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Automatic roller bearings fault diagnosis using DSAE in deep learning and CFS algorithm

  • Fan Xu
  • Peter W Tse
Methodologies and Application
  • 418 Downloads

Abstract

A method based on denoising stacked auto-encoder in deep learning and clustering fast searching for roller bearings fault diagnosis automatically is presented in this paper. Unlike traditional classification methods, such as support vector machine, clustering methods can identify the faults without data label. However, most popular clustering methods like fuzzy-c-mean, Gustafson–Kessel, and Gath–Geva methods are needed to preset the number of the cluster. Different from these clustering methods, clustering fast searching model can select the cluster center points according to the local density and distance from any two points automatically. This paper presents a method based on denoising stacked auto-encoder in deep learning for feature extraction and clustering fast searching algorithm for fault diagnosis automatically without principal components analysis. Firstly, the denoising stacked auto-encoder is deployed to extract the useful fault feature from the different roller bearings vibration signals. Secondly, in order to visualize the data, the denoising stacked auto-encoder model with several hidden layers is used to reduce the dimension of the extracted features, then the extracted features are regarded as the input of the clustering fast searching model for fulfilling the roller bearings fault diagnosis. The experimental results show that the performance of the presented method is superior to the other different combination models include sparse auto-encoder, ensemble empirical mode decomposition, fuzzy entropy, fuzzy-c-means, Gustafson–Kessel, and Gath–Geva

Keywords

Roller bearings Denoising stacked auto-encoder Clustering fast searching Deep learning Fault diagnosis 

Abbreviation

DSAE

Denoising stacked auto-encoder

SAE

Stacked auto-encoder

SVM

Support vector machine

RF

Random forest

ANN

Artificial neural networks

FCM

Fuzzy-c-means

GK

Gustafson–Kessel

GG

Gath–Geva

WT

Wavelet transform

FFT

Fast Fourier transform

EMD

Empirical mode decomposition

EEMD

Ensemble empirical mode decomposition

FE

Fuzzy entropy

CFS

Clustering fast searching

NR

Normal

BF

Ball fault

IRF

Inner race fault

ORF

Outer race fault

SD

Standard deviation

AE

Auto-encoder

DAE

Denoising auto-encoder

Notes

Acknowledgements

The work described in this paper is fully supported by a Grant from the Research Grants Council (Project No. CityU 11201315) and a Grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. [T32-101/15-R]).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Systems Engineering and Engineering ManagementCity University of Hong KongKowloonChina

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