Abstract
Convolutional neural network (CNN) is increasingly applied to data-driven fault diagnosis of mechanical equipment spare parts. However, CNN training network parameters need a large amount of fault data, and better training effect can be obtained by updating network parameters repeatedly. In this paper, an improved CNN by multiwavelets is introduced multiwavelets into convolution layer, the natural convolution attribute of multiwavelets is fused with convolution layer to fully release the two-channel feature extraction ability of multiwavelets transform. At the same time, we change the parameters of the multiwavelets convolution kernel to discuss the overall diagnostic performance of the network in the same dataset. Thus, different multiwavelets kernel parameters are customized according to different signal characteristics. The feasibility and effectiveness of improved CNN by multiwavelets for case Western Reserve University fault-bearing data are verified.
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Acknowledgements
This research is sponsored by the National Natural Science Foundations of China (No. 51975377 and 52005335), Shanghai Sailing Program (No. 21YF1430600).
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Ren, G., Yuan, J., Su, F., Jiang, H., Zhao, Q. (2022). An Improved Convolutional Neural Network Model by Multiwavelets for Rolling Bearing Fault Diagnosis. In: Zhang, H., et al. Neural Computing for Advanced Applications. NCAA 2022. Communications in Computer and Information Science, vol 1637. Springer, Singapore. https://doi.org/10.1007/978-981-19-6142-7_32
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DOI: https://doi.org/10.1007/978-981-19-6142-7_32
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