Acoustic spectral imaging and transfer learning for reliable bearing fault diagnosis under variable speed conditions.

Hasan, Md. Junayed; Islam, M.M. Manjurul; Kim, Jong-Myon

Abstract

Incipient fault diagnosis of a bearing requires robust feature representation for an accurate condition-based monitoring system. Existing fault diagnosis schemes are mostly confined to manual features and traditional machine learning approaches such as artificial neural networks (ANN) and support vector machines (SVM). These handcrafted features require substantial human expertise and domain knowledge. In addition, these feature characteristics vary with the bearing's rotational speed. Thus, such methods do not yield the best results under variable speed conditions. To address this issue, this paper presents a reliable fault diagnosis scheme based on acoustic spectral imaging (ASI) of acoustic emission (AE) signals as a precise health state. These health states are further utilized with transfer learning, which is a machine learning technique, which shares knowledge with convolutional neural networks (CNN) for accurate diagnosis under variable operating conditions. In ASI, the amplitudes of the spectral components of the windowed time-domain acoustic emission signal are transformed into spectrum imaging. ASI provides a visual representation of acoustic emission spectral features in images. This ensures enhanced spectral images for transfer learning (TL) testing and training, and thus provides a robust classifier technique with high diagnostic accuracy.

Citation

HASAN, M.J., ISLAM, M.M.M. and KIM, J.-M. 2019. Acoustic spectral imaging and transfer learning for reliable bearing fault diagnosis under variable speed conditions. Measurement [online], 138, pages 620-631. Available from: https://doi.org/10.1016/j.measurement.2019.02.075

• Journal Article Type: Article
• Acceptance Date: Feb 26, 2019
• Publication Date: May 31, 2019
• Deposit Date: May 12, 2022
• Publicly Available Date: Jun 28, 2022
• Journal: Measurement: Journal of the International Measurement Confederation
• Print ISSN: 0263-2241
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 138
• Pages: 620-631
• DOI: https://doi.org/10.1016/j.measurement.2019.02.075
• Keywords: Acoustic emission signal; Spectrum imaging; Feature extraction and classification; Fault diagnosis; Convolution neural network; Transfer learning
• Public URL: https://rgu-repository.worktribe.com/output/1664345