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Article

A Multitask-Aided Transfer Learning-Based Diagnostic Framework for Bearings under Inconsistent Working Conditions

1
School of Computer Engineering and Information Technology, University of Ulsan, Ulsan 44610, Korea
2
Department of Computer Science, Lahore Garrison University, Lahore 54000, Pakistan
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(24), 7205; https://doi.org/10.3390/s20247205
Received: 22 November 2020 / Revised: 13 December 2020 / Accepted: 15 December 2020 / Published: 16 December 2020
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. View Full-Text
Keywords: bearing; bispectrum; convolution neural network; fault diagnosis; multitask learning; transfer learning bearing; bispectrum; convolution neural network; fault diagnosis; multitask learning; transfer learning
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MDPI and ACS Style

Hasan, M.J.; Sohaib, M.; Kim, J.-M. A Multitask-Aided Transfer Learning-Based Diagnostic Framework for Bearings under Inconsistent Working Conditions. Sensors 2020, 20, 7205. https://doi.org/10.3390/s20247205

AMA Style

Hasan MJ, Sohaib M, Kim J-M. A Multitask-Aided Transfer Learning-Based Diagnostic Framework for Bearings under Inconsistent Working Conditions. Sensors. 2020; 20(24):7205. https://doi.org/10.3390/s20247205

Chicago/Turabian Style

Hasan, Md J., Muhammad Sohaib, and Jong-Myon Kim. 2020. "A Multitask-Aided Transfer Learning-Based Diagnostic Framework for Bearings under Inconsistent Working Conditions" Sensors 20, no. 24: 7205. https://doi.org/10.3390/s20247205

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