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Use Analytics Manager to automatically perform engine analytical calculations.
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.
NOTE: This guide's content aligns with ThingWorx 9.3. The estimated time to complete this guide is 60 minutes
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.
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.
Once you have configured an Analysis Provider, you can publish Models from Analytics Builder to Analytics Manager.
Close that new browser tab, and instead click Analysis Models in the ThingWorx Composer Analytics navigation.
At the top, click Enable.
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.
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.
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.