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This video concludes Module 8: Time Series Modeling of the ThingWorx Analytics Training videos. 

This video continues Module 8: Time Series Modeling of the ThingWorx Analytics Training videos. It continues to show how ThingWorx Analytics automatically transforms time series datasets into ones that are ready for machine learning. It also describes the concept of virtual sensors. It finishes by describing the time series dataset that will be used in the following modules.

This video begins Module 8: Time Series Modeling of the ThingWorx Analytics Training videos. It describes the differences between time series and cross-sectional datasets. It begins to show how ThingWorx Analytics automatically transforms time series datasets into ones that are ready for machine learning. 

This video concludes Module 7: Predictive & Prescriptive Scoring of the ThingWorx Analytics Training videos. It describes how ThingWorx Analytics automatically evaluates a range of values for chosen fields to produce prescriptive scores. 

This video begins Module 7: Predictive & Prescriptive Scoring of the ThingWorx Analytics Training videos. It describes how a trained machine learning model takes inputs and makes predictions of different kinds, depending on the use case. It shows how scoring works in production, taking inputs from various sources and producing a score to help users make informed decisions. It also covers the concept of field importance in an individual score.

This video concludes Module 6: Predictive Models & Model Validation of the ThingWorx Analytics Training videos. 

This video continues Module 6: Predictive Models & Model Validation of the ThingWorx Analytics Training videos. It covers some modeling techniques to help build better predictive models. It discusses the dangers of models that overfit data, and how to avoid overfitting. 

This video continues Module 6: Predictive Models & Model Validation of the ThingWorx Analytics Training videos. It then begins to describe some of the performance metrics used to evaluate predictive models. 

This video continues Module 6: Predictive Models & Model Validation of the ThingWorx Analytics Training videos. It describes the remaining machine learning algorithms used by ThingWorx Analytics to build predictive models that weren't covered in Part 1. In addition, this video describes the different kinds of ensembles you can build that utilize multiple algorithms. 

This video begins Module 6: Predictive Models & Model Validation of the ThingWorx Analytics Training videos. It gives examples of different types of goal variables. It also discusses data considerations in predictive modeling, It begins describing the machine learning algorithms used by ThingWorx Analytics to build predictive models. 

This video concludes Module 5: Descriptive Analytics of the ThingWorx Analytics Training videos. It covers signals, profiles, and clusters, and how these forms of descriptive analytics provide crucial insight into your data.

This video begins Module 5: Descriptive Analytics of the ThingWorx Analytics Training videos. It covers signals, profiles, and clusters, and how these forms of descriptive analytics provide crucial insight into your data.

This video concludes Module 4: Data Transformation & Feature Engineering of the ThingWorx Analytics Training videos. It covers Descriptive Services and Derived Properties, and how they can be leveraged to create helpful alerts and make data transformation easier. 

This video begins Module 4: Data Transformation & Feature Engineering of the ThingWorx Analytics Training videos. It describes what data transformation is, and how feature engineering can improve machine learning models. You will learn about independent and dependent variables in your data, and how an "analytics ready view" looks for use with ThingWorx Analytics.

This video concludes Module 3: Data Profiling of the ThingWorx Analytics Training videos. It shows you a few examples of questions that should be asked of a subject-matter expert (SME) to better understand the information contained in a dataset. Using answers to these questions, you will  use a tool such as Microsoft Excel to modify a given dataset, and prepare it for future exercises in this course.

This video is Module 2: Use Case Discussion of the ThingWorx Analytics Training videos. It covers what a use case is, and what a successful use case requires. It details a few examples that have been explored using ThingWorx Analytics. 

This video continues Module 1: ThingWorx Analytics Overview of the ThingWorx Analytics Training videos. It covers some of the functionality of the ThingWorx platform, as well as ThingWorx Analytics capabilities.

    Step 13: C - Support Other Platforms   If you are using a platform that is different than the options provided in the CMake configurations or in the C SDK configurations, you can add your own computer type.   CMake has its own custom toolchain support that enables you to add your computer to the CMake configurations.   Go to the CMake Toolchain. NOTE: The C SDK provides a CMakeList.txt file that contains the setup instructions for the C SDK. Make changes only if you have verified the configurations and compiler information. Create a CMakeList.txt file based on your OS inside the folder in which you would like to setup your application. Modify the Platform Configuration section of the CMakeList.txt file to add your personal computer architecture if it is not already listed. Ensure your configurations are in your application's CMakeList.txt file.   An example of the Platform Configuration section is shown below. if (PLATFORM) if (${PLATFORM} MATCHES "linux-arm") set(OS "linux") set(ARCHITECTURE "arm") set(CMAKE_C_COMPILER ${CMAKE_CURRENT_SOURCE_DIR}/../TOOLS/gcc-linux-arm-cross/bin/arm-angstrom-linux-gnueabi-gcc) set(CMAKE_FIND_ROOT_PATH ${CMAKE_CURRENT_SOURCE_DIR}/../TOOLS/gcc-linux-arm-cross ${CMAKE_CURRENT_SOURCE_DIR}/../TOOLS/gcc-linux-arm-cross/arm-angstrom-linux-gnueabi) # Set LD_LIBRARY_PATH set(Env{LD_LIBRARY_PATH} "${CMAKE_FIND_ROOT_PATH}/lib/gcc") else () if (${CMAKE_SYSTEM_NAME} MATCHES "Linux") set(OS "linux") if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "x86_64" OR ${CMAKE_SYSTEM_PROCESSOR} MATCHES "AMD64") set(ARCHITECTURE "x86_64") set(PLATFORM "linux-x86-64") elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "x86_32" OR ${CMAKE_SYSTEM_PROCESSOR} MATCHES "i[36]86") set(ARCHITECTURE "x86_32") set(PLATFORM "linux-x86-32") elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv5tejl") set(ARCHITECTURE "arm") set(PLATFORM "linux-arm") elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv[67]l") set(ARCHITECTURE "arm-hwfpu") set(PLATFORM "linux-arm-hwfpu") else () # default to linux-x86_64 set(ARCHITECTURE "x86_64") set(PLATFORM "linux-x86-64") endif() endif ()   An example of how to work with your OS in your application's CMakeList.txt file is shown below: add_executable (SteamSensor src/main.c src/SteamThing.c) target_link_libraries (SteamSensor LINK_PUBLIC twCSdk) if (${OS} MATCHES "windows") add_custom_command(TARGET SteamSensor POST_BUILD COMMAND cmake -E copy_if_different "${CMAKE_BINARY_DIR}/$<CONFIGURATION>/twCSdk.dll" "${CMAKE_CURRENT_BINARY_DIR}/$<CONFIGURATION>/twCSdk.dll" COMMENT "Copying C SDK dll to example directory.") if (${TLS_LIB} MATCHES "openssl") # Copy over openssl dll files add_custom_command(TARGET SteamSensor POST_BUILD COMMAND cmake -E copy_if_different "${OPENSSL_SSLEAY_BIN_PATH}" "${CMAKE_CURRENT_BINARY_DIR}/$<CONFIGURATION>/ssleay32.dll" COMMENT "Copying ssleay dll to example directory.") add_custom_command(TARGET SteamSensor POST_BUILD COMMAND cmake -E copy_if_different "${OPENSSL_LIBEAY_BIN_PATH}" "${CMAKE_CURRENT_BINARY_DIR}/$<CONFIGURATION>/libeay32.dll" COMMENT "Copying ssleay dll to example directory.") endif () endif () if (${OS} MATCHES "linux") if (${TLS_LIB} MATCHES "openssl") # Copy over openssl libraries. add_custom_command(TARGET SteamSensor POST_BUILD COMMAND cmake -E copy_if_different "${OPENSSL_LIB_DIR}/libcrypto.so.1.0.0" "${CMAKE_CURRENT_BINARY_DIR}" COMMAND cmake -E copy_if_different "${OPENSSL_LIB_DIR}/libssl.so.1.0.0" "${CMAKE_CURRENT_BINARY_DIR}" COMMENT "Copying openssl to test directory.") endif () endif () if (${OS} MATCHES "macos") if (${TLS_LIB} MATCHES "openssl") # Copy over openssl libraries. add_custom_command(TARGET SteamSensor POST_BUILD COMMAND cmake -E copy_if_different "${OPENSSL_LIB_DIR}/libcrypto.1.0.0.dylib" "${CMAKE_CURRENT_BINARY_DIR}" COMMAND cmake -E copy_if_different "${OPENSSL_LIB_DIR}/libssl.1.0.0.dylib" "${CMAKE_CURRENT_BINARY_DIR}" COMMENT "Copying openssl to test directory.") endif () endif()     Step 14: Next Steps   Congratulations! You've successfully completed the SDK Reference Guide.   This guide is designed to be used as a reference when developing your application with one of the ThingWorx SDKs.   Learn More   We recommend the following resources to continue your learning experience:   Capability Guide Build Design Your Data Model   Additional Resources   If you have questions, issues, or need additional information, refer to: Resource Link   Community Developer Community Forum   Support C SDK Help Center Java SDK Help Center

    Step 7: Add Grid   It might also be helpful to display the data you've used to build the ThingWorx Analytics model.   In the future, this might be split out into an entirely separate page/Mashup that is exclusively devoted to backend model creation, but that would be beyond the scope of this guide.   For now, we'll simply display that collected data via the Grid Widget      1. On the EEFV_Mashup, drag-and-drop a Grid Advanced Widget onto the bottom-left Canvas section..     2. On the top-right Data tab, click the green </> button beside Things_EdgeThing. Note that this will open the Add Data pop-up, but with EdgeThing pre-selected.   3. In the Services Filter field, type getproperties.   4. Click the right-arrow beside GetProperties to add it to Selected Services on the right.   5. Check Execute on Load.     6. Click Done.   7. Under the Data tab, expand GetProperties to reveal the options.     8. Drag-and-drop Things_EdgeThing > GetProperties > infoTableProperty onto the Grid Advanced Widget.     9. On the Select Binding Target pop-up, click Data.     10. Click Save.   11. Click View Mashup.         Step 8: Add Controls   Throughout this Learning Path, it has been recommended that you stop Analysis Events when not actively using their functionality.   However, this requires going into the backend of ThingWorx Analytics to disable the event, which is not ideal.   Instead, let's enable or disable the Analysis Event from inside the Mashup by adding some Button Widgets to directly interface the Analytics backend for us.       1. Click the bottom-right Canvas section to select it.         2. In Mashup Builder top-left, click the Layout tab.         3. Under Positioning, click the Static radio-button.         4. Click the Widgets tab.       5. Drag-and-drop a Button Widget onto the bottom-right section.         6. Drag-and-drop another Button Widget onto the bottom-right section.       7. Drag-and-drop a Text Field Widget onto the bottom-right section.       8. Click Save.       Bring in More Data   Now that we have Buttons to trigger enable/disable, as well as a Text Field to display information, we now need to bring in some additional Mashup Data Services to interact with the ThingWorx Analytics backend.       1. Click the green + button at the top of the Data tab.       2. In the Entity Filter field, search for and  select TW.AnalysisServices.EventManagementServicesAPI.       3. In the Services Filter field, search for and add QueryAnalysisEvents by clicking the right arrow.       4. Check Execute on Load.         5. In Services Filter, search for and select EnableAnalysisEvent by clicking the right arrow. Note that you should NOT check "Execute on Load", as we'll trigger this Service only when the Button is clicked.     6. In Services Filter, search for and select DisableAnalysisEvent by clicking the right arrow. Likewise, do NOT check "Execute on Load" here either.       7. Click Done.         8. Click Save.     Display Event Key   To enable or disable Analytics Events, we need to know the eventId, which is returned by QueryAnalysisEvent Service as the parameter labeled key.   We'll bind that to the Text Field Widget for later usage in enabling/disabling.        1. Change the top-button's Label Property to Enable Analytics Event.       2. Change the bottom-button's Label Property to Disable Analytics Event.         3. Under the Data tab, expand QueryAnalysisEvents > Returned Data > All Data to reveal the options. .       4. Drag-and-drop QueryAnalysisEvents > Returned Data > All Data > key to the TextField Widget.         5. On the Select Binding Target pop-up, click Text.         6. Click Save.     Enable/Disable Analytics Events   Now that we know the key/eventId, we can call the EnableAnalysisEvent and DisableAnalysisEvent services.       1. Under the Data tab, expand EnableAnalysisEvent > Parameters to reveal eventId.       2. Click the Text Field Widget to select it, and then click the top-left drop down to reveal the options.         3. Drag-and-drop the Text Field's Text Property onto EnableAnalysisEvent > Parameters > eventId.         4. Repeat steps 1-3 for DisableAnalysisEvent.         5. Click the Enable Analytics Event Button Widget to select it, then click the top-left to reveal the drop down option .       6. Drag-and-drop the Clicked Event onto the EnableAnalysisEvent Service under the Data tab.         7. Repeat steps 5-6 for DisableAnalysisEvent, using the other Button Widget.         8. Click Save     Step 9: View Mashup   Throughout this guide, we've added various additional functionality to our MVP Mashup. At this point, you could continue to update the Mashup as you see fit.   For instance, you could change the background color of the top-left section to better match the header. Or you could further modify the original Mashup shown in the Contained Mashup Widget so that it better fits in the allowed space. You could add another Label Widget to the Header section to also display the company's motto / tag-line.   Regardless, when you are done with modifications, Save and click View Mashup.     Note that you can left-click-and-drag on the Time Series Chart to select particular time ranges. Or you could add a Time Selector Widget to the bottom-right section to control it there.   Similarly, you could add controls for the Grid Widget to only show the Identifier ranges in which you were interested.   Or you could split out the Model-creation values to a completely separate Mashup as previously discussed.   The extent to which you develop your Mashup is entirely up to you.        Step 10: Next Steps   Congratulations. You've completed the Enhanced Engine Failure Visualization guide. In this guide, you learned how to:   Create a Mashup with a Header Divide your Mashup into Sub-sections Use a Contained Mashup to reuse development Store historical data in a Value Steam Display historical data in a Line Chart Show spreadsheet data via a Grid Advanced Widget Tie Mashup controls into the ThingWorx backend   This is the last guide in the Vehicle Predictive Pre-Failure Detection with ThingWorx Platform learning path.   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 5: Contained Mashup   Our Minimum Viable Product (MVP) Mashup which we created in the last guide did have valid information.   Being able to display the inputs coming from the engine, as well as the analytical results coming from ThingWorx Analytics, are certainly items we don’t want to lose in this new, more complete Mashup.   Rather than recreating that work from scratch, we’ll simply include that previous Mashup in one of our sub-section via the Contained Mashup Widget.       1. Click on the top-left section to select it, and ensure that you’re on the Widgets tab in the top-left.       2. Drag-and-drop a Contained Mashup Widget onto the top-left section.       3. With the Contained Mashup Widget selected, return to the Properties tab in the bottom-left.       4. Scroll down and locate the Name Property.       5. Search for and select EFPG_Mashup       6. Click Save.     Add Column Labels   The original Mashup we created (and have now embedded in the new one) had some labels for the inputs and outputs. However, you had to know what things like “s1_fb1” meant to understand that that was an input.   We can go back to the original EPFG_Mashup, make some modifications for greater clarity, and those changes will also carry over to our new Mashup.       1. Reopen the old EPFG_Mashup on the Design tab.       2. Move all of the Widgets down to leave some extra room at the top.       3. Drag-and-drop two Divider Widgets onto the Canvas above both the Inputs and Results columns.       4. Select a Divider Widget, and go to its Style Properties.       5. Expand Base > Line to reveal the background Style Property.       6. Click on the default gray color to see the available options.       7. Choose the built-in black at the bottom, and click Select.       8. Make the same modification to the other Divider Widget.       9. Drag-and-drop two more Label Widgets onto the Canvas above the two columns.       10. Change their LabelText Properties to Inputs and Results, respectively.     Change Background and Size       1. From the Explorer tab in the top-left, select the container.       2. Select the Style Properties tab in the bottom-left and expand Base > Container.       3. Change the background Style Property to a color you prefer.       4. With the container still selected in the Explorer tab, drag the corners of the Mashup to reduce its size.       5. You could even move the Results column over, place the Auto Refresh Widget underneath, and then reduce the container size even further.       6. Click Save.     View Mashup Thus Far   With the changes to the previous EFPG_Mashup now complete, let’s ensure that everything carried over to our new Mashup.       1. Return to EEFV_Mashup.       2. Click Save.       3. Click View Mashup.   Note how the various changes we made to the base Mashup are also being shown, via a Contained Mashup Widget, in our new Mashup.   Splitting out functionality to a separate Mashup that is then embedded where needed is a great way to re-use content and simplify development.       Step 6: Add Chart   Our original Mashup (which has now been embedded in our new one) shows the instantaneous analytical results based on the inputs coming from the Edge MicroServer (EMS).   However, when investigating remote customer issues, it might be helpful to see some historical trends. A temporary "blip" of a low-grease indication might be worrisome, but it may not require immediate intervention unless the issue was occuring consistently or for extended periods of time.   Fortunately, creating a historical record is relatively simple in ThingWorx Foundation.   All that is really needed is a place in which to store the past records.   One of the easiest such storage methods is a Value Stream.       1. In ThingWorx Foundation, click Browse > Data Storage > Value Streams.       2. Click + New.       3. On the Choose Template pop-up, select ValueStream and click OK.       4. In the Name field, type EEFV_ValueStream.       5. If Project is not already set, search for and select PTCDefaultProject.       6. At the top, click Save.     Link Value Stream and Begin Storage   Now that we have a Value Stream to act as a storage location, we want to link it to EdgeThing.   After EdgeThing knows where to store historical data, we can simply instruct it which Property we want to archive by setting it to Logged.       1. Return to EdgeThing and its General Information tab.       2. In the Value Stream field, search for and select EEFV_ValueStream.       3. Click Save.       4. Still on EdgeThing, click Properties and Alerts.       5. Click Result_low_grease_mo to trigger the slide-out from the right-side.         6. Check Logged.       7. Click the Check icon in the top-right to close the slide-out.       8. Click Save.     Add Line Chart and Data   As per most guides in this Learning Path, it is assumed that you have an active connection to the EMS Engine Simulator and have your Analytics Event currently set to active.   This provides both the engine-sensor inputs and the analytical results for our Mashup.   After adding the Value Stream above, you'll need to let it run for a bit for the historical data to be archived. After it's run for a while and we have a valid history build-up, you can display that history in a Line Chart.       1. Return to EEFV_Mashup on the Design tab.       2. Click on the top-right section to select it.         3. From the Widgets tab, drag-and-drop a Line Chart onto the top-right section.         4. In the top-right of Mashup Builder, ensure the Data tab is selected.         5. Click the green + button.         6. On the Add Data pop-up in Entity Filter, search for and select EdgeThing.       7. In Services Filter, type queryprop.       8. Click the right arrow button besides QueryPropertyHistory.       9. Check Execute on Load.         10. Click Done.       11. Expand Data > Things_EdgeThing > QueryPropertyHistory > Returned Data.       Bind Data and View Mashup   Now that we have both our method of displaying the historical data, i.e. a Line Chart, as well as a method to bring backend data into the Mashup, i.e. QueryPropertyHistory, we can bind them together and see how our Mashup is progressing.       1. From the right under the Data tab, drag-and-drop EdgeThing > QueryPropertyHistory > Returned Data > All Data onto the Line Chart in the top-right of the Canvas.         2. On the Select Binding Target pop-up, click Data.         3. With the Line Chart selected, explore its Properties in the bottom-left.       4. Change XAxisField to timestamp.         5. Click Save.       6. Click View Mashup.     Your own Line Chart will vary depending on what values your Engine Simulator is sending to Foundation and Analytics.   NOTE: Remember that the Analysis Event needs to be Enabled for new values to be fed into Result_low_grease_mo.     Click here to view Part 3 of this guide.  

    Step 3: Modify YourEdgeThingTemplate.lua   Now that the task rate has been decreased to 1000ms (one second), we can create a series of Thing Properties.   In Windows, navigate to C:\CDEMO_EMS\etc\custom\templates.   In your prefered text-editor, open YourEdgeThingTemplate.lua.   We now want to add several lines of Lua code to define some Properties for EdgeThing. You’ll do some with some references that are pre-built into the EMS, primarily the properties structure.   Working with the engine R&D team, their plan is to place two vibration sensors on the proptype engine. In addition, each vibration sensor will have five frequency bands. As such, we’ll need ten Properties to represent the vibration readings.   In addition, we also want a Property that will record whether or not the engine is currently experiencing the low grease condition. This will be entered via manual-inspection at the same time that the frequency readings are recorded.   Perform the following to implement the ten vibration frequency bands and the low grease condition.   Below the module line of YourEdgeThingTemplate.lua, add the following line to create a low_grease Property: properties.low_grease={baseType="NUMBER", pushType="ALWAYS", value=0} Below that, add the following lines to create the five frequency bands for the first vibration sensor: properties.s1_fb1={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s1_fb2={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s1_fb3={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s1_fb4={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s1_fb5={baseType="NUMBER", pushType="ALWAYS", value=0} Below that, add the following lines to create the five frequency bands for the second vibration sensor: properties.s2_fb1={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s2_fb2={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s2_fb3={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s2_fb4={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s2_fb5={baseType="NUMBER", pushType="ALWAYS", value=0} Your code should now look like the picture below.   The code above adds each new Property to the properties structure, and the name of the Property will be what follows after the “.”, i.e. low_grease, s1_fb1, s1_fb2, etc.   In addition, the baseType defines the type of each Property, in this case, all Numbers.   The pushType of ALWAYS means that there are no restrictions on sending new Property values up to Foundation, and the value of 0 indicates the default value to which each Property will initially be set.   Generate Property Values   Now that we have the Properties defined, we want to add code which will give us different values.   To do so, we’ll define a queryHardware function, and tie the calling of it to the task rate which we had set earlier. This queryHardware function will use random numbers to simulate code that would gather actual data.    Add the following Lua code to define a GetSystemProperties function. Note that this calls a separate queryHardware function which we split out to also be called by the tasks timer. serviceDefinitions.GetSystemProperties( output { baseType="BOOLEAN", description="" }, description { "updates properties" } ) services.GetSystemProperties = function(me, headers, query, data) queryHardware() return 200, true end Add the following Lua code to define queryHardware. Note that Lua’s random number generation requires a new seed on each calling, and the randomseed function is using the built-in os.time function (plus some additional noise created by turning that time into a string and back). function queryHardware() math.randomseed( tonumber(tostring(os.time()):reverse():sub(1,6)) ) local temp = math.random(10) if temp < 6 then properties.low_grease.value=0 properties.s1_fb1.value=161+math.random() properties.s1_fb2.value=180+math.random() properties.s1_fb3.value=190+math.random() properties.s1_fb4.value=176+math.random() properties.s1_fb5.value=193+math.random() properties.s2_fb1.value=130+math.random() properties.s2_fb2.value=200+math.random() properties.s2_fb3.value=195+math.random() properties.s2_fb4.value=165+math.random() properties.s2_fb5.value=190+math.random() else properties.low_grease.value=1 properties.s1_fb1.value=90+math.random() properties.s1_fb2.value=170+math.random() properties.s1_fb3.value=170+math.random() properties.s1_fb4.value=95+math.random() properties.s1_fb5.value=190+math.random() properties.s2_fb1.value=165+math.random() properties.s2_fb2.value=195+math.random() properties.s2_fb3.value=190+math.random() properties.s2_fb4.value=140+math.random() properties.s2_fb5.value=190+math.random() end end Finally, we want to tie the calling of queryHardware to the tasks timer by adding the following code: tasks.refreshProperties = function(me) queryHardware() end   We now have code in our EMS template that not only defines the low grease condition and the five frequency bands of our two vibration sensors, but also generates some values in the ranges that R&D have typically seen in both good grease amount and bad grease amount conditions.   The final Lua code of the YourEdgeThingTemplate.lua file should look like the following:   require "shapes.swupdate" module ("templates.YourEdgeThingTemplate", thingworx.template.extend) properties.low_grease={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s1_fb1={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s1_fb2={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s1_fb3={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s1_fb4={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s1_fb5={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s2_fb1={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s2_fb2={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s2_fb3={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s2_fb4={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s2_fb5={baseType="NUMBER", pushType="ALWAYS", value=0} serviceDefinitions.GetSystemProperties( output { baseType="BOOLEAN", description="" }, description { "updates properties" } ) services.GetSystemProperties = function(me, headers, query, data) queryHardware() return 200, true end function queryHardware() math.randomseed( tonumber(tostring(os.time()):reverse():sub(1,6)) ) local temp = math.random(10) if temp < 6 then properties.low_grease.value=0 properties.s1_fb1.value=161+math.random() properties.s1_fb2.value=180+math.random() properties.s1_fb3.value=190+math.random() properties.s1_fb4.value=176+math.random() properties.s1_fb5.value=193+math.random() properties.s2_fb1.value=130+math.random() properties.s2_fb2.value=200+math.random() properties.s2_fb3.value=195+math.random() properties.s2_fb4.value=165+math.random() properties.s2_fb5.value=190+math.random() else properties.low_grease.value=1 properties.s1_fb1.value=90+math.random() properties.s1_fb2.value=170+math.random() properties.s1_fb3.value=170+math.random() properties.s1_fb4.value=95+math.random() properties.s1_fb5.value=190+math.random() properties.s2_fb1.value=165+math.random() properties.s2_fb2.value=195+math.random() properties.s2_fb3.value=190+math.random() properties.s2_fb4.value=140+math.random() properties.s2_fb5.value=190+math.random() end end tasks.refreshProperties = function(me) queryHardware() end       Step 4: Modify EdgeThing   Now that our EMS has been updated with Properties, as well as code to generate values for those Properties, we want to re-connect the EMS to Foundation and update the EdgeThing.   Note once again that EdgeThing was previously created in the Use the Edge MicroServer (EMS) to Connect to ThingWorx guide.   Restart the wsemse.exe program by returning to its PowerShell window and executing the following command: .\wsems.exe   Restart the luaScriptResource.exe program by returning to its separate PowerShell window and executing the following command: .\luaScriptResource.exe   Return to ThingWorx Foundation's EdgeThing. Note that EdgeThing is connected.   On the Properties and Alerts tab, click Manage Bindings.   At the bottom-left of the Manage Bindings pop-up, click + Add all properties.   At the bottom-right of the pop-up, click Done.   At the top, click Save.   Near the top, click Refresh repeatedly. Note that the Property values consistently change.          Click here to view Part 3 of this guide.