
Anomaly Detection in Test Fields Study
Explore anomaly detection techniques in test fields through data analysis and distribution changes to identify potential issues and improve performance in June 2019.
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Anomaly Detection in Test Fields Sandipan Roy, Margaret Duff, Lizhi Zhang, Clarice Poon, Matthew Nunes, Georg Seewald (AVL), Corinna Kindhofer (AVL) ITT10, June 2019
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The Data Control Points 2500 Average Engine Speed 2000 1500 1000 500 0 -500 0 500 1000 1500 2000 2500 Average Lever Arm Torque 11 examples 5 with problems , 6 without 180 control points for each sample 3 main measures and some more information Anomaly Detection in Test Fields | ITT10 | June 2019
The Master Plan (for now) 1. Inverting formula Given data from 2 sensors and assuming a perfect ratio of air intake and output , recreate the data from the third sensor 2. With labelled data train classifiers E.g. Logistic regression, neural networks Data augmentation if needed 3. Looking for changes in distribution Gaussian Mixture Treat results as sampling from some unknown distribution Look for evidence of changes in distribution Anomaly Detection in Test Fields | ITT10 | June 2019