Optimization-based Approaches for Fault Detection and Estimation

Abstract

Advancements in technology and societal demands have led to increasing complexity, size, and automation in modern industrial systems. This trend makes these systems more safety-critical, as the occurrence of faults in system components or subsystems may cause the entire system to fail, resulting in significant economic losses and casualties. Consequently, developing an effective fault diagnosis method is crucial for ensuring the reliability, safety, and performance of industrial systems, especially energy systems, which are so relevant to our lives. However, most model-based fault diagnosis systems developed based on observers and parity space relations have the same order as that of the system. This can cause a significant computational burden when dealing with large-scale and high-dimensional systems. This thesis is dedicated to the design of fault diagnosis filters in the framework of differential-algebraic equations, which produce scalable residual generators with design flexibility. Meanwhile, we consider the impact of disturbances and stochastic noise ondiagnosis results, as well as the fault diagnosis problem within the finite frequency domain. In order to design filters capable of handling these issues, we solve filter parameters through optimization problems that are constructed based on specific diagnosis requirements.