由于原料质量波动、设备老化或局部故障、误操作等多重因素影响，工业过程不可避免的会出现生产异常，进而导致的生产过程运行工况不稳定、产品质量下降，甚至生产停机。时间序列数据异常是生产异常的直接表现，研究工业过程时间序列异常检测及校正方法将有助于工业过程操作人员提前感知生产过程运行状态，及时调整相关操作参数，该研究对保障生产安全、提升产品质量产量具有重要意义。该文综述工业过程中时间序列异常检测及校正方法。首先，分析工业过程的复杂特性，以及时间序列的时序性、多参数和多工况特性。然后，从点异常检测、上下文异常检测、集体异常检测三个方面综述时间序列异常检测方法的研究进展，针对不同异常存在的特点，分析其异常检测方法的优点和局限性。再从统计学、机器学习两个方面综述时间序列异常校正方法的研究进展。最后，总结现有时间序列的异常检测及校正方法的局限性，提出面向时序依赖与多变量耦合的时间序列异常检测方法、考虑多工况迁移与分布漂移的时间序列异常检测方法和面向可验证与可解释的时间序列异常闭环校正方法三个未来发展方向。

Due to multiple factors such as fluctuations in raw material quality, ageing equipment or localized faults, and operational errors, production anomalies inevitably occur in industrial processes. These anomalies lead to unstable operating conditions, diminished product quality, and even production stoppages. Anomalies in time series data directly manifest production irregularities. Research into anomaly detection and correction methods for industrial process time series will assist operators in anticipating operational states and adjusting relevant parameters promptly. This research holds significant importance for ensuring production safety and enhancing product quality and output. This paper reviews time-series anomaly detection and correction methods for industrial processes. First, the complex characteristics of industrial processes are analyzed, alongside the temporal, multi-parameter, and multi-condition properties of time series data. Subsequently, research progress in time series anomaly detection methods is reviewed from three perspectives: point anomaly detection, context-aware anomaly detection, and collective anomaly detection. The advantages and limitations of the respective anomaly detection methods is are analyzed based on the characteristics of different types of anomalies. Further, advancements in time series anomaly correction methods are examined from both statistical and machine learning approaches. Finally, the limitations of existing anomaly detection and correction methods are summarized, and three future research directions are proposed: a time-series anomaly detection method focused on temporal dependencies and multivariate couplings, a time-series anomaly detection method with multi-condition transferability and distribution drift, and a closed-loop time-series anomaly correction method for verifiability and interpretability.