TPA-AD: 一种用于轴承时间序列异常检测的两阶段伪异常引导方法
TPA-AD: A Two-Stage Pseudo Anomaly-Guided Method for Bearing Time-Series Anomaly Detection
AI总结：
提出一种两阶段伪异常引导方法TPA-AD，通过重构模型和特征误差控制生成边界伪异常窗口，结合对比学习与KNN实现无监督轴承时间序列异常检测，在轴承故障和退化数据集上表现稳定且具泛化性。
摘要：
本文提出了一种两阶段伪异常引导的异常检测方法（TPA-AD），用于在仅正常样本可用的训练设置下进行轴箱轴承时间序列异常检测（TSAD）。该方法首先利用重构模型和每特征目标误差控制在正常边界附近生成伪异常窗口，然后通过正常窗口与伪异常窗口之间的对比学习学习异常敏感表示，最后使用k近邻（KNN）生成窗口级和点级异常分数。与依赖已知故障类别、真实异常先验或随机异常注入的现有方法相比，TPA-AD通过在边界邻域构建伪异常提高了正常边界的可分离性，并能联合处理混合变量场景中的连续和离散特征。主要实验在轴承故障检测数据集和退化过程数据集上进行，并在13个公共TSAD数据集上进行了额外的探索性扩展。结果表明，所提方法产生相对稳定的异常响应，对退化演化敏感，并在公共TSAD基准和真实高速列车相关轴承数据上表现出一定程度的更广泛适用性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
