Multiple Fault Detection and Diagnosis in a Gas Turbine Using Nonlinear Principal Component Analysis and Structured Residuals

Abstract

Multiple fault detection and diagnosis is a challenging problem because the number of candidates grows exponentially in the number of faults. In addition, multiple faults in dynamic systems may be hard to detect, because they can mask or compensate each other’s effects. This paper presents the study of the detection and diagnosis of multiple faults in a SR-30 Gas Turbine using nonlinear principal component analysis as the detection method and structured residuals as the diagnosis method. The study includes developing a mathematical model, software simulation with Matlab Simulink and implementation of algorithms for detection and diagnosis of multiple faults in the system using nonlinear principal component analysis and structured residuals. A real SR-30 gas turbine was used for our studies. The equipment is at the moment installed in the Inter American University of Puerto Rico, Bayamon Campus, and Department of Mechanical Engineering.