Next Article in Journal
Sintering of Two Viscoelastic Particles: A Computational Approach
Next Article in Special Issue
Detection of Eccentricity Faults in Five-Phase Ferrite-PM Assisted Synchronous Reluctance Machines
Previous Article in Journal
Advanced Emergency Braking Control Based on a Nonlinear Model Predictive Algorithm for Intelligent Vehicles
Previous Article in Special Issue
Numerical Control Machine Tool Fault Diagnosis Using Hybrid Stationary Subspace Analysis and Least Squares Support Vector Machine with a Single Sensor
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessFeature PaperArticle
Appl. Sci. 2017, 7(5), 515; doi:10.3390/app7050515

Detection of Pitting in Gears Using a Deep Sparse Autoencoder

1
School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
2
Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
3
College of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
*
Author to whom correspondence should be addressed.
Academic Editor: César M. A. Vasques
Received: 15 March 2017 / Revised: 5 May 2017 / Accepted: 12 May 2017 / Published: 16 May 2017
(This article belongs to the Special Issue Deep Learning Based Machine Fault Diagnosis and Prognosis)
View Full-Text   |   Download PDF [4238 KB, uploaded 16 May 2017]   |  

Abstract

In this paper; a new method for gear pitting fault detection is presented. The presented method is developed based on a deep sparse autoencoder. The method integrates dictionary learning in sparse coding into a stacked autoencoder network. Sparse coding with dictionary learning is viewed as an adaptive feature extraction method for machinery fault diagnosis. An autoencoder is an unsupervised machine learning technique. A stacked autoencoder network with multiple hidden layers is considered to be a deep learning network. The presented method uses a stacked autoencoder network to perform the dictionary learning in sparse coding and extract features from raw vibration data automatically. These features are then used to perform gear pitting fault detection. The presented method is validated with vibration data collected from gear tests with pitting faults in a gearbox test rig and compared with an existing deep learning-based approach. View Full-Text
Keywords: gear; pitting detection; deep sparse autoencoder; vibration; deep learning gear; pitting detection; deep sparse autoencoder; vibration; deep learning
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 alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Qu, Y.; He, M.; Deutsch, J.; He, D. Detection of Pitting in Gears Using a Deep Sparse Autoencoder. Appl. Sci. 2017, 7, 515.

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