Next Article in Journal
Effect of Coil Width on Deformed Shape and Processing Efficiency during Ship Hull Forming by Induction Heating
Previous Article in Journal
Planning Lung Radiotherapy Incorporating Motion Freeze PET/CT Imaging
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Appl. Sci. 2018, 8(9), 1584; https://doi.org/10.3390/app8091584

End-To-End Convolutional Neural Network Model for Gear Fault Diagnosis Based on Sound Signals

1
School of Mechanical Engineering, Guizhou University, Guiyang 550025, China
2
Guizhou Provincial Key Laboratory of Internet Collaborative Intelligent Manufacturing, Guizhou University, Guiyang 550025, China
3
Key Laboratory of Advanced Manufacturing Technology of Ministry of Education, Guizhou University, Guiyang 550025, China
4
Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA
5
National Institute of Measurement and Testing Technology, Chengdu 610021, China
*
Authors to whom correspondence should be addressed.
Received: 15 August 2018 / Revised: 24 August 2018 / Accepted: 30 August 2018 / Published: 7 September 2018
(This article belongs to the Section Environmental and Sustainable Science and Technology)
Full-Text   |   PDF [8724 KB, uploaded 7 September 2018]   |  

Abstract

Currently gear fault diagnosis is mainly based on vibration signals with a few studies on acoustic signal analysis. However, vibration signal acquisition is limited by its contact measuring while traditional acoustic-based gear fault diagnosis relies heavily on prior knowledge of signal processing techniques and diagnostic expertise. In this paper, a novel deep learning-based gear fault diagnosis method is proposed based on sound signal analysis. By establishing an end-to-end convolutional neural network (CNN), the time and frequency domain signals can be fed into the model as raw signals without feature engineering. Moreover, multi-channel information from different microphones can also be fused by CNN channels without using an extra fusion algorithm. Our experiment results show that our method achieved much better performance on gear fault diagnosis compared with other traditional gear fault diagnosis methods involving feature engineering. A publicly available sound signal dataset for gear fault diagnosis is also released and can be downloaded as instructed in the conclusion section. View Full-Text
Keywords: gear fault diagnosis; acoustic-base diagnosis; deep learning; convolutional neural network; data fusion gear fault diagnosis; acoustic-base diagnosis; deep learning; convolutional neural network; data fusion
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

Yao, Y.; Wang, H.; Li, S.; Liu, Z.; Gui, G.; Dan, Y.; Hu, J. End-To-End Convolutional Neural Network Model for Gear Fault Diagnosis Based on Sound Signals. Appl. Sci. 2018, 8, 1584.

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]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top