A multitask-aided transfer learning-based diagnostic framework for bearings under inconsistent working conditions.

Hasan, Md. Junayed; Sohaib, Muhammad; Kim, Jong-Myon

Abstract

Rolling element bearings are a vital part of rotating machines and their sudden failure can result in huge economic losses as well as physical causalities. Popular bearing fault diagnosis techniques include statistical feature analysis of time, frequency, or time-frequency domain data. These engineered features are susceptible to variations under inconsistent machine operation due to the non-stationary, non-linear, and complex nature of the recorded vibration signals. To address these issues, numerous deep learning-based frameworks have been proposed in the literature. However, the logical reasoning behind crack severities and the longer training times needed to identify multiple health characteristics at the same time still pose challenges. Therefore, in this work, a diagnosis framework is proposed that uses higher-order spectral analysis and multitask learning (MTL), while also incorporating transfer learning (TL). The idea is to first preprocess the vibration signals recorded from a bearing to look for distinct patterns for a given fault type under inconsistent working conditions, e.g., variable motor speeds and loads, multiple crack severities, compound faults, and ample noise. Later, these bispectra are provided as an input to the proposed MTL-based convolutional neural network (CNN) to identify the speed and the health conditions, simultaneously. Finally, the TL-based approach is adopted to identify bearing faults in the presence of multiple crack severities. The proposed diagnostic framework is evaluated on several datasets and the experimental results are compared with several state-of-the-art diagnostic techniques to validate the superiority of the proposed model under inconsistent working conditions.

Citation

HASAN, M.J., SOHAIB, M. and KIM, J.-M. 2020. A multitask-aided transfer learning-based diagnostic framework for bearings under inconsistent working conditions. Sensors [online], 20(24): deep learning, artificial neural networks and sensors for fault diagnosis, article 7205. Available from: https://doi.org/10.3390/s20247205

• Journal Article Type: Article
• Acceptance Date: Dec 15, 2020
• Online Publication Date: Dec 16, 2020
• Publication Date: Dec 31, 2020
• Deposit Date: May 13, 2022
• Publicly Available Date: May 30, 2022
• Journal: Sensors (Switzerland)
• Print ISSN: 1424-8220
• Electronic ISSN: 1424-8220
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 20
• Issue: 24
• Article Number: 7205
• DOI: https://doi.org/10.3390/s20247205
• Keywords: Bearing; Bispectrum; Convolution neural network; Fault diagnosis; Multitask learning; Transfer learning
• Public URL: https://rgu-repository.worktribe.com/output/1664549