Gas path fault diagnostics using a hybrid intelligent method for industrial gas turbine engines

• Published in: Journal of the Brazilian Society of Mechanical Sciences and Engineering Vol. 40; no. 12; pp. 1 - 17
• Main Authors: Amare, D. F., Aklilu, T. B., Gilani, S. I.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2018; Springer Nature B.V
• Subjects: Classification; Classifiers; Engineering; Fault diagnosis; Feature extraction; Gas turbine engines; Hybrid systems; Industrial gases; Machine learning; Mechanical Engineering; Multilayer perceptrons; Neural networks; Noise measurement; Noise reduction; Nonlinear analysis; Nonlinearity; Principal components analysis; Random noise; Technical Paper; Uncertainty; Gas turbine; Artificial neural network; Gas path fault; Fault diagnostics; Machine learning
• ISSN: 1678-5878; 1806-3691
• DOI: 10.1007/s40430-018-1497-6

There are many challenges against an accurate gas turbine fault diagnostics, such as the nonlinearity of the engine health, the measurement uncertainty, and the occurrence of simultaneous faults. The conventional methods have limitations in effectively handling these challenges. In this paper, a hybrid intelligent technique is devised by integrating an autoassociative neural network (AANN), nested machine learning (ML) classifiers, and a multilayer perceptron (MLP). The AANN module is used as a data preprocessor to reduce measurement noise and extract the important features for visualisation and fault diagnostics. The features are extracted from the bottleneck layer output values based on the concept of the nonlinear principal component analysis (NLPCA). The nested classifier modules are then used in such a manner that fault and no-fault conditions, component and sensor faults, and different component faults are distinguished hierarchically. As part of the classification, evaluation of the fault classification performance of five widely used ML techniques aiming to identify alternative approaches is undertaken. In the end, the MLP approximator is utilised to estimate the magnitude of the isolated component faults in terms of flow capacity and isentropic efficiency indices. The developed system was implemented to diagnose up to three simultaneous faults in a two-shaft industrial gas turbine engine. Its robustness towards the measurement uncertainty was also evaluated based on Gaussian noise corrupted data. The test results show the derivable benefits of integrating two or more methods in engine diagnostics on the basis of offsetting the weakness of the one with the strength of another.