Anomaly Detection for Vehicle Diagnostics based on OBD Snapshots with Cause Investigation

Vehicle diagnostic systems are crucial for the normal operation of vehicles and their propulsion-related systems. Undetected unusual behaviour of such systems makes the vehicle diagnostic system unreliable. Current diagnostic systems, such as On-Board Diagnostics (OBD), are limited to monitoring only specific systems in order to make fault decision. However, various anomalies, including drastic performance drops, vehicle tampering, and changes in the driving environment, often go undetected during OBD system testing, validation, and inspection. This research presents a novel explainable OBD anomaly detection pipeline that is able to detect anomalies based only on OBD data snapshots during processes of OBD validation and inspection. The novel approach is implemented using combined dimension reduction and data clustering methodologies. First, the data is transformed into a latent space using t-distributed Stochastic Neighbor Embedding (t-SNE), where the general structure of the anomaly in the data can be exploited. Subsequently, clustering using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is applied to group similar normal data and identify anomalous patterns. The novelty of the solution is further extended with a feedback loop that suggests the root cause of OBD signals for individual anomalies using explainable AI (XAI) methodology, in this case Shapley additive explanations (SHAP). The proposed concept was verified and evaluated using real OBD snapshots with synthetically generated anomalies in two scenarios with different engine status, engine off and on, with an achieved accuracy of 92.89% and 96.45% for anomaly detection, respectively. The majority of anomaly causes in the form of specific OBD signals from propulsion- and emission-related systems were successfully explained using SHAP.