• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Fault Diagnosis of Bearing Based on Convolutional Neural Network Using Multi-Domain Features

Vol. 15, No. 5, May 31, 2021
10.3837/tiis.2021.05.002, Download Paper (Free):

Abstract

Failures frequently occurred in manufacturing machines due to complex and changeable manufacturing environments, increasing the downtime and maintenance costs. This manuscript develops a novel deep learning-based method named Multi-Domain Convolutional Neural Network (MDCNN) to deal with this challenging task with vibration signals. The proposed MDCNN consists of time-domain, frequency-domain, and statistical-domain feature channels. The Time-domain channel is to model the hidden patterns of signals in the time domain. The frequency-domain channel uses Discrete Wavelet Transformation (DWT) to obtain the rich feature representations of signals in the frequency domain. The statistic-domain channel contains six statistical variables, which is to reflect the signals’ macro statistical-domain features, respectively. Firstly, in the proposed MDCNN, time-domain and frequency-domain channels are processed by CNN individually with various filters. Secondly, the CNN extracted features from time, and frequency domains are merged as time-frequency features. Lastly, time-frequency domain features are fused with six statistical variables as the comprehensive features for identifying the fault. Thereby, the proposed method could make full use of those three domain-features for fault diagnosis while keeping high distinguishability due to CNN's utilization. The authors designed massive experiments with 10-folder cross-validation technology to validate the proposed method's effectiveness on the CWRU bearing data set. The experimental results are calculated by ten-time averaged accuracy. They have confirmed that the proposed MDCNN could intelligently, accurately, and timely detect the fault under the complex manufacturing environments, whose accuracy is nearly 100%.


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Cite this article

[IEEE Style]
X. Shao, L. Wang, C. S. Kim, I. Ra, "Fault Diagnosis of Bearing Based on Convolutional Neural Network Using Multi-Domain Features," KSII Transactions on Internet and Information Systems, vol. 15, no. 5, pp. 1610-1629, 2021. DOI: 10.3837/tiis.2021.05.002.

[ACM Style]
Xiaorui Shao, Lijiang Wang, Chang Soo Kim, and Ilkyeun Ra. 2021. Fault Diagnosis of Bearing Based on Convolutional Neural Network Using Multi-Domain Features. KSII Transactions on Internet and Information Systems, 15, 5, (2021), 1610-1629. DOI: 10.3837/tiis.2021.05.002.

[BibTeX Style]
@article{tiis:24634, title="Fault Diagnosis of Bearing Based on Convolutional Neural Network Using Multi-Domain Features", author="Xiaorui Shao and Lijiang Wang and Chang Soo Kim and Ilkyeun Ra and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2021.05.002}, volume={15}, number={5}, year="2021", month={May}, pages={1610-1629}}