Abstract

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.

Highlights

  • Gas turbine engine provides power for airplane, and its reliable operation plays an important role in flight safety

  • It ispotential important develop methodologies tocosts improve confidence of performance important to develop methodologies to improve the confidence of performance fault diagnosis for gas fault diagnosis for gas turbine engine using various kinds of available sensors

  • In order to assess the confidence of fault diagnosis by the weighted Dempster–Shaffer evidence theory (WDS), two engine fault modes such as High-Pressure Turbine (HPT) failure (F3 ) and Fan and Compressor (F5 ) are simulated

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Summary

Introduction

Gas turbine engine provides power for airplane, and its reliable operation plays an important role in flight safety. Due to the complexity of structure and terrible work condition, gas turbine engine has more opportunity to break down [1]. Engine failures is generally divided into gas-path performance fault, vibration structure fault and auxiliary system fault, among which the performance fault causes more maintenance costs and off-wing time [2]. Energies 2016, 9, 828; doi:10.3390/en9100828 www.mdpi.com/journal/energies interest, with great to to reduce engine maintenance and the improve availability [3,4]. It is availability [3,4]. It ispotential important develop methodologies tocosts improve confidence of performance important to develop methodologies to improve the confidence of performance fault diagnosis for gas fault diagnosis for gas turbine engine using various kinds of available sensors

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