Bearing fault diagnosis under variable rotational speeds using Stockwell transform-based vibration imaging and transfer learning.

Hasan, Md. Junayed; Kim, Jong-Myon

Abstract

In this paper, discrete orthonormal Stockwell transform (DOST)-based vibration imaging is proposed as a preprocessing step for supporting load and rotational speed invariant scenarios for signals of various health conditions. For any health condition, features can easily be extracted from its generated health pattern. To automate the feature selection process, a convolutional neural network (CNN)-based transfer learning (TL) approach for diagnosis has also been introduced. Transfer learning allows an established model to use feature knowledge obtained under one set of working conditions through hidden layers to diagnose faults that occur under other working conditions. The network learns from the massive source dataset, and that knowledge is applied to the target data to identify faults. Using the bearing dataset of Case Western Reserve University, the proposed approach yields an average 99.8% classification accuracy and, specifically, 99.99% for healthy condition (HC), 99.95% for inner race fault (IRF), 99.96% for ball fault (BF), 99.68% for outer race fault for 12 o'clock sensor position (ORF@12), 99.93% for outer race fault for 3 o'clock sensor position (ORF@3), and 99.89% for outer race fault for 6 o'clock sensor position (ORF@6). In this paper, the proposed approach is compared with conventional artificial neural networks (ANNs), support vector machines (SVMs), hierarchical CNNs, and deep autoencoders. The proposed approach outperforms these conventional methods in the accuracy under all working conditions.

Citation

HASAN, M.J. and KIM, J.-M. 2018. Bearing fault diagnosis under variable rotational speeds using Stockwell transform-based vibration imaging and transfer learning. Applied sciences [online], 8(12): fault detection and diagnosis in mechatronics systems, article 2357. Available from: https://doi.org/10.3390/app8122357

• Journal Article Type: Article
• Acceptance Date: Nov 19, 2018
• Online Publication Date: Nov 22, 2018
• Publication Date: Dec 31, 2018
• Deposit Date: May 12, 2022
• Publicly Available Date: May 30, 2022
• Journal: Applied Sciences (Switzerland)
• Electronic ISSN: 2076-3417
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 8
• Issue: 12
• Article Number: 2357
• DOI: https://doi.org/10.3390/app8122357
• Keywords: Stockwell transform; Vibration imaging; Fault diagnosis; Transfer learning; Neural network; Vibration signals
• Public URL: https://rgu-repository.worktribe.com/output/1664350