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.

翻译：本文提出了一种两阶段伪异常引导的异常检测方法（TPA-AD），用于在仅训练样本为正常样本的条件下进行轴箱轴承时间序列异常检测。该方法首先利用重构模型和逐特征目标误差控制，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，学习对异常敏感的表征；最后基于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通过构造边界邻域内的伪异常提升了正常边界的可分性，并能协同处理混合变量场景中的连续与离散特征。主要实验在轴承故障检测数据集和退化过程数据集上开展，并在13个公开时间序列异常检测数据集上进行了探索性扩展。结果表明，所提方法能产生相对稳定的异常响应，对退化演变敏感，并在公开时间序列异常检测基准测试及真实高速列车相关轴承数据上展现出一定程度的泛化适用性。