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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.
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.
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.
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.
Congratulations! You've successfully completed the Build an Engine Analytical Model guide, and learned how to:
The next guide in the Vehicle Predictive Pre-Failure Detection with ThingWorx Platform
learning path is Manage an Engine Analytical Model.
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