Next Article in Journal
A Study on Wireless Charging for Prolonging the Lifetime of Wireless Sensor Networks
Previous Article in Journal
An RFID-Based Smart Structure for the Supply Chain: Resilient Scanning Proofs and Ownership Transfer with Positive Secrecy Capacity Channels
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(7), 1564; https://doi.org/10.3390/s17071564

Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network

1,2
,
1,2,* and 1,2
1
The State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
2
The Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province, College of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
*
Author to whom correspondence should be addressed.
Received: 26 May 2017 / Revised: 29 June 2017 / Accepted: 30 June 2017 / Published: 4 July 2017
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [11985 KB, uploaded 4 July 2017]   |  

Abstract

Artificial intelligence (AI) techniques, which can effectively analyze massive amounts of fault data and automatically provide accurate diagnosis results, have been widely applied to fault diagnosis of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others. In this paper, a novel AI method based on a deep belief network (DBN) is proposed for the unsupervised fault diagnosis of a gear transmission chain, and the genetic algorithm is used to optimize the structural parameters of the network. Compared to the conventional AI methods, the proposed method can adaptively exploit robust features related to the faults by unsupervised feature learning, thus requires less prior knowledge about signal processing techniques and diagnostic expertise. Besides, it is more powerful at modelling complex structured data. The effectiveness of the proposed method is validated using datasets from rolling bearings and gearbox. To show the superiority of the proposed method, its performance is compared with two well-known classifiers, i.e., back propagation neural network (BPNN) and support vector machine (SVM). The fault classification accuracies are 99.26% for rolling bearings and 100% for gearbox when using the proposed method, which are much higher than that of the other two methods. View Full-Text
Keywords: deep belief networks; unsupervised feature learning; fault diagnosis; gear transmission chain deep belief networks; unsupervised feature learning; fault diagnosis; gear transmission chain
Figures

Figure 1

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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

He, J.; Yang, S.; Gan, C. Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network. Sensors 2017, 17, 1564.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top