An Input Transformation with MFCCs and CNN Learning Based Robust Bearing Fault Diagnosis Method for Various Working Conditions


KIPS Transactions on Software and Data Engineering, Vol. 11, No. 4, pp. 179-188, Apr. 2022
https://doi.org/10.3745/KTSDE.2022.11.4.179,   PDF Download:
Keywords: Bearing Fault Diagnosis, Deep Learning, Distribution Difference, MFCCs, CNN
Abstract

There have been many successful researches on a bearing fault diagnosis based on Deep Learning, but there is still a critical issue of the data distribution difference between training data and test data from their different working conditions causing performance degradation in applying those methods to the machines in the field. As a solution, a data adaptation method has been proposed and showed a good result, but each and every approach is strictly limited to a specific applying scenario or presupposition, which makes it still difficult to be used as a real-world application. Therefore, in this study, we have proposed a method that, using a data transformation with MFCCs and a simple CNN architecture, can perform a robust diagnosis on a target domain data without an additional learning or tuning on the model generated from a source domain data and conducted an experiment and analysis on the proposed method with the CWRU bearing dataset, which is one of the representative datasests for bearing fault diagnosis. The experimental results showed that our method achieved an equal performance to those of transfer learning based methods and a better performance by at least 15% compared to that of an input transformation based baseline method.


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Cite this article
[IEEE Style]
Y. Seo, "An Input Transformation with MFCCs and CNN Learning Based Robust Bearing Fault Diagnosis Method for Various Working Conditions," KIPS Transactions on Software and Data Engineering, vol. 11, no. 4, pp. 179-188, 2022. DOI: https://doi.org/10.3745/KTSDE.2022.11.4.179.

[ACM Style]
Yangjin Seo. 2022. An Input Transformation with MFCCs and CNN Learning Based Robust Bearing Fault Diagnosis Method for Various Working Conditions. KIPS Transactions on Software and Data Engineering, 11, 4, (2022), 179-188. DOI: https://doi.org/10.3745/KTSDE.2022.11.4.179.