TPA-AD uses pseudo-anomalies to detect bearing faults without labeled data
Training only on normal data, this new method catches anomalies near the boundary.
TPA-AD (Two-stage Pseudo Anomaly-guided Anomaly Detection) tackles a critical industrial challenge: detecting bearing faults in high-speed trains when only normal operating data is available for training. Unlike traditional methods that require known fault categories or random anomaly injection, TPA-AD constructs pseudo-anomalies by perturbing normal samples near the decision boundary. The first stage uses a reconstruction model with per-feature target-error control to generate realistic anomalous windows that lie just outside the normal region. The second stage applies contrastive learning between normal and these pseudo-anomalous windows to learn representations that are highly sensitive to subtle deviations.
For scoring, TPA-AD employs k-nearest neighbors (KNN) to produce both window-level and point-level anomaly scores, enabling granular fault localization. The method handles mixed-variable data (continuous and discrete features) seamlessly. Experiments on bearing fault detection and degradation-process datasets demonstrate stable anomaly responses and sensitivity to gradual wear. An exploratory extension across 13 public time-series anomaly detection (TSAD) benchmarks confirms broader applicability. This work is especially relevant for predictive maintenance in railways, where fault examples are scarce but normal operation data is abundant.
- Two-stage pipeline: pseudo-anomaly generation via reconstruction, then contrastive learning for anomaly-sensitive representations.
- Uses k-nearest neighbors (KNN) for both window-level and point-level anomaly scoring.
- Validated on bearing fault datasets, degradation processes, and 13 public TSAD benchmarks with stable, degradation-sensitive results.
Why It Matters
Enables reliable bearing anomaly detection in high-speed trains without requiring any labeled fault data.