Bearing Fault Diagnosis using Multi-sensor Fusion based on weighted D-S Evidence Theory

Huo, Zhiqiang and Zhang, Yu and Shu, Lei and Yunrong, Lv and Shuiquan, Lin (2018) Bearing Fault Diagnosis using Multi-sensor Fusion based on weighted D-S Evidence Theory. In: The 18th International Conference on Mechatronics – Mechatronika 2018, December 5 – 7, 2018, Brno, Czech Republic.

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Bearing Fault Diagnosis using Multi-sensor Fusion based on weighted D-S Evidence Theory
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Abstract

This paper has presented a novel method for bearing fault diagnosis using a multi-sensor fusion approach based on an improved weighted Dempster-Shafer (D-S) evidence theory combined with Genetic Algorithm (GA). Vibration measurements are collected from an industrial multi-stage centrifugal air compressor using three wireless acceleration sensors. Fine-to-Coarse Multiscale Permutation Entropy (F2CMPE)
is applied to extract the complexity changes of vibration data sets. Then, the extracted feature vectors produced by
F2CMPE via multiple scales are fed into Back Propagation Neural Network (BPNN) for fault classification. The normalized
probability outputs of BPNN are considered now as inputs of the proposed weighted D-S evidence theory for multi-sensor
information fusion. The measurements collected from real industrial equipment are analyzed using the proposed diagnosis
method, and the experimental validation has demonstrated its efficiency to identify rolling bearing conditions, the results of which have also shown higher accuracy compared to those using individual sensor signal analysis.

Keywords:Fault Diagnosis, Multi-sensor Fusion, Fine-to- Coarse Multiscale Permutation Entropy, Back Propagation Neural Network, Weighted D-S Evidence Theory
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
H Engineering > H342 Vibration
H Engineering > H300 Mechanical Engineering
Divisions:College of Science > School of Engineering
ID Code:33821
Deposited On:19 Oct 2018 08:22

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