A novel gas turbine fault detection and identification strategy based on hybrid dimensionality reduction and uncertain rule-based fuzzy logic

Abstract

Persistent health monitoring of a gas turbine engine preserves the operational efficiency and lifetime of its components. An online monitoring system should meet requirements such as high accuracy, reliability against measurement uncertainty, and ease of implementation. Factors such as high dimensions of data, variety of deteriorations, and similarity of fault patterns are also challenging to the Fault Detection and Identification (FDI) process. In this paper, a new gas turbine health assessment strategy based on hybrid dimensionality reduction and Interval Type-2 Fuzzy Logic (IT2FL) is developed, and it is demonstrated that the proposed pre-processing method not only reduces the computational burden, but also preserves the part of information that is necessary for maintaining the reliability of the FDI procedure. In this approach, raw data are projected to a nonlinear self-organized feature space, and optimum feature subsets are selected using multi-objective optimization. Pre-processed data are then utilized for training a bank of IT2FL Systems (IT2FLSs), which are developed to estimate the gas turbine’s health status at various ambient and working conditions. The proficiency of the proposed approach is compared to other feature extraction methods, and it is indicated that the Self-Organizing Map (SOM) based FDI system outstands in terms of incipient fault diagnosis, reliability, and robustness against measurement uncertainty.