A data indicator-based deep belief networks to detect multiple faults in axial piston pumps

A data indicator-based deep belief networks to detect multiple faults in axial piston pumps Mechanical Systems and Signal Processing 112 (2018) 154–170 Contents lists available at ScienceDirect Mechanical Systems and Signal Processing journal homepage: www.elsevier.com/locate/ymssp A data indicator-based deep belief networks to detect multiple faults in axial piston pumps Shuhui Wang, Jiawei Xiang , Yongteng Zhong, Hesheng Tang College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, PR China article i nfo ab stra ct Article history: Detecting faults in axial piston pumps is of significance to enhance the reliability and secu- Received 19 December 2017 rity of hydraulic systems. However, it is difficult to detect multiple faults in the hydraulic Received in revised form 10 April 2018 electromechanical coupling systems because the fault mechanism of some faults is unclear. Accepted 22 April 2018 In this paper, a method using deep belief networks (DBNs) is proposed to detect multiple Available online 27 April 2018 faults in axial piston pumps. Firstly, for each individual fault, all the data indicators extracted from the raw signals in time domain, frequency domain and time-frequency Keywords: domain are calculated to construct training and testing samples. Then, the constructed Piston pumps samples are fed into DBNs to classify the multiple faults in axial piston pumps. With Multiple faults classification http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Mechanical Systems and Signal Processing Elsevier

A data indicator-based deep belief networks to detect multiple faults in axial piston pumps

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
0888-3270
eISSN
1096-1216
D.O.I.
10.1016/j.ymssp.2018.04.038
Publisher site
See Article on Publisher Site

Abstract

Mechanical Systems and Signal Processing 112 (2018) 154–170 Contents lists available at ScienceDirect Mechanical Systems and Signal Processing journal homepage: www.elsevier.com/locate/ymssp A data indicator-based deep belief networks to detect multiple faults in axial piston pumps Shuhui Wang, Jiawei Xiang , Yongteng Zhong, Hesheng Tang College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, PR China article i nfo ab stra ct Article history: Detecting faults in axial piston pumps is of significance to enhance the reliability and secu- Received 19 December 2017 rity of hydraulic systems. However, it is difficult to detect multiple faults in the hydraulic Received in revised form 10 April 2018 electromechanical coupling systems because the fault mechanism of some faults is unclear. Accepted 22 April 2018 In this paper, a method using deep belief networks (DBNs) is proposed to detect multiple Available online 27 April 2018 faults in axial piston pumps. Firstly, for each individual fault, all the data indicators extracted from the raw signals in time domain, frequency domain and time-frequency Keywords: domain are calculated to construct training and testing samples. Then, the constructed Piston pumps samples are fed into DBNs to classify the multiple faults in axial piston pumps. With Multiple faults classification

Journal

Mechanical Systems and Signal ProcessingElsevier

Published: Nov 1, 2018

References

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