Research on residual life prediction of rolling bearings based on STFT-CNN

Qi Liu; Sunqing Xu; Xiangyu Cao

doi:10.1117/12.2637638

24 May 2022 Research on residual life prediction of rolling bearings based on STFT-CNN

Qi Liu, Sunqing Xu, Xiangyu Cao

Author Affiliations +

Proceedings Volume 12260, International Conference on Computer Application and Information Security (ICCAIS 2021); 1226012 (2022) https://doi.org/10.1117/12.2637638
Event: International Conference on Computer Application and Information Security (ICCAIS 2021), 2021, Wuhan, China

Abstract

Rolling bearings are one of the key components in rotating machinery equipment. It is of great significance to carry out state monitoring and residual life prediction for rolling bearings to strengthen service management of bearings and maximize the use value of bearings. In this paper, a bearing remaining life prediction model based on STFT-CNN was built. The STFT transform was performed on the original signal before the CNN model was input, and the one-dimensional time series signal was converted into the time-frequency domain. Finally, experimental verification was completed on the IEEE PHM 2012 dataset, and comparative experiments were conducted. Experimental results show that the residual life curve predicted by the STFT-CNN model is more accurate and fits the actual curve.

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Qi Liu, Sunqing Xu, and Xiangyu Cao "Research on residual life prediction of rolling bearings based on STFT-CNN", Proc. SPIE 12260, International Conference on Computer Application and Information Security (ICCAIS 2021), 1226012 (24 May 2022); https://doi.org/10.1117/12.2637638

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KEYWORDS

Denoising

Convolution

Fourier transforms

Convolutional neural networks

Time-frequency analysis

Failure analysis

Neural networks

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