A Unified Detection Approach for Point and Subsequence Anomaly Data From Train Axle Temperature Sensors

The train axle temperature sensor data exhibit two distinct abnormal patterns: point anomalies and subsequence anomalies, which reflect the intricate operational state of the axle and train. Precise detection of multicategory anomalies within the axle temperature data is crucial for ensuring the safe and stable operation of trains. However, the coexistence of multiple anomaly types leads to diminished accuracy in existing anomaly detection methods. Hence, we propose an unsupervised unified detection model, leveraging the power of the prediction model and targeting both point and subsequence anomalies. Our model begins by utilizing long short-term memory (LSTM) networks to predict the axle temperature, thereby generating a residual sequence. To ameliorate the error accumulation effect of LSTM, a weighting mechanism is introduced during the prediction process. Subsequently, the kernel density estimation (KDE) method is adopted to perform piecewise nonparametric fitting on the distribution of the residual sequence, enabling the determination of nonparametric dynamic thresholds for point anomalies. Finally, using the detected point anomalies as boundary points, the residual sequence is partitioned into multiple subsequences of varying lengths. The improved local outlier factor (ILOF) method is applied to compute the anomaly score of each subsequence, enabling the recognition of subsequence anomalies. The evaluation experiments for anomaly detection were conducted on two axle temperature datasets. The F1-scores were all above 0.975, which verified the effectiveness of the model. Compared with the baseline models, the F1-score of the proposed model has increased by more than 5%, which verified the superiority of the proposed model.