Abstract
In a manufacturing industry, there is a lot of rotating machinery used and rolling bearing is an essential part of rotating machinery. Fault occurring probability in rolling bearing is very high, so this is necessary to predict fault to reduce various losses. Fault diagnosis methods using the concept of deep learning (DL) require less human expertise than traditional fault diagnosis, and it also has automatic feature extracting capability. If we want to train our DL model from scratch, it requires numerous fault signals and long training time for better results. But in practical condition, available fault data is small. Therefore, the limitation DL for small dataset has been solved using pre-trained DL models. The pre-trained AlexNet and analytic Morlet wavelet function based scalogram have been used in the proposed algorithm. The performance of the algorithm has been tested on the different size dataset. Also, its performance has been compared with other existing methods.
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Sharma, P., Amhia, H., Sharma, S.D. (2022). Transfer Learning-Based Model for Rolling Bearing Fault Classification Using CWT-Based Scalograms. In: Pandian, A.P., Palanisamy, R., Narayanan, M., Senjyu, T. (eds) Proceedings of Third International Conference on Intelligent Computing, Information and Control Systems. Advances in Intelligent Systems and Computing, vol 1415. Springer, Singapore. https://doi.org/10.1007/978-981-16-7330-6_43
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DOI: https://doi.org/10.1007/978-981-16-7330-6_43
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