{"ID":3053043,"CreatedAt":"2026-06-04T04:41:36.695875263Z","UpdatedAt":"2026-06-05T11:43:53.432517148Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.04073","arxiv_id":"2606.04073","title":"TPA-AD: A Two-Stage Pseudo Anomaly-Guided Method for Bearing Time-Series Anomaly Detection","abstract":"This paper proposes a two-stage pseudo anomaly-guided anomaly detection method (\\textbf{T}wo-stage \\textbf{P}seudo \\textbf{A}nomaly-guided \\textbf{A}nomaly \\textbf{D}etection, \\textbf{TPA-AD}) for axle-box bearing time-series anomaly detection (time series anomaly detection, TSAD) under the setting where only normal samples are available for training. The method first generates pseudo-anomalous windows near the normal boundary using a reconstruction model and per-feature target-error control. It then learns anomaly-sensitive representations through contrastive learning between normal and pseudo-anomalous windows, and finally produces window-level and point-level anomaly scores using k-nearest neighbors (KNN). Compared with existing methods that rely on known fault categories, real anomaly priors, or random anomaly injection, TPA-AD improves the separability of the normal boundary by constructing pseudo-anomalies in boundary neighborhoods and can jointly handle continuous and discrete features in mixed-variable scenarios. The main experiments are conducted on bearing fault detection datasets and degradation-process datasets, with an additional exploratory extension on $13$ public TSAD datasets. The results show that the proposed method yields relatively stable anomaly responses, is sensitive to degradation evolution, and demonstrates a certain degree of broader applicability on public TSAD benchmarks and real high-speed-train-related bearing data.","short_abstract":"This paper proposes a two-stage pseudo anomaly-guided anomaly detection method (\\textbf{T}wo-stage \\textbf{P}seudo \\textbf{A}nomaly-guided \\textbf{A}nomaly \\textbf{D}etection, \\textbf{TPA-AD}) for axle-box bearing time-series anomaly detection (time series anomaly detection, TSAD) under the setting where only normal sa...","url_abs":"https://arxiv.org/abs/2606.04073","url_pdf":"https://arxiv.org/pdf/2606.04073v1","authors":"[\"Xiancheng Wang\",\"Zhibo Zhang\",\"Ran Li\",\"Rui Wang\",\"Minghang Zhao\",\"Shisheng Zhong\",\"Lin Wang\"]","published":"2026-06-02T15:39:10Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"stat.ML\"]","methods":"[\"LoRA\"]","has_code":false}