Data driven approach for fault detection and diagnosis of turbine in thermal power plant using Independent Component Analysis (ICA)

► Statistical signal processing method is used for fault detection and diagnosis. ► ICA is used for finding underlying components from multivariate statistical data. ► Combined statistical approach is applied for fault detection of a real power plant. ► Suggested method eliminates the requirement to detailed model of power plant. ► Results indicate that suggested method can distinguish main factors of abnormality. In this paper, a statistical signal processing technique, known as Independent Component Analysis (ICA) for fault detection and diagnosis in the real Turbine system (V94.2 model) is suggested. The information of one of MAPNA’s power plants turbine system is utilized at first. In order to reduce the dimensionality of the data set, to identify the essential variables and to choose the most useful variables, PCA approach is applied. Then, the fault sources are diagnosed by ICA technique. The results indicate that suggested approach can distinguish main factors of abnormality, among many diverse parts of a typical turbine system. The presented results will show that suggested approach can avoid false alarms and fault misdiagnosis due to changes in operation conditions and model uncertainty. The presented results show the validity and effectiveness of ICA approach for faults detection and diagnosis in noisy states.