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A Novel Data Hierarchical Fusion Method for Gas Turbine Engine Performance Fault Diagnosis

Jiangsu Province Key Laboratory of Aerospace Power Systems, College of Energy & Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu, China
Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada
Aviation Motor Control System Institute, Aviation Industry Corporation of China, Wuxi 214063, Jiangsu, China
Author to whom correspondence should be addressed.
Academic Editor: Chang Sik Lee
Energies 2016, 9(10), 828;
Received: 27 July 2016 / Revised: 27 September 2016 / Accepted: 11 October 2016 / Published: 15 October 2016
PDF [3788 KB, uploaded 15 October 2016]


Gas path fault diagnosis involves the effective utilization of condition-based sensor signals along engine gas path to accurately identify engine performance failure. The rapid development of information processing technology has led to the use of multiple-source information fusion for fault diagnostics. Numerous efforts have been paid to develop data-based fusion methods, such as neural networks fusion, while little research has focused on fusion architecture or the fusion of different method kinds. In this paper, a data hierarchical fusion using improved weighted Dempster–Shaffer evidence theory (WDS) is proposed, and the integration of data-based and model-based methods is presented for engine gas-path fault diagnosis. For the purpose of simplifying learning machine typology, a recursive reduced kernel based extreme learning machine (RR-KELM) is developed to produce the fault probability, which is considered as the data-based evidence. Meanwhile, the model-based evidence is achieved using particle filter-fuzzy logic algorithm (PF-FL) by engine health estimation and component fault location in feature level. The outputs of two evidences are integrated using WDS evidence theory in decision level to reach a final recognition decision of gas-path fault pattern. The characteristics and advantages of two evidences are analyzed and used as guidelines for data hierarchical fusion framework. Our goal is that the proposed methodology provides much better performance of gas-path fault diagnosis compared to solely relying on data-based or model-based method. The hierarchical fusion framework is evaluated in terms to fault diagnosis accuracy and robustness through a case study involving fault mode dataset of a turbofan engine that is generated by the general gas turbine simulation. These applications confirm the effectiveness and usefulness of the proposed approach. View Full-Text
Keywords: gas turbine; performance fault diagnosis; data fusion; extreme learning machine; evidence theory gas turbine; performance fault diagnosis; data fusion; extreme learning machine; evidence theory

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Lu, F.; Jiang, C.; Huang, J.; Wang, Y.; You, C. A Novel Data Hierarchical Fusion Method for Gas Turbine Engine Performance Fault Diagnosis. Energies 2016, 9, 828.

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