In this work, the sensor data related to a diesel engine system and specifically its turbocharger subsystem were analyzed. An incident where the turbocharger seized was recorded by dozens of standard turbocharger-related sensors. By training models to distinguish between normal healthy operating conditions and deteriorated conditions, there is an opportunity to develop prognostic and predictive tools to ideally help prevent a similar occurrence in the future. Analysis of this event provides an opportunity to identify changes in equipment indicators with a known outcome. A number of data analysis tools were used to characterize the healthy and deteriorated states of the turbocharger system, including various supervised classification as well as semi-supervised and unsupervised anomaly detection techniques. The leader clustering algorithm was also implemented to reduce the amount of data to train and develop the models. This paper describes the results of this modeling process, validated by testing on healthy data from the same propulsion system and a second distinct one. Although this problem posed challenges due to the severely imbalanced class distribution, the supervised classifiers, in particular Support Vector Machine (SVM) and Random Forest (RF), performed very well in all metrics while the unsupervised anomaly detection models achieved near-perfect accuracy for identifying healthy turbocharger states.
Anomaly detection in multi-class time series
