Fault detection under fuzzy model uncertainty

Abstract

This chapter tackles a problem of robust fault detection using Takagi–Sugeno fuzzy models. The model-based strategy is employed to generate the residuals to make decision about the state of the process. Unfortunately, this method is corrupted by the model uncertainty due to the fact that in real applications model-reality mismatch usually exists. To ensure the reliable fault detection the adaptive threshold technique is used to deal with the problem. The chapter also focuses on fuzzy model structure design. The bounded error approach is applied to generate the rules for the model using available measurements. Proposed approach is applied to fault detection in the DC laboratory engine.