Case Study: Models for Detecting Low Oil Pressure Anomalies on Commercial Vehicles
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Abstract
We present a case study of anomaly detection using commercial vehicle data (from a single vehicle collected over a six-month interval) and propose a failure-event analysis. Our analysis allows performance comparison of anomaly detection models in the absence of sufficient anomalies to compute the Receiver Operating Characteristic curve.
Several heuristically-guided data-driven models were considered to capture the relationship among three main engine signals (oil pressure, temperature, and speed). These models include regression-based approaches and distance-based approaches; the former use the residual’s z-score as the detection metric, while the latter use a Mahalanobis distance or similar measure as the metric. The selected regression-based models (Boosted Regression Trees, Feed-Forward Neural Networks, and Gridded Regression tables) outperformed the selected distance-based approaches (Gaussian Mixtures and Replicator Neural Networks). Both groups of models were superior to existing Diagnostic Trouble Codes. The Gridded Regression tables and Boosted Regression Trees exhibited the best overall metric performance.
We report a surprising behavior of one of the models: locally- optimal Gaussian Mixture Models often had zero detection performance, with such models occurring in at least 25% of the iterations with seven or more Gaussians in the mixture. To overcome the problem, we propose a regularization method that employs a heuristic filter for rejecting Gaussian Mixtures with non-discriminative components.
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