can only perform analytics operations on time-series datasets. However, accessing a time-series Value Stream can have a performance hit on the system.   Instead, a Property with an Info Table using a timestamp/value Data Shape is used as the universal input to each built-in service of the Statistical Calculation Thing Shape.   For efficiency, we only reference the Value Stream once to create a formatted timedValues that is used as an input to all other service calls.   At the top, click Services.   Click + Add.   In the Name field, enter timed_values_service. In the Javascript field, copy and past the following code: me.timed_values = me.QueryTimedValuesForProperty({ propertyName: "numbers", maxItems: 10, startTime: me.start_time, endTime: me.end_time });   At the bottom, click Save and Execute.   Click Done, and return to Properties and Alerts. On the timed_values property row, click the pencil icon for Set value of property.   In the pop-up, note that there are now seven entries - each with the 1, 5, 9, 5, 9, 1, and 9 values and the timestamps when you entered them.   In the pop-up, click Cancel. If needed, in the top-right, click the icon to close the slide-out.   Step 6: Calculation Services   Now that the numbers, start_time, end_time, and timed_values service inputs have been set, you can use the built-in Services of the Statistical Calculation Thing Shape to perform a variety of analytics calculations.   Mean Service   First, you will utilize the built-in CalculateMeanValue service.   The dataset is the following: 1, 5, 9, 5, 9, 1, 9.   As such, the mean should be (1+5+9+5+9+1+9)/7 = 39/7 = 5.571...   Return to the Services section. At the top, click + Add. In the Name field, enter mean_service. In the Javascript code section, copy and paste the following: me.mean_result = me.CalculateMeanValue({ timedValues: me.timed_values }); At the top, click Save and Continue.   At the bottom, click Execute. Click Done, then return to the Properties and Alerts section. Note that the mean_result property now has the value 5.571....     Median Service   Next, you will utilize the built-in CalculateMedianValue service.   With our dataset having 5 as the middle value, that should be the result.   Return to the Services section. At the top, click + Add. In the Name field, enter median_service. In the Javascript code section, copy and paste the following: me.median_result = me.CalculateMedianValue({ timedValues: me.timed_values }); At the top, click Save and Continue. At the bottom, click Execute. Click Done, and return to the Properties and Alerts section. Note that the median_result Property now has the value 5.     Mode Service   You will now utilize the built-in CalculateModeValue service.   With the dataset having 9 as the most common value, that should be the result.   Return to the Services section. At the top, click + Add. In the Name field, enter mode_service. In the Javascript code section, copy and paste the following: me.mode_result = me.CalculateModeValue({ timedValues: me.timed_values }); At the top, click Save and Continue.   At the bottom, click Execute. Click Done, and return to the Properties and Alerts section. On the mode_result row and under the Value column, click the "pencil" icon for Set value of property.   In the popup, note that the mode_result Property now has the value 9.   Click Cancel to close the popup. If necessary, at the top-right, click the button to close the slide-out.   Standard Deviation Service   Lastly, you will utilize the built-in CalculateStandardDeviationValue service.   There are multiple free Standard Deviation calculators to check the answer.   Accordingly, the Standard Deviation should be 3.59894...   Return to the Service section. At the top, click + Add. In the Name field, enter standarddev_service. In the Javascript code section, copy and paste the following: me.standarddev_result = me.CalculateStandardDeviationValue({ timedValues: me.timed_values }); At the top, click Save and Continue.   At the bottom, click Execute. Click Done, and return to the Properties and Alerts section. Note that the standarddev_result property now has the value 3.59894...       Step 7: Other Options   The Mean, Median, Mode, and Standard Deviation services you have completed are just a sampling of what the Statistical Calculation Thing Shape offers.   Below is a table of additional built-in services:   Calculation Service Name Description Binned Data Distribution for Bin Size CalculateBinnedDataDistributionForBinSize Calculate the binned distribution of data points based on the desired bin size. Binned Data Distribution for Number of Bins CalculateBinnedDataDistributionForNumberOfBins Calculate the binned distribution of data points based on the desired number of bins. Confidence Interval Values CalculateConfidenceIntervalValues Confidence Interval Values Based on a specified confidence interval percentage, calculate the minimum, median, and maximum interval values. Five Number Property Values CalculateFiveNumberPropertyValues Calculate the five number values: minimum, lower quartile, median, upper quartile, and maximum. Fourier Transform CalculateFourierTransform Calculate the results of running the fast Fourier transform on the specified values. Maximum Value CalculateMaximumValue Calculate the maximum property value in the provided infotable. Minimum Value CalculateMinimumValue Calculate the minimum property value in the provided infotable. Sampling Frequency Values CalculateSamplingFrequencyValues Calculate the sampling frequency values: minimum, median, and maximum.     Step 8: Next Steps   Congratulations!   In this guide, you have learned how to:   Create a Value Stream and Data Shape Create a Thing with the Statistical Calculation Thing Shape Modify a property to record values to the Value Stream Utilize various built-in services for analytical calculations   Learn More   We recommend the following resources to continue your learning experience:   Capability Guide Build Build a Predictive Analytics Model Build Operationalize an Analytics Model   Additional Resources   If you have questions, issues, or need additional information, refer to:   Resource Link Community Developer Community Forum Support Descriptive Analytics Help Center

  Use the Statistical Calculation Thing Shape to Execute Common Statistical Functions   GUIDE CONCEPT   This project will introduce the Statistical Calculation Thing Shape.   The steps in this guide outline how to utilize Descriptive Analytics in ThingWorx Analytics to perform common mathematical analyses on data sets. You will learn how to use the Statistical Calculation Thing Shape's built-in functionality to calculate the mean, median, mode, and other useful insights.     YOU'LL LEARN HOW TO   Create a Value Stream and Data Shape Create a Thing with the Statistical Calculation Thing Shape Modify a Property to record values to the Value Stream Utilize various built-in services to perform the Mean, Median, Mode, and Standard Deviation calculations   NOTE: The estimated time to complete this guide is 30 minutes.     Step 1: Introduction   Descriptive Analytics enables users to perform on-demand common statistical calculations and enable statistical monitoring. Output from these Services can then easily be added to IoT applications built with the ThingWorx platform.   For example, output generated by Descriptive Analytics can be used to build solution-specific visualizations (Mashups or Widgets), create Alerts based on logged Property values, or generate transformed data for machine learning processes.   Descriptive Analytics includes two microservers, each with its own set of statistical Services. This guide will deal specifically with the Statistical Calculation Thing Shape.   This Thing Shape will add a variety of built-in Services to any Thing or Thing Template you choose, such as calculations for Mean, Median, Mode, or Standard Deviation.   To perform these statistical calculations, a Property must have time-series data logged to a Value Stream.   In addition, the Statistical Calculation Thing Shape has been optimized to perform calculations only on particular time ranges and with a maximum entry-count. This helps minimize some of the performance hits that can occur from repeatedly accessing Value Streams.   You perform this "data grooming" via the QueryTimedValuesForProperty Service, which is also part of the Statistical Calculation Thing Shape.   In addition to the name of a Property with values logged to a Value Stream, you also provide a Start Date, End Date, and Max Item Count.   The QueryTimedValuesForProperty Service then formats the values from the Value Stream to work with for the other built-in statistical calculation Services.   Step 2: Create Prerequisites   The inputs to the built-in Services of the Statistical Calculation Thing Shape require time-series data stored in a Value Stream. Setting a Thing's Properties to be Logged will store valid time-series data on which to perform Descriptive Analytics.   Create Value Stream   Follow the steps below to create a Value Stream that you will later tie to a Thing.   On the ThingWorx Composer Browse tab, click DATA STORAGE > Value Streams, + New.   Select ValueStream and click OK.   In the Name field, enter scts_valuestream. If Project is not already set, search for and select PTCDefaultProject.   At the top, click Save.   Create Data Shape   You will need a Data Shape to format the timed_values Property we will create in the next step.   On the ThingWorx Composer Browse tab, click MODELING > Data Shapes, + New.   In the Name field, enter scts_timed_values_datashape. If Project is not already set, search for and select PTCDefaultProject.   At the top, click Field Definitions.   Click + Add. On the right slide-out, in the Name field, enter value. Change the Base Type to NUMBER.   At the top-right, click the "check with a +" icon for Done and Add. In the Name field, enter timestamp. Change the Base Type to LONG.   At the top-right, click the "check" icon for Done. At the top, click Save.       Step 3: Create Thing   Next, you will create a Thing and tie the previously-created Value Stream to it.   You will assign the Statistical Calculation Thing Shape to get a variety of built-in analytics Services to manipulate the data. You will also create a series of Properties to facilitate the new Services.   On the ThingWorx Composer Browse tab, click MODELING > Things, + New.   In the Name field, enter scts_thing. If Project is not already set, search for and select PTCDefaultProject. In the Base Thing Template field, search for and select GenericThing. In the Implemented Shapes field, search for and select StatisticalCalculationThingShape. In the Value Stream field, search for and select scts_valuestream.   At the top, click Save.   Add Properties   Next, you will add a series of Properties to scts_thing.   Perform the following steps repeatedly until all Properties have been added.   At the top, click Properties and Alerts.   Click + Add. On the right slide-out, in the Name field, enter numbers. Change the Base Type to NUMBER. Click Persistent. Click Logged.   At the top-right, click the "check with a +" icon for Done and Add. Repeat steps 3-7 until all of the properties in the table below have been added. NOTE: For the final standarddev_result Property, do NOT click Done and Add; you'll just click the check for Done instead. Property Name Base Type Data Shape Persistent  Logged start_time DATETIME N/A Checked NOT checked end_time DATETIME N/A Checked NOT checked timed_values INFOTABLE scts_timed_values_datashape Checked NOT checked mean_result NUMBER N/A Checked NOT checked median_result NUMBER N/A Checked NOT checked mode_result INFOTABLE none, i.e. leave it blank Checked NOT checked standarddev_result NUMBER N/A Checked NOT checked     At the top-right, click the "check" icon for DONE. At the top, click Save.   Step 4: Set Properties   You will now set the properties to guarantee that each calculation gives us a different answer.   Mean is the average. Median is the "middle" number from the dataset. Mode is the most common number. Standard Deviation is a measure of the "dispersion" of the dataset.   You will use the following dataset: 1, 5, 9, 5, 9, 1, 9.   The mean is 39 / 7 = 5.571...   The median is 5, as 5 is the middle of 1, 5, and 9.   Mode is 9, because 9 appears most commonly in the dataset.   Standard Deviation is 3.5989...   Perform the following steps to enter the above values into the numbers property.   Set numbers   Under the Value column and on the numbers property row, click the "pencil" icon for Set value of property.   On the right slide-out, enter 1.   At the top-right, click the "check" icon for Done. At the top, click Save.   Repeat steps 1-4 above, changing the value each time according to the table below: Value Change Count  Entered Value 2nd 5 3rd 9 4th 5 5th 9 6th 1 7th 9   You also need to set the start_time and end_time. These are dates used to define the time-period in which the Statistical Calculation Thing Shape will search for values.   For instance, you could set the calculations to run at midnight, and then use the past 24-hours as your time-period.   For this example, set dates to be 24 hours prior to the time at which you set the above values of the numbers property, through 24 hours in the future.   Set start_time   Under the Value column and on the start_time Property row, click the "pencil" icon for Set value of property.   On the right slide-out, search for and select the previous day.   At the top-right, click the "check" icon for Done. At the top, click Save.   Set end_time   Under the Value column and on the end_time property row, click the "pencil" icon for Set value of property.   On the right slide-out, search for and select the following day. At the top-right, click the "check" icon for Done. At the top, click Save.     Click here to view Part 2 of this guide.

  Create an Engine Failure Prediction GUI to warn about customer issues.   GUIDE CONCEPT   This guide will use ThingWorx Foundation's Mashup Builder to create a Graphical User Interface (GUI) to display results from Analytics Manager comparisons of your analytical model to real-time data.   Following the steps in this guide, you will learn how to utilize Widgets and backend data to quickly visualize customer failure conditions.     YOU'LL LEARN HOW TO   Create a Mashup Add Widgets to represent different data Bring Backend Data into the Mashup Tie data to Widgets View Analytical Results in a convenient GUI   NOTE:  The estimated time to complete all parts of this guide is 30 minutes.     Step 1: Scenario   In this guide, we're finishing up with the MotorCo scenario where an engine can fail catastrophically in a low-grease condition.   In previous guides, you've gathered and exported engine vibration-data from an Edge MicroServer (EMS) and used it to build an engine analytics model. You've even put that analytical model into service to give near-immediate results from current engine-vibration readings.   The goal of this guide is to create a GUI to visualize those predicted "low grease" conditions to facilitate customer warnings.     GUI-creation to visualize analytical model deployment can be extremely helpful for the automative segment in particular. For instance, each car that comes off the factory line could have an EMS constantly sending data from which an analytical model could automatically detect engine trouble.   This could enable your company to offer an engine monitoring subscription service to your customers.   This guide will show you how to visualize the results of an engine analytic model for a "smart, connected products" play.     Step 2: Create Mashup   Mashups are ThingWorx Foundation's method of creating Graphical User Interfaces (GUIs).   Mashups are created through the Mashup Builder interface.   Before you can use this drag-and-drop interface, you must first create a new Mashup.      1. In ThingWorx Foundation Composer, click Browse > Visualization > Mashups.      2. Click + New.        3. Leave the defaults and click OK.        4. In the Name field, type EFPG_Mashup.      5. If Project is not already set, search for and select PTCDefaultProject.        6. At the top, click Save.        7. At the top, click Design. You are now in the Mashup Builder interface, where you can drag-and-drop graphical elements (Widgets) to create your GUI.      8. At the top-left, click the Layout tab.        9. Change Positioning to Static. This removes any auto-positioning or dynamic-sizing from our Mashup, so that we can place and size Widgets manually.      10. At the top, click Save again.       Step 3: Add Widgets   We now want to add several Widgets to our Mashup by dragging-and-dropping them into the central Canvas area.   Return to the Widgets tab in the top-left.       2. In the Filter Widgets field, type label.        3. Drag-and-drop five (5) Label Widgets onto the central Canvas area.      4. Select the Label Widgets, go to the Properties in the bottom-left, and change the Label's LabelText to s1_fb1 through s1_fb5.         5. Filter Widgets for text field, and then drag-and-drop five (5) Text Field Widgets onto the central Canvas area.        6. Drag-and-drop two (2) more Label Widgets onto the central Canvas area.      7. Change their LabelText Properties to Result_low_grease and Result_low_grease_mo.        8. Drag-and-drop two (2) more Text Field Widgets onto the central Canvas area.        9. In the top-left Widget's tab, change Category to Legacy.      10. Drag-and-drop an Auto Refresh Widget onto the Canvas.        11. At the top, click Save.         Step 4: Add Data   Now that we have a rough set of Widgets in place to display Data in our Mashup, we need to bring in backend Data from Composer.      1. In the top-right, ensure the Data tab is active.      2. Click the green + button.        3. In the Entity Filter field, search for and select EdgeThing.      4. In the Services Filter field, type getprop.      5. Click the right-arrow beside GetProperties.      6. On the right, check Execute on Load.        7. In the bottom-right, click Done.      8. On the right, expand GetProperties.        9. At the top, click Save.       Step 5: Bind Data   Now that backend Data is accessible inside Mashup Builder, we need to bind it to the various Widgets.   Drag-and-drop Data > Things_EdgeThing > GetProperties > s1_fb1 to the top-left Text Field under the s1_fb1 Label Widget.           2. On the Select Binding Target pop-up, click Text.         3. Repeat for s1_fb2 through s1_fb5 on the 2nd-5th Text Field Widgets.       4. Drag-and-drop Data > Things_EdgeThing > GetProperties > Result_low_grease to the top-right Text Field under the Result_low_grease Label Widget, and select Text on the Binding pop-up.       5. Repeat for Result_low_grease_mo on the Text Field just below.       6. Click the Auto Refresh Widget to select it.       7. In the bottom-left Properties, set RefreshInterval to 1.         8. With the Auto Refresh Widget still selected, click the top-left corner to reveal a drop-down.       9. Drag-and-drop the Refresh Event onto Things_EdgeThing > GetProperties.       10. On the right, click the GetProperties Mashup Data Service to reveal all of its interactions in the bottom-center Connections window.              11. At the top, click Save.       Step 6: View Mashup   In the last guide, we disabled the Analysis Event after confirming that appropriate analytical jobs were being created and completed whenever new data from our EMS Engine Simulator entered Foundation.   We now need to re-enable that Event so that we can see the results in our Mashup.       1. Return to Analytics > Analytics Manager > Analysis Events.       2. Select the Event, and click Enable.   View Analytical Results   With the Analysis Event enabled, we can now view our Mashup to watch engine-failure-predictions be displayed in real time.       1. Return to EFPG_Mashup.       2. At the top, click View Mashup.       3. Wait and notice how the refresh occurs every second, including both true and false analytics results for the "low grease" condition.      4. When you are satisfied that you have observed enough true/false results, return to Analytics > Analytics Manager > Analysis Events and Disable the event.   Through this Learning Path, you have done all of the following:   Connect a remote device to Foundation using the Edge MicroServer (EMS) Fed relevant product-data to the Foundation backend Formatted and exported that data in a manner which Analytics could understand Imported that data and used it to build an Analytics Model of your remote device Started feeding real-time remote data into the model to achieve immediate failure-prediction results Create a GUI to easily visualize those results   You now have a completed Minimum Viable Product (MVP) for a Service Play with MotorCo's new engines.   These new engines may have a known-failure condition, but (because you've instrumented those engines to provide constant data) you can monitor them and predict failures before they actually happen.   This could easily be an up-sell scenario for MotorCo's customers. They could simply purchase the engine. Or they could both purchase the engine and enroll in a service contract where you notified them of impending issues.   What was originally a single-point source of income is now ongoing        Step 7: Next Steps   Congratulations. You've completed the Engine Failure-Prediction GUI guide. In this guide you learned how to:   Create a Mashup Add Widgets to represent different data Bring Backend Data into the Mashup Tie data to Widgets View Analytical Results in a convenient GUI   The next guide in the Vehicle Predictive Pre-Failure Detection with ThingWorx Platform learning path is Enhanced Engine Failure Visualization.   Learn More   We recommend the following resources to continue your learning experience:   Capability Guide Build Implement Services, Events, and Subscriptions Guide   Additional Resources   If you have questions, issues, or need additional information, refer to:   Resource Link Community Developer Community Forum Support Analytics Manager Help Center

    Step 6: Map Data   Now that the event is created, we need to map the Properties of EdgeThing to the fields required to invoke an Analysis Job.   We'll start with the Inputs.   Select the previously-created Event, and click Map Data....   Click Inputs Mapping.   In Source Type, select Thing. In Source, search for and select EdgeThing.   On the left, scroll down and select s1_fb1. Note that you do NOT want the s1_fb1 that is part of the InfoTable Property, because the Info Table Property only stores recorded data, not live data. On the right, select _s1_fb1, the first frequency band required for the Model to make a prediction.   Click the Map button in the center.   Repeat this mapping process for for s1_fb2 through s1_fb5.   Map causalTechnique to causalTechnique in the same manner. This is a String Property in EdgeThing with a Default Value of "FULL_RANGE". Map goalField to goalField in the same manner. This is a String Property in EdgeThing with a Default Value of "low_grease".   Map Results   Now that the Inputs are mapped, we also want to map the Results.   Click Results Mapping on the left.   Map _low_grease to Result_low_grease. Map _low_grease_mo to Result_low_grease_mo.   Click Close to close the mapping pop-up.   Enable Event   Now that we've done the mapping from Foundation to Analytics, let's Enable the Analysis Event so that it can automatically generate and process Analysis Jobs. Select the mapped Analysis Event. Select Enable.   Now that you have enabled the Analysis Event, the new data will be submitted to Analytics Manager whenever EdgeThing's s1_fb1 Property changes.   An Analysis Job will automatically run, with a predictive score sent back and stored in EdgeThing's Result_low_grease (Boolean) and Result_low_grease_mo (Number) Properties.     Step 7: Check Jobs   In this step, we'll confirm that the automatic analysis of information coming from remote devices is operational.   On the ThingWorx Composer Analytics tab, click Analytics Manager > Analysis Jobs.   Uncheck Filter Completed Jobs.   Select a Job and click View.... Click Results.   NOTE: You will see true or false, corresponding to either a low grease or no low grease condition. Using this technology, you could create a paid customer service, where you offered to monitor remote engines, in return for automatically shutting them down before they experience catastrophic engine failure.   For that example implementation, you would utilize the EdgeThing.Result_low_grease BOOLEAN Property to trigger other actions.   For instance, you could create an Alert Event which would be triggered on a true reading.   You could then have a Subscription which paid attention to that Alert Event, and performed an action, such as sending an automatic shutdown command to the engine when it was experiencing a likely low grease event.   NOTE: We recommend that you return to the ThingWorx Composer Analytics > Analytics Manager > Analysis Events tab and Disable the Event prior to continuing. Since the simulator generates an Event every ~1 seconds, this can create a large number of Events, which can fill up your log.       Step 8: Next Steps   Congratulations. You've completed the Manage an Engine Analytical Model guide. In this guide you learned how to:   Define an Analysis Provider that uses the built-in Analytics Server Connector Publish a Model from Analytics Builder to Manager Create an Analysis Event which takes data from ThingWorx Foundation and decides whether or not a failure is likely   The next guide in the Vehicle Predictive Pre-Failure Detection with ThingWorx Platform learning path is Engine Failure-Prediction GUI.   Learn More   We recommend the following resources to continue your learning experience:    Capability     Guide Build Implement Services, Events, and Subscriptions Guide   Additional Resources   If you have questions, issues, or need additional information, refer to:    Resource              Link Community Developer Community Forum Support Analytics Manager Help Center      

    Use Analytics Manager to automatically perform engine analytical calculations.   Guide Concept   This guide will use ThingWorx Analytics Manager to compare external-data from an Edge MicroServer (EMS) "Engine Simulator" to a previously-built analytical model.   Following the steps in this guide, you will learn how to deploy the model which you created in the earlier Builder guide.   We will teach you how to utilize this deployed model to investigate whether or not live data indicates a potential engine failure.   You'll learn how to   Deploy and execute computational models Define and trigger Analysis Events Map incoming data to the Model Map analytic outputs to applications   NOTE: This guide's content aligns with ThingWorx 9.3. The estimated time to complete this guide is 60 minutes       Step 1: Scenario   In this guide, we're continuing the same MotorCo scenario, where an engine can fail catastrophically in a low-grease condition.   In previous guides, you've gathered and exported engine vibration-data from an Edge MicroServer (EMS) and used it to build an engine analytics model.   The goal of this guide is to now operationalize that previously-created model to analyze individual, external readings to see if the "low grease" condition is currently happening.     Analytical model creation can be extremely helpful for the automotive segment in particular. For instance, each car that comes off the factory line could have an EMS constantly sending data from which an analytical model could automatically detect engine trouble.   This could enable your company to offer an engine monitoring subscription service to your customers.   This guide will show you how to put an analytic model of your engine into service to actively monitor performance.       Step 2: Configure Provider   In ThingWorx terminology, an Analysis Provider is a mathematical analysis engine.   Analytics Manager can use a variety of Providers, such as Excel, Mathcad, or even Analytics Server pre-built ones.   In this guide, we use the built-in AnalyticsServerConnector, a Provider that has been specifically created to work seamlessly in Manager and to use Builder Models.   From the ThingWorx Composer Analytics tab, click Analytics Manager > Analysis Providers > New....   In the Provider Name field, type Vibration_Provider. In the Connector field, search for and select TW.AnalysisServices.AnalyticsServer.AnalyticsServerConnector.   Leave the rest of the options at default and click Save.     Step 3: Publish Analysis Model   Once you have configured an Analysis Provider, you can publish Models from Analytics Builder to Analytics Manager. On the ThingWorx Composer Analytics tab, click Analytics Builder > Models.   Select vibration_model_s1_only and click Publish.   On the Publish Model? pop-up, click Yes. A new browser tab will open with the Analytics Manager's Analysis Models menu. Close that new browser tab, and instead click Analysis Models in the ThingWorx Composer Analytics navigation. This is the same interface as the auto-opened tab which you closed.   Click the model to select it. At the top, click Enable. Note the pop-up indicating that the Enable was successful.       Step 4: Modify EdgeThing   In previous guides in this Vehicle Predictive Pre-Failure Detection Learning Path, you have created various Entities, including Things such as EdgeThing.   In order to automate the process of pushing data from EdgeThing to Analytics Manager, we need to add a few more Properties to EdgeThing.   These Properties are simple STRING variables, and we'll also set Default Values for them to configure parameters of Analytics Manager.   The first is causalTechnique, which tells Analytics Manager which criteria to use when measuring the impact of a feature on a range of goal values.   The second is goalField, which is simply the data field for which Analytics Manager should try to identify the correlation. In this case, it'll be our primary issue, i.e. low_grease.   It is not mandatory that these suggested Property names match, but they are the names used within ThingWorx Analytics. You could use any Property name you wanted, as you'll be mapping from a particular Property to the functionality within Analytics in a later step.   Return to EdgeThing > Properties and Alerts.   Click + Add.   In the Name field, type causalTechnique. Check Has Default Value. In the text field under "Has Default Value", type FULL_RANGE. Note that you MUST type FULL_RANGE, including capitalization, as that is a command within Analytics Server.   Click the "Check with a +" icon for Done and Add. In the Name field, type goalField. Check Has Default Value. In the text field under "Has Default Value", type low_grease. Note that you MUST type low_grease, including being all lower-case, as that is the exact name of the Analytics Server goal.   Click the "Check" icon for Done. Click Save.   Results Storage   We also need a place in which to store the results that Analytics Manager returns. We'll utilize a few additional Properties for that as well.   On the EdgeThing > Properties and Alerts tab, click + Add. In the Name field, type Result_low_grease. Check the Base Type to BOOLEAN. Check Persistent.   Click the "Check with a +" icon for Done and Add. In the Name field, type Result_low_grease_mo. Change the Base Type to NUMBER. Check Persistent.   Click the "Check" icon for Done. Click Save.       Step 5: Create Event   Events are automatic analysis jobs which are submitted based on a pre-defined condition. In this step, we'll configure an Analysis Event, which will execute automatically whenever there is a data-change in our simulated engine.   On the ThingWorx Composer Analytics tab, click Analytics Manager > Analysis Events.   Click New.... If not already selected, change Source Type (Required) to Thing.   In Source, search for and select EdgeThing. In Event, select DataChange. In Property, select s1_fb1. If there are multiple s1_fb1 Properties, select the first one, as the second one is the s1_fb1 entry in the Info Table Property. In Provider Name, select Vibration_Provider. In Model Name, select the published Model.   Click Save.       Click here to view Part 2 of this guide.  

  Step 6: Create OPC UA Tag in ThingWorx   We will now create a Device in Kepware with a Tag whose value will be shown in ThingWorx.   Right-click on Channel1 that was just created, then click Next to accept the default name Device1       Click Next six times to accept the default settings, Then click the Select import items button.     Expand the remote OPC UA server URL, then expand OpcPlc and Telemetry.     Click SlowUint1 to select it, then click Add items >>. Click OK, Next, Finish. Return to ThingWorx Composer. Under Browse > Modeling > Industrial Connections, open IndConn_Server. Expand Channel1 > Device1 > OpcPlc, then click Telemetry.     Click the check-box next to SlowUint1. Click Bind to New Entity.     Select RemoteThing, then click OK. Enter azure-opcua-plc in the Name field, then click Save.     Click Properties and Alerts, then Click Refresh to see the property value changing every 10 seconds.     Step 7: Next Steps   Congratulations! You've successfully completed the Connect to an Azure OPC UA Server guide, and learned how to:   Create an OPC UA Server in Azure Configure Kepware as on OPC UA Client Connect Kepware to ThingWorx Foundation Monitor OPC UA data in ThingWorx Composer   Learn More   We recommend the following resources to continue your learning experience: Capability Guide Experience Create Your Application UI   Additional Resources   If you have questions, issues, or need additional information, refer to:   Resource Link Community Developer Community Forum Support Getting Started with ThingWorx Documentation Kepware documentation Support Kepware Support site

    Step 3: Monitor SPC Properties   Once the properties have been configured, you are ready to progress on to SPC monitoring.   This is done by viewing the PTC.SPC.Monitoring_MU Mashup.   NOTE: Once a property has been configured for SPC monitoring, 2-5 minutes must pass before the accelerator has collected enough streaming values to perform SPC calculations. During this time, you may see an item that has 2 OOC rules violated; “Collecting Enough Data to Begin Monitoring” and “… to Compute Capability”.     Within Property SPC Status, click an item from the grid that has 0 in the OOC column. See that the Out of Control Rules area is blank, since there are no currently-violated SCP rules for that selected property. Both CP and CPK are above 1.0, showing that the process is capable. All the points in the charts are blue, as they do not violate any OOC rules. Within Property SPC Status, click an item from the grid that has a nonzero number in the OOC column.   See that the Out of Control Rules area has one or more rules listed that are currently being violated. Here, the rule being violated is Range Out of Control. One or more of the charts may have point(s) that are red. Here, you can see one of the points in the R chart is red, as it is above the Upper Control Limit. In the bottom-left, click Refresh Now.   Note that the datetime value of the date picker in the lower-left indicates the period to which the Mashup has updated. It is possible that Properties that were previously not in violation are now violating OOC rules; the opposite is also possible. Note that not all points that violate an OOC rule will display in red. Here, you can see that the rule 8 Consecutive Points Above Center Line is violated, but the corresponding points in the X-Bar Chart are not red. View the SPC status at a time from the recent past. In the lower-left, click date picker. Choose a datetime of 2-3 minutes in the past. You will see that the date picker reflects the datetime value you selected. All the Widgets in the Mashup are updated to reflect their status at that point in time. Enter continuous monitoring mode. Select any of the eight (8) items from the Property SPC Status area. To the left of the Refresh Now button, click the toggle. This will enable the Mashup to auto-refresh every 30 seconds. In the upper-right corner of the Property SPC Status area, click the gray arrow to expand the monitoring display.   Every 30 seconds, the monitoring display will update to show the most recent status for the item you selected.       Step 4: Next Steps   Congratulations! You’ve successfully completed the Deploy Statistical Process Control guide, and learned how to:   Configure multiple properties for SPC monitoring Identify abnormalities in streaming property values   Learn More   We recommend the following resources to continue your learning experience:    Site              Link Wikipedia Western Electric Rules Wikipedia Process Capability   Additional Resources If you have questions, issues, or need additional information, refer to:    Resource       Link Community Developer Community Forum Support ThingWorx Help Center  

    Configure streaming asset properties for SPC monitoring.   Guide Concept   Note: This guide is intended only as a starting point and is not a fully developed or supported solution. This accelerator has been developed using ThingWorx 8.5 and should not be used with any previous versions of the software.   This project introduces you to configuring Properties from your connected streaming assets for Statistical Process Control (SPC) monitoring.   Following the steps in this guide, you will learn how multiple connected assets and their Properties can be displayed in a hierarchy tree.   You will then configure these Properties using predetermined set points, and upper and lower control points for the assets.   Finally, you will learn to navigate the monitoring of the Properties.   We introduce some of the basic building blocks of an SPC accelerator, including important Things and Mashups. You will also use ThingWorx Timers to simulate streaming data.     You'll learn how to   Configure multiple properties for SPC monitoring Identify abnormalities in streaming property values   NOTE: The estimated time to complete this guide is 30 minutes     Step 1: Import SPC Accelerator   Before exploring Statistical Process Control (SCP) within ThingWorx Foundation, you must first import some Entities via the top-right Import / Export button.   Download and unzip PTC_StatisticalCalculations_PJ.zip and PTC_SPC_PJ.zip. These two files each contain a ThingWorx project of a similar name. Import PTC_StatisticalCalculations_PJ.twx first. Import PTC_SPC_PJ.twx once the other import has completed. Explore the imported entities.   Each of the projects contain multiple entities of various types. The most important entities you will use in this guide are as follows:    Entity Name                                   Description Motor_Pump1 Timer to simulate streaming data Motor_Blower1 Timer to simulate streaming data PTC.SPC.ConfigurationHelper Thing that manages the accelerator PTC.SPC.Configuration_MU Mashup for configuring SPC properties PTC.SPC.Monitoring_MU Mashup for monitoring SPC properties   Step 2: Configure Properties for SPC monitoring   You may configure SPC monitoring for multiple production lines, connected assets on those lines, and time-series Properties on those assets using the SPC accelerator.   This is done by viewing the PTC.SPC.Configuration_MU Mashup.   Follow these steps to configure an SPC Property.   Create a new production line   In the Enter New Production Line Name text field, type Line100. Click Add New Line.   Now you will see the new production line added to the Asset Hierarchy tree along the left. All production lines you’ve created (as well as their assets and the assets’ Properties) will be shown here.   In the lower-right, the SPC Property Configuration area has disappeared because the item selected in the Asset Hierarchy tree is the new line; only assets within lines can have streaming Properties.   Add a streaming asset to Line100   Within the Select Asset Thing entity picker, type Motor_Blower1. Click Add Asset for Line.   This asset has Properties that are streaming into ThingWorx. Multiple assets can be added to the same production line by selecting the line from the Asset Hierarchy tree and following the steps above.   If you select the new asset from the Asset Hierarchy tree, you will see that the list of Properties Eligible for SPC Monitor is shown in the SPC Property Configuration area. All Properties have the same default configuration associated with them.   Configure a property for SPC Monitoring   Below is a brief description of each of the configuration parameters:    Parameter           Description Sample Size Number of consecutive property values to average together. For example, a Sample Size of 5 will tell the accelerator to group every 5 property values together and calculate their average value. XBar Points Number of the most recent sample aggregations to display in the SPC Monitoring Mashup. This also affects SPC calculations. Capability Points Number of the most recent sample aggregations to use when populating the Capability Histogram in the SPC Monitoring Mashup. Set Point Value determined to be the set point for that particular property on the asset. Lower Design Spec Value determined to be the lower design spec for that particular property on the asset. This is used for capability calculations. Upper Design Spec Value determined to be the upper design spec for that particular property on the asset. This is used for capability calculations.   Select Pressure1 from the list of eligible properties. Enter the following values:  Properties                      Values Sample Size 5 Xbar Points 30 Capability Points 60 Set Point 73 Lower Design Spec 68 Uppder Design Spec 78   3, Select Xbar-R Chart. 4. Click Add or Update SPC Monitoring. 5. Pressure1 is added to the Asset Hierarchy tree underneath the Motor_Blower1 asset. 6. If you select this Property, you can modify the configuration and save it by clicking Add or Update SPC Monitoring.   Configure assets and Properties   Follow these steps using the following parameters:                                     Line Asset  Property  Sample Size Xbar Point Capability Points Set Points Lower Design Spec Upper Design Spec Chart Type 100 Motor_Blower1 Pressure1 5 30 60 73 68 78 Xbar-R 100 Motor_Blower1 Pressure2 5 30 60 78 68 89 Xbar-R 100 Motor_Blower1 Temperature1 5 30 60 50 44 56 Xbar-s 100 Motor_Blower1 Temperature2 5 30 60 85 73 97 Xbar-s 100 Motor_Pump1 Vibration10 5 30 60 150 108 190 Xbar-s 100 Motor_Pump1 Vibration11 8 60 100 200 168 220 Xbar-s 100 Motor_Pump1 Pressure100 5 30 60 100 84 118 Xbar-R 100 Motor_Pump1 Pressure200 5 30 60 90 84 97 Xbar-R   As you add assets to Line100 and configure their Properties, you will see the Asset Hierarchy tree grow. If you need to remove an asset or its associated Properties from the Asset Hierarchy tree, you may select that item, and click Remove Selected. For any item you remove, its child-items will also be removed.   Click here to view Part 2 of this guide.

  A series of training videos for ThingWorx Analytics   Guide Concept   This guide provides a series of training videos covering ThingWorx Analytics Server and Platform Analytics.   It is recommended that they be viewed in order.    Additionally, a downloadable .zip with additional training materials is provided.     You'll learn how to   Use ThingWorx Analytics Understand the Analytics Thought Process Basic Analytics concepts (data types, variables, modeling) Descriptive Analytics Predictive Analytics modeling techniques Familiarity with other topics: Prescriptive Analytics, Clustering, Time Series, Anomaly Detection Acquire basic knowledge of how to create an end-to-end Smart Application Deepen your Foundation and Analytics understanding NOTE: This guide's content aligns with ThingWorx 9.3. The estimated time to complete all parts of this guide is 12 hours       ThingWorx Analytics Overview   This guide will present a series of training videos for ThingWorx Analytics.It is recommended that you view each guide in-order, as future videos may build on the concepts learned in earlier ones.    Download CourseFiles.zip included in this guide, as it contains important materials for particular videos.   In this course, you will learn the basics of the ThingWorx Analytics Machine Learning process. You will understand how to perform Descriptive Analytics such as identifying Signals, Profiles, or building Clusters.   You will also understand how to train a Model and use Predictive and Prescriptive Scoring. Additional topics include Time Series, Anomaly Detection, and near-real-time Scoring. You will also learn about how to create a simple smart application using Analytics together with other pieces of the overall ThingWorx platform.     ThingWorx Analytics Video Guide   Module 1: ThingWorx Analytics Overview Module 2: Use Case Discussion Module 3: Data Profiling Module 4: Data Transformation and Feature Engineering Module 5: Descriptive Analytics Module 6: Predictive Models and Model Validation Module 7: Scoring Predictive Realtime Prescriptive Module 8: Time Series Modeling Module 9: Anomaly Detection Module 10: ThingWorx Foundation and Analytics Integration Module 11: Mini Project     Next Steps   Congratulations! You've successfully completed the Analytics Training Videos guide, and learned how to:   Use ThingWorx Analytics Understand the Analytics Thought Process Basic Analytics concepts (data types, variables, modeling) Descriptive Analytics Predictive Analytics modeling techniques Familiarity with other topics: Prescriptive Analytics, Clustering, Time Series, Anomaly Detection Acquire basic knowledge of how to create an end-to-end Smart Application Deepen your Foundation and Analytics understanding   Additional Resources If you have questions, issues, or need additional information, refer to:   Resource Link Community Developer Community Forum Analytics Support Help Center          

  Step 7: Real World Model   We’ll now rerun model creation with the Real World data.   Even though Signals and Profiles are possibly telling us that only Sensor 1 is needed, the first Model you’ll create will contain all the data, while the second Model will exclude Sensor 2. We’ll then compare the Models to see which one is going to work the best for predicting engine failures.   On the left, click Analytics Builder > Models. Click New….   In the Model Name field, enter vibration_model. In the Dataset field, select vibration_dataset.   Click Submit. After ~60 seconds, the Model Status will change to COMPLETED. Select the model that was created in the previous step, i.e. vibration_model. Click View… to open the Model Information page. Note that your model may differ slightly from the picture below, as the automatically-withheld "test" data is randomly chosen.       Unlike our simulated dataset, this real-world data is not perfect. However, it’s still pretty good, and is much more representative of what a real-world scenario would indicate.   The True Positive Rate shown on the Receiver Operating Characteristic (ROC) chart are much higher than the False Positives.   The curve is relatively high and to the left, which indicates a high accuracy level.   You may also click on the Confusion Matrix tab in the top-left, which will show you the number of True Positive and True Negatives in comparison to False Positives and False Negatives.     NOTE: The number of correct predictions is much higher than the number of incorrect predictions.   As such, we now know that our Sensors have a relatively good chance at predicting an impending failure by detecting low grease conditions before they cause catastrophic engine failure.   Refined Model   We can now compare this first Model that includes both Sensors to a Model using only Sensor 1, since we suspect that Sensor 2 may not be necessary to achieve our goal. On the left, click Analytics Builder > Models. Click New…. In the Model Name field, enter vibration_model_s1_only. In the Dataset field, select vibration_dataset.   On the right beside Excluded Fields from Model, click the Excluded Fields button.   Select s2_fb1 through s2_fb5.   While all the s2 values are selected, click the green "right-arrow", i.e. > button, in the middle.   At the bottom-left, click Save.   Click Submit. After ~60 seconds, the Model State will change to COMPLETED. With vibration_model_s1_only selected, click View….     The ROC chart is comparable to the original model (including Sensor 2).   Likewise, the Confusion Matrix (on the other tab) indicates a good ratio of correct predictions versus incorrect predictions.     NOTE: These Models may vary slightly from your own final scores, as what data is used for the prediction versus for evaluation is random.   ThingWorx Analytics’s Models have indicated that you are likely to receive roughly the same accuracy of predicting a low-grease condition whether you use one sensor or two!   If we can get an accurate early-warning of the low grease condition with just one sensor, it then becomes a business decision as to whether the extra cost of Sensor 2 is necessary.     Step 8: Next Steps   Congratulations! You've successfully completed the Build an Engine Analytical Model guide, and learned how to:   Load an IoT dataset Generate machine learning predictions Evaluate the analytics output to gain insight   The next guide in the Vehicle Predictive Pre-Failure Detection with ThingWorx Platform learning path is Manage an Engine Analytical Model.   Learn More   We recommend the following resources to continue your learning experience:   Capability Guide Analyze Operationalize an Analytics Model Build Implement Services, Events, and Subscriptions Additional Resources   If you have questions, issues, or need additional information, refer to:   Resource Link Community Developer Community Forum Support Analytics Builder Help Center

  Step 4: Simulated Model   Models are primarily used by Analytics Manager (which will be discussed in the next guide), but they can still be used to estimate the accuracy of predictions.   When Models are calculated, they inherently withhold a certain amount of data (~20%). The prediction model is then run against the withheld data. This provides a form of "accuracy measure".   The withheld-data is selected randomly, so you'll actually get a slightly different model and accuracy measure each time that you create a Model versus the same dataset.   On the left, click Analytics Builder > Models.   Click New….   In the Model Name field, enter simulated_model. In the Dataset field, select simulated_dataset.   Click Submit. After ~60 seconds, the Model Status will change to COMPLETED.     Select the model that was created in the previous step, i.e. simulated_model. Click View… to open the Model Information page.   As with Signals and Profiles, our Model is once again "too good". In fact, it's perfect.   The expected "Precision" is 1.0, i.e. 100%. The True vs False Positive rate shown in the graph goes straight up to the top immediately.   While you want a graph that is "high and left", you're very unlikely to ever see real-world scenarios such as shown here.   Still, you've been able to progress the process of using Foundation (and now Analytics) to build an analytical model of MotorCo's prototype engine.   What needs to happen now is to receive real world data from your R&D engineers.     Step 5: Upload Real World Data   In your process of using the EMS Engine Simulator, the idea has always been to get a headstart on the engine developers.   At some point, they would wire sensors into the EMS and start providing real world data.   In our scenario, that has now happened. Real world data is being fed from the EMS to Foundation, Foundation is collecting that data in an Info Table Property, and you've even exported the data as a .csv. file.   This new dataset is over periods of both good and bad grease conditions. The engineers monitoring the process can flip a sensor switch connected to the EMS to log the current grease situation as either good or bad at the same time that the vibration sensors are taking readings.   We will now load this real world dataset into Analytics in the same manner that we did earlier with the simulated dataset.   Download the attached analytics_vibration.zip file to your computer. Unzip the analytics_vibration.zip file to access the vibration_data_and_header.csv and vibration_metadata.json files. On the left, click Analytics Builder > Data. Under Datasets, click New....   In the Dataset Name field, enter vibration_dataset. In the File Containing Dataset Data section, search for and select vibration_data_and_header.csv. In the File Containing Dataset Field Configuration section, search for and select vibration_metadata.json.   Click Submit.     Step 6: Real World Signals and Profiles   Now that the real-world vibration data has been uploaded, we’ll re-run Signals and Profiles just as we did before.   Hopefully, we’ll start seeing some patterns.   On the left, click Analytics Builder > Signals. At the top, click New….   In the Signal Name field, enter vibration_signal. In the Dataset field, select vibration_dataset.   Click Submit. Wait ~30 seconds for Signal State to change to COMPLETED     The results show that the five Frequency Bands for Sensor 1 are the most highly correlated with determining our goal of detecting a low grease condition.   For Sensor 2, only bands one and four seem to be related, while bands two, three, and five are hardly relevant at all.   This is a very different result than our earlier simulated data. Instead, it looks like it’s possible that the vibration-frequencies getting pickup up by our first sensor are explicitly more important.   Profiles   We’ll now re-run Profiles with our real-world dataset. On the left, click Analytics Builder > Profiles. Click New….   In the Profile Name field, enter vibration_profile. In the Dataset field, select vibration_dataset.   Click Submit. After ~30 seconds, the Signal State will change to COMPLETED.     The results show several Profiles (combinations of data) that appear to be statistically significant.   Only the first few Profiles, however, have a significant percentage of the total number of records. The later Profiles can largely be ignored.   Of those first Profiles, both Frequency Bands from Sensor 1 and Sensor 2 appear.   But in combination with the result from Signals (where Sensor 1 was always more important), this could possibly indicate that Sensor 1 is still the most important overall.   In other words, since Sensor 1 is statistically significant both by itself and in combination (but Sensor 2 is only significant in combination  with Sensor 1), then Sensor 2 may not be necessary.     Click here to view Part 3 of this guide.

    Generate engine-failure predictions and gain insight into your data with machine learning.   GUIDE CONCEPT   This guide will upload captured data from an Edge MicroServer (EMS) "Engine Simulator" to ThingWorx Analytics Builder.   Following the steps in this guide, you will create an analytical model, and then refine it based on further information from the Analytics platform.   We will teach you how to determine whether or not a model is accurate and how you can optimize both your data inputs and the model itself.   NOTE: This guide's content aligns with ThingWorx 9.3. The estimated time to complete ALL parts of this guide is 60 minutes     YOU'LL LEARN HOW TO   Load an IoT dataset Generate machine learning predictions Evaluate the analytics output to gain insight     Step 1: Scenario   In this guide, we’re continuing the same MotorCo scenario, where an engine can fail catastrophically in a low-grease condition.   In previous guides, you’ve gathered and exported engine vibration-data from an Edge MicroServer (EMS).   The goal of this guide is to now import that previously-exported Comma-Separated Values (.csv) data into ThingWorx Analytics, and then create an analytical model for predictive maintenance.   Analytical model creation can be extremely helpful for the automotive segment in particular. For instance, each car that comes off the factory line could have an EMS constantly sending data from which an analytical model could automatically detect engine trouble.   This could enable your company to offer an engine monitoring subscription service to your customers.   This guide will show you how to build an analytic model of your engine to facilitate this monitoring service.     Step 2: Upload Simulated Data   This guide assumes that you are using either the hosted trial (with has both Foundation and Analytics pre-installed) or a combination of the Foundation and Analytics downloadable installers.   To confirm that Foundation is communicating with Analytics, perform the following steps:   On the ThingWorx Foundation left-side navigation column, click Analytics > Analytics Builder > Settings.   At the top-right in the Analytics Server Version field, ensure that you see an appropriate version number.     NOTE:  If you use your own dataset, it's possible that you're results in the following steps will differ from those created by the provided-dataset. If you were unable to generate a 30,000+ entry dataset in the last guide, then you may download testCSVfile.csv attached here,instead. You will also need to download and extract vibration_metadata.zip which describes each column of the dataset. On the left, click Analytics Builder > Data.   Under Datasets, click New....   In the Dataset Name field, enter simulated_dataset. In the File Containing Dataset Data section, search for and select testCSVfile.csv. In the File Containing Dataset Field Configuration section, search for and select vibration_metadata.json.   Click Submit. Note that the time it takes to import the dataset is determined by its size.       Step 3: Simulated Signals and Profiles    The Signals section of ThingWorx Analytics looks for the most statistically correlated single field in the dataset which relates to your selected goal.   This doesn't necessarily indicate that it is the cause of your goal, whether maximizing or minimizing. It just means that the dataset indicates that this single field happens to correlate with the goal that you desire.   On the left, click Analytics Builder > Signals.   At the top, click New….   In the Signal Name field, enter simulated_signal. In the Dataset field, select simulated_dataset.   Click Submit. Wait ~30 seconds for Signal State to change to COMPLETED     Unfortunately, our results aren't very good. Or, more accurately, they're too good.   Our simulated dataset has some noise in it from adding random values to our five frequency bands on each our two sensors. However, ThingWorx Analytics has instantly seen through that noise and discarded it. Instead, it's only detected that s2_fb5 isn't relevant.   If you look back at the Use the EMS to Create an Engine Simulator guide, you'll see that s2_fb5 has the same base value between both a "good grease" and a "bad grease" condition, i.e. a base of 190.   This does show already that Analytics is working, though. Since s2_fb5 didn't change between good and bad grease conditions, our Signal analysis is indicating that it's not relevant to our model.   Profiles   Now, let's do the same for a Profile.   The Profiles section of ThingWorx Analytics looks for combinations of data which are highly correlated with your desired goal.   On the left, click Analytics Builder > Profiles.   Click New....   In the Profile Name field, enter simulated_profile. In the Dataset field, select simulated_dataset.   Click Submit. Wait ~30 seconds for the Profile State to change to COMPLETED.     Just like with Signals, our Profile is too good. In fact, Analytics is indicating that just s1_fb2 by itself is the primary indicator of good vs. bad grease conditions.   This is likely due to random chance. The random noise added to s1_fb2 just happened to be slightly less than the other frequency bands, so everything else was discarded.   Regardless, ThingWorx Analytics is quickly seeing through our simulated data.   Next, we'll actually create a Model using the simulated dataset.     Click here to view Part 2 of this guide  

  Build a Predictive Analytics Model - Video Guide   This project will introduce ThingWorx Analytics Builder via a convenient video-guide. Following the steps in this video, you will create an analytical model, and then refine it based on further information from the Analytics platform. We will teach you how to determine whether or not a model is accurate and how you can optimize both your data inputs and the model itself.     ThingWorx Analytics Server Installation with SSL   This video demonstrates the installation of Analytics Server with SSL protections enabled. It includes information about generating the necessary certificates and truststores to enable SSL for each component that connects with the server.     ThingWorx Platform Analytics Installation with SSL   This video demonstrates the Platform Analytics installation with SSL protections enabled. It provides information about generating the necessary certificates and truststores to enable SSL for all connected components, including RabbitMQ and Flink.     Toolbar Widget | ThingWorx 9   Watch this video to learn how to add a Toolbar widget to a mashup in ThingWorx. You'll learn how to define actions using a data service and create bindings to control and configure a Grid widget.     ThingWorx SSO: Login demonstration from Azure AD to ThingWorx   This video demonstrates the ThingWorx SSO login procedure using Azure AD. The login procedure is shown from a user point of view. Then a behind the scenes view looks at the design in ThingWorx Composer.     Menu Bar Widget ThingWorx 9   Watch this video to learn how to create a mashup layout that uses a Menu Bar widget for navigation. You'll learn how to create a layout, define menu items, and configure the widget.     ThingWorx AD FS SSO Setup   This video provides a walk-through of the steps required to set up SSO for ThingWorx in an environment where AD FS is both the CAS and the IdP. The focus is on the AD FS setup steps.     Create Your Application UI   Following the steps in this video-guide, you will learn how to use the ThingWorx Mashup Builder tool to create a Graphical User Interface (GUI) for your IoT Application. We will teach you how to rapidly create and update a Mashup, which is a custom visualization built to display data from devices according to your application's business and technical requirement     Get Started with ThingWorx for IoT   Explore the ThingWorx Foundation Internet-of-Things application building platform in a convenient, instructional video guide format.     Thingworx Mashup 101 - Do's and Don'ts   This session covers the most common and useful tips about how to correctly use Mashup builder, Widgets and Layouts – and what to avoid - to create applications with good principles of UI/UX and easier to maintain.     What's New in ThingWorx 9.1   The industry’s most complete IIoT platform just got better. Loaded with a full range of new and updated features and functionality, you can expect powerful platform capability enhancements across the board.       Standardize Connectivity to Devices, Applications, & Systems for Centralized IIot Data   You can’t implement IIoT solutions if you can’t connect to your assets, but in complex environments connectivity can seem like an insurmountable challenge. ThingWorx makes it easy to establish standardized connectivity, so you can create a secure, single-source for accessing industrial data across your IT and OT systems     Bar Chart Widget | ThingWorx 9   Watch the following video on how to add the Bar Chart widget to a mashup and configure basic widget properties.     Button Widget | ThingWorx 9   Watch the this video to learn how to add the Button widget to a mashup and use its Clicked event to trigger data services.     Line Chart Widget | ThingWorx 9   Watch the this video to learn how to add the Line Chart widget to a mashup and bind a data source.     ReImagine Your Application UI With Collection and Custom CSS   Create compelling, modern application user interfaces in ThingWorx with the latest enhancements to our Mashup visualization platform - Collection and Custom CSS. In this webinar with IoT application designer Gabriel Bucur, we'll show how the new Collection widget makes it easy to replicate visual content in your UI for menu systems, dashboards, tables, and more.     Testing Your Edge Application   Native testing is an often-overlooked aspect of edge application development. This session shares how to perform thorough testing before you push your application into live production.     Predictive Maintenance with Thingworx 101   Adopting a predictive maintenance strategy for the first time can often feel daunting, but it doesn't have to. Learn how ThingWorx can be used to leverage edge devices and turn monitoring activity into action.     ThingWorx Mashup Pitfalls: What to Avoid and What Not to Do   Designing Mashups takes time. Moreover, expanding upon a Mashup that was built for a small-scale application can get cumbersome.     IoT Security: Keeping Devices Safe in a High Risk World   Understanding access controls can be difficult. With the built-in security features and concepts in ThingWorx, you can easily give your devices access on-demand.     Edge Connectivity in Unreliable Networks   The Store and Forward feature within ThingWorx Industrial Connectivity assures users that critical data collected at the edge won't get lost during an outage.  

Video Author:                     Asia Garrouj Original Post Date:            March 31, 2017 Applicable Releases:        ThingWorx Analytics 7.4 to 8.1   Description: This video is the second part of a two part video series walking thru the configuration of Analysis Event which is applied for Real-Time Scoring.  This second video will walk you thru the configuration of Analysis Event for Real Time Scoring and validating that a predictions job has been executed based on new input data.    

Video Author:                     Christophe Morfin Original Post Date:            October 6, 2017 Applicable Releases:        ThingWorx Analytics 8.1   Description: This video covers the new features of ThingWorx Analytics Builder 8.1      

Video Author:                     Christophe Morfin Original Post Date:            October 2, 2017 Applicable Releases:        ThingWorx Analytics 8.1   Description:​ In this video we will walk thru a few steps to ensure the installation process was successful.    

Video Author:                     Christophe Morfin Original Post Date:            October 2, 2017 Applicable Releases:        ThingWorx Analytics 8.1   Description:​ In this video we will walk thru the installation steps of ThingWorx Analytics Server 8.1.  This covers the Native Linux installation though the steps will be similar for a docker installation on Windows or Linux.    

Video Author:                     Christophe Morfin Original Post Date:            September 26, 2017 Applicable Releases:        ThingWorx Analytics 8.0 & 8.1   Description:​ This video shows the commands to execute to deploy the training and results microservices as docker container.  This is based on Docker Toolbox to highlight the specific settings required on Toolbox.