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

  • Fan Xu
  • Peter W Tse
Methodologies and Application


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


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



Denoising stacked auto-encoder


Stacked auto-encoder


Support vector machine


Random forest


Artificial neural networks








Wavelet transform


Fast Fourier transform


Empirical mode decomposition


Ensemble empirical mode decomposition


Fuzzy entropy


Clustering fast searching




Ball fault


Inner race fault


Outer race fault


Standard deviation




Denoising auto-encoder



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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