Next Article in Journal
Ensuring the Reliability of Pneumatic Classification Process for Granular Material in a Rhomb-Shaped Apparatus
Previous Article in Journal
Experimental Study on Mechanical Properties and Fractal Dimension of Pore Structure of Basalt–Polypropylene Fiber-Reinforced Concrete
Previous Article in Special Issue
Structural Methodologies for Distributed Fault Detection and Isolation
Article Menu

Article Versions

Export Article

Open AccessArticle
Appl. Sci. 2019, 9(8), 1603; https://doi.org/10.3390/app9081603

A Fault Diagnosis Approach for Rolling Bearing Based on Convolutional Neural Network and Nuisance Attribute Projection under Various Speed Conditions

College of Energy & Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
*
Author to whom correspondence should be addressed.
Received: 6 March 2019 / Revised: 22 March 2019 / Accepted: 11 April 2019 / Published: 17 April 2019
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
PDF [1278 KB, uploaded 17 April 2019]
  |  

Abstract

Intelligent fault diagnosis is a promising tool for processing mechanical big data. It can quickly and efficiently process the collected signals and provide accurate diagnosis results. However, rotating machinery often works under various speed conditions, which makes it difficult to extract fault features. Inspired by speech recognition, the nuisance attribute projection method in speech recognition is introduced into fault diagnosis to solve the problem of feature extraction in variable speed signals. Based on the idea of unsupervised feature learning, the loss function of nuisance attribute projection is added to the loss function of convolutional neural network (CNN) to learn fault features from original data. Health status is classified according to the learned characteristics and projection matrix P. A special designed bearing dataset is employed to verify the effectiveness of the proposed method. The results show that the proposed method has a higher accuracy and a simpler framework, which is superior to the existing methods in bearing fault diagnosis.
Keywords: rolling bearing; fault diagnosis; variable speed condition; convolutional neural network; nuisance attribute projection rolling bearing; fault diagnosis; variable speed condition; convolutional neural network; nuisance attribute projection
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

Ma, H.; Li, S.; An, Z. A Fault Diagnosis Approach for Rolling Bearing Based on Convolutional Neural Network and Nuisance Attribute Projection under Various Speed Conditions. Appl. Sci. 2019, 9, 1603.

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