TPA-AD: A Two-Stage Pseudo Anomaly-Guided Method for Bearing Time-Series Anomaly Detection

In three linesTPA-AD proposes a two-stage method for bearing time-series anomaly detection. It generates pseudo-anomalies near the normal boundary using a reconstruction model, then learns anomaly-sensitive representations via contrastive learning and KNN scoring. Tested on bearing fault datasets and 13 public TSAD benchmarks.

Summary generated by Claude — human-verified