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Sensors 2016, 16(2), 185; doi:10.3390/s16020185

Simultaneous-Fault Diagnosis of Gearboxes Using Probabilistic Committee Machine

Department of Electromechanical Engineering, University of Macau, Macao, China
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Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 23 October 2015 / Revised: 21 January 2016 / Accepted: 22 January 2016 / Published: 2 February 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [1766 KB, uploaded 2 February 2016]   |  

Abstract

This study combines signal de-noising, feature extraction, two pairwise-coupled relevance vector machines (PCRVMs) and particle swarm optimization (PSO) for parameter optimization to form an intelligent diagnostic framework for gearbox fault detection. Firstly, the noises of sensor signals are de-noised by using the wavelet threshold method to lower the noise level. Then, the Hilbert-Huang transform (HHT) and energy pattern calculation are applied to extract the fault features from de-noised signals. After that, an eleven-dimension vector, which consists of the energies of nine intrinsic mode functions (IMFs), maximum value of HHT marginal spectrum and its corresponding frequency component, is obtained to represent the features of each gearbox fault. The two PCRVMs serve as two different fault detection committee members, and they are trained by using vibration and sound signals, respectively. The individual diagnostic result from each committee member is then combined by applying a new probabilistic ensemble method, which can improve the overall diagnostic accuracy and increase the number of detectable faults as compared to individual classifiers acting alone. The effectiveness of the proposed framework is experimentally verified by using test cases. The experimental results show the proposed framework is superior to existing single classifiers in terms of diagnostic accuracies for both single- and simultaneous-faults in the gearbox. View Full-Text
Keywords: simultaneous-fault diagnosis; Hilbert-Huang transform; pairwise-coupling probabilistic committee machine simultaneous-fault diagnosis; Hilbert-Huang transform; pairwise-coupling probabilistic committee machine
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Zhong, J.-H.; Wong, P.K.; Yang, Z.-X. Simultaneous-Fault Diagnosis of Gearboxes Using Probabilistic Committee Machine. Sensors 2016, 16, 185.

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