A New Fault Diagnosis Approach for Heavy-Duty Gas Turbines

Abstract

Effective fault detection, estimation, and isolation are essential for the safety and reliability of gas turbines. In this article, a hybrid fault detection and isolation (FDI) approach is presented for condition monitoring of heavy-duty gas turbines. First, nonlinear dynamical models are constructed using an orthonormal basis function and an adaptive neuro-fuzzy inference system through experimental data. Following that, a fuzzy inference system is employed to estimate the fault severity. For this aim, fuzzy rules are extracted from the fault patterns by defining appropriate features. Later, various faults are isolated using an ensemble decision tree classifier. The proposed nonlinear modeling compensates for disturbances and uncertainty in the system and leads to adaptive thresholds for fault detection, which reduces the false alarm rate. Moreover, the proposed FDI method brings high accuracy in fault estimation by properly modeling a bounded uncertainty using the adaptive threshold. Experimental data are applied to validate the gas turbine model. Test results indicate that the proposed hybrid FDI method via adaptive threshold overwhelms the other FDI methods, where the misclassified data are 5.6%.