An Efficient CNN with Tunable Input-Size for Bearing Fault Diagnosis

Authors

Jungan Chen^{1, *,} , Jean Jiang², Xinnian Guo³, Lizhe Tan⁴

¹College of Electronic and Computer Science, Zhejiang Wanli University, No. 8, South Qian Hu Road Ningbo, Zhejiang, 315100, China

²College of Technology, Purdue University Northwest, 2200 169th Street, Hammond, Indiana, 46323, USA

³Department of Electronic Information Engineering, Huaiyin Institute of Technology, No. 1, East Meicheng Road, Huaian, 223003, China

⁴Department of Electrical and Computer Engineering, Purdue University Northwest, 2200 169th Street, Hammond, Indiana, 46323, USA

^*Corresponding author. Email: friendcen21@hotmail.com

DOI

10.2991/ijcis.d.210113.001How to use a DOI?

Keywords

Bearing fault diagnosis; Deep learning; CNN; STFT

Abstract

Deep learning can automatically learn the complex features of input data and is recognized as an effective method for bearing fault diagnosis. Convolution neuron network (CNN) has been successfully used in image classification, and images of vibration signal or time-frequency information from short-time Fourier transform (STFT), wavelet transform (WT), and empirical mode decomposition (EMD) can be fed into CNN to achieve promising results. However, the CNN structure is complex and not efficient enough for different datasets. Furthermore, it is less efficient to process the input data by WT and EMD than by STFT. In this work, the low bound for input size of 2D data is analyzed by considering the relationship between the characteristic vibration frequencies and the window size of STFT to guide the determination of the minimum input size. Then a general adaptive CNN structure for different datasets is designed. According to the experimental results for four datasets, the proposed method is universal and the parameter settings can be guided by the low bound of input size. Surprisingly, all classification accuracies for the four datasets can achieve 100% in ten times of independent run without redesigning the CNN structure.

Copyright

© 2021 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Jungan Chen
AU  - Jean Jiang
AU  - Xinnian Guo
AU  - Lizhe Tan
PY  - 2021
DA  - 2021/01/20
TI  - An Efficient CNN with Tunable Input-Size for Bearing Fault Diagnosis
JO  - International Journal of Computational Intelligence Systems
SP  - 625
EP  - 634
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.210113.001
DO  - 10.2991/ijcis.d.210113.001
ID  - Chen2021
ER  -