Abstract This article proposes a new active fault-tolerant control (AFTC) method based on moving window hidden Markov models (MWHMM) for nonlinear processes. The AFTC approach could be divided into two steps, fault detection and identification (FDI) and fault accommodation. The information obtained from the FDI step is used for fault accommodation, under the framework of model predictive control (MPC). Firstly, MWHMM is utilized for online fault detection and identification. Moreover, to improve the discernibility of the prediction status probability density function (PDF) for identification between the normal and faulty modes, the Hellinger distance, which could measure the similarity between probability distributions, is introduced. As the separability between the normal and faulty modes is increased, the accuracy and reliability of fault identification by MWHMM is greatly improved. At last, not only the occurrence of a single fault but also the occurrence of a hybrid fault, which means that multiple faults occur simultaneously, is considered in this article. The validity of the suggested method is verified by the simulation results on a three-tank water system.

中文翻译：

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一种基于移动窗口隐马尔可夫模型的主动容错控制方法

摘要 本文针对非线性过程，提出了一种基于移动窗口隐马尔可夫模型（MWHMM）的主动容错控制（AFTC）方法。AFTC 方法可以分为两个步骤，故障检测和识别（FDI）和故障调节。在模型预测控制 (MPC) 的框架下，从 FDI 步骤获得的信息用于故障调节。首先，MWHMM用于在线故障检测和识别。此外，为了提高预测状态概率密度函数（PDF）用于识别正常模式和故障模式的可辨别性，引入了可以衡量概率分布之间相似性的Hellinger距离。随着正常模式和故障模式之间的可分离性增加，MWHMM故障识别的准确性和可靠性大大提高。最后，本文不仅考虑了单个故障的发生，还考虑了混合故障的发生，即多个故障同时发生。通过对三箱水系统的仿真结果验证了所提出方法的有效性。