Abstract Conventional adaptive monitoring strategies detect anomalies in time-varying process by frequently updating models, which requires high computation complexity and may falsely include abnormal samples. Cointegration analysis (CA) based monitoring strategies can be implemented with less model updating since they are developed based on the extracted long-term equilibrium relationship. However, once the cointegration relationship changes, the previous CA model cannot accurately reflect the operation status of future nonstationary process. In this study, an adaptive monitoring scheme based on recursive CA is proposed to address the aforementioned issues for nonstationary processes. First, a recursive strategy is developed for CA to effectively update the monitoring model. After that, three monitoring statistics are developed to reflect the operation status of the industrial process with representation of both static deviation and dynamic fluctuation. Finally, an adaptive monitoring strategy is constructed based on the proposed recursive CA using the aforementioned monitoring statistics. Experimental results of two real industrial processes show that the adaptive monitoring strategy based on recursive CA can effectively adapt to normal process changes without frequent model updating.

中文翻译：

---

用于具有静态和动态变化的非平稳工业过程的自适应监控的递归协整分析

摘要 传统的自适应监测策略通过频繁更新模型来检测时变过程中的异常，计算复杂度高，并且可能会错误地包含异常样本。基于协整分析 (CA) 的监控策略可以通过较少的模型更新来实施，因为它们是基于提取的长期均衡关系开发的。但是，一旦协整关系发生变化，之前的CA模型就无法准确反映未来非平稳过程的运行状态。在这项研究中，提出了一种基于递归 CA 的自适应监控方案来解决上述非平稳过程的问题。首先，为CA开发递归策略以有效更新监控模型。之后，开发了三种监测统计量来反映工业过程的运行状态，同时具有静态偏差和动态波动的表现。最后，使用上述监控统计数据，基于所提出的递归 CA 构建自适应监控策略。两个真实工业过程的实验结果表明，基于递归CA的自适应监控策略可以有效适应正常的过程变化，无需频繁更新模型。