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Appl. Sci. 2017, 7(3), 280; doi:10.3390/app7030280

A New Engine Fault Diagnosis Method Based on Multi-Sensor Data Fusion

School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, Shaanxi, China
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Academic Editor: David He
Received: 29 January 2017 / Revised: 1 March 2017 / Accepted: 9 March 2017 / Published: 14 March 2017
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Abstract

Fault diagnosis is an important research direction in modern industry. In this paper, a new fault diagnosis method based on multi-sensor data fusion is proposed, in which the Dempster–Shafer (D–S) evidence theory is employed to model the uncertainty. Firstly, Gaussian types of fault models and test models are established by observations of sensors. After the models are determined, the intersection area between test model and fault models is transformed into a set of BPAs (basic probability assignments), and a weighted average combination method is used to combine the obtained BPAs. Finally, through some given decision making rules, diagnostic results can be obtained. The proposed method in this paper is tested by the Iris data set and actual measurement data of the motor rotor, which verifies the effectiveness of the proposed method. View Full-Text
Keywords: multi-sensor data fusion; fatal diagnosis; Dempster–Shafer evidence theory; uncertainty; Gaussian distribution multi-sensor data fusion; fatal diagnosis; Dempster–Shafer evidence theory; uncertainty; Gaussian distribution
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Jiang, W.; Hu, W.; Xie, C. A New Engine Fault Diagnosis Method Based on Multi-Sensor Data Fusion. Appl. Sci. 2017, 7, 280.

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