Intelligent fault diagnosis for dynamic systems via extended state observer and soft computing

Abstract This chapter aims to address the common model-based fault diagnosis difficulties encountered in industries today. In other words, the plant model is usually uncertain, which leads to the model-based fault diagnosis techniques not working well. Furthermore, most fault diagnosis techniques assume that only the same type of fault, such as a process fault, occurs at the same time. This study uses extended state observer to detect faults without exact knowledge of the plant model, and a fuzzy inference system to help in fault isolation and fault identification. Considering that some faults are incipient in nature, trained neural networks were employed to help identify faults and determine the degree of fault in real time by means of inputs-to-outputs mapping. This chapter first presents fault diagnosis techniques by means of extended state observer, followed by investigating the complexity of fault isolation due to simultaneous faults of different types. A strongly coupled three-tank dynamic system—MIMO (a typical multiple-input, multiple-output system)—is given as a case study to illustrate the effectiveness of the presented techniques by means of computer simulation. As a result, this study demonstrates how faults can be detected with an uncertain plant model and how soft computing can make fault isolation and identification more reliable.

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