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Sensors 2016, 16(6), 895; doi:10.3390/s16060895

Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning

1
School of Mechanical Engineering, Dongguan University of Technology, Dongguan 523808, China
2
Department of Mechanical Engineering, Universidad Politécnica Salesiana, Cuenca 010105, Ecuador
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 13 May 2016 / Revised: 7 June 2016 / Accepted: 13 June 2016 / Published: 17 June 2016
(This article belongs to the Section Physical Sensors)
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Abstract

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults. View Full-Text
Keywords: fault diagnosis; deep learning; statistical feature; vibration sensor; rotating machinery fault diagnosis; deep learning; statistical feature; vibration sensor; rotating machinery
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

Li, C.; Sánchez, R.-V.; Zurita, G.; Cerrada, M.; Cabrera, D. Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning. Sensors 2016, 16, 895.

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