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.
Bibliographical noteFunding Information:
This work is supported in part by the Secretariat for Higher Education, Science, Technology and Innovation of the Republic of Ecuador (GIDTEC project No. 009-004-2015-07-16), the National Natural Science Foundation of China (Grant No. 51375517), and the Project of Chongqing Science & Technology Commission (Grant No. cstc2014gjhz70002). The valuable comments and suggestions from the two anonymous reviewers are very much appreciated
© 2016 by the authors.
- Deep learning
- Fault diagnosis
- Rotating machinery
- Statistical feature
- Vibration sensor