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Energies 2018, 11(1), 232; https://doi.org/10.3390/en11010232

An Intelligent Fault Diagnosis Method for Bogie Bearings of Metro Vehicles Based on Weighted Improved D-S Evidence Theory

School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
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Received: 26 December 2017 / Revised: 14 January 2018 / Accepted: 15 January 2018 / Published: 18 January 2018
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Abstract

Bogie bearings are very important for the safe and normal operation of metro vehicles. The prevailing fault diagnosis methods for bogie bearings generally utilize a single information source, such as vibration, temperature or acoustics. There are some shortcomings in these methods, including low accuracy and poor reliability. To address these shortcomings, this paper proposes an intelligent fault diagnosis method. Based on improved D-S (Dempster-Shafer) evidence theory, this method comprehensively analyzes vibration and temperature signals to diagnose bearing faults. In order to verify the feasibility and effectiveness of the proposed method, this study designed the hardware device and constructed a test platform. Bogie bearings with faults occurring on the outer ring, inner ring and rolling elements were tested on this platform. The diagnosis accuracy rate of the proposed fusion algorithm reached 91%, and the misdiagnosis rate was only 2%. The test results showed that the proposed method can accurately and reliably realize fault diagnosis with a high accuracy rate and a low misdiagnosis rate compared to previous methods. Thus, the proposed fault diagnosis method can accurately and effectively identify the faults of metro vehicle bogie bearings. View Full-Text
Keywords: fault diagnosis; bogie bearing; multi-source information; D-S evidence theory; fusion algorithm fault diagnosis; bogie bearing; multi-source information; D-S evidence theory; fusion algorithm
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Liu, J.; Chen, A.; Zhao, N. An Intelligent Fault Diagnosis Method for Bogie Bearings of Metro Vehicles Based on Weighted Improved D-S Evidence Theory. Energies 2018, 11, 232.

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