Abstract
AbstractBearings are vital components in rotating machinery. Undetected bearing faults may result not only in financial loss, but also in the loss of lives. Hence, there exists an abundance of studies working on the early detection of bearing faults. The rising use of deep learning in recent years increased the number of imaging types/neural network architectures used for bearing fault classification, making it challenging to choose the most suitable 2-D imaging method and neural network. This study aims to address this challenge, by sharing the results of the training of eighteen imaging methods with four different networks using the same vibration data and training metrics. To further strengthen the results, the validation dataset size was taken as five times the training dataset size. The best results obtained is 99.89% accuracy by using Scattergram Filter Bank 1 as the image input, and ResNet-50 as the network for training. Prior to our work, Scattergram images have never been used for bearing fault classification. Ten out of 72 methods used in this work resulted in accuracies higher than 99.5%.
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Information Systems,Theoretical Computer Science,Software
Reference86 articles.
1. Abdelkader, R., Kaddour, A., & Derouiche, Z. (2018). Enhancement of rolling bearing fault diagnosis based on improvement of empirical mode decomposition denoising method. The International Journal of Advanced Manufacturing Technology, 97(5), 3099–3117.
2. Abed, S. T., Dallalbashi, Z. E., & Taha, F. A. (2012). Studying the effect of window type on power spectrum based on MatLab. Tikrit Journal of Engineering Sciences, 19(2), 63–70.
3. An, X., & Jiang, D. (2014). Bearing fault diagnosis of wind turbine based on intrinsic time-scale decomposition frequency spectrum. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 228(6), 558–566.
4. Andén, J., & Mallat, S. (2014). Deep scattering spectrum. IEEE Transactions on Signal Processing, 62(16), 4114–4128.
5. Antoni, J. (2006). The spectral kurtosis: A useful tool for characterising non-stationary signals. Mechanical Systems and Signal Processing, 20(2), 282–307.
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