Research on remaining life prediction of machine tool spindle bearing combining neural network and weighted average de-noising method

Jizhuang Hui; Jian Huang; Fuqiang Zhang; Yaqian Zhang; Yuan Tian

doi:10.1117/12.2658824

23 November 2022 Research on remaining life prediction of machine tool spindle bearing combining neural network and weighted average de-noising method

Jizhuang Hui, Jian Huang, Fuqiang Zhang, Yaqian Zhang, Yuan Tian

Author Affiliations +

Proceedings Volume 12454, International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022); 124542L (2022) https://doi.org/10.1117/12.2658824
Event: International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022), 2022, Hohhot, China

Abstract

The prediction of the remaining useful life of machine tool spindle bearings contributes to the increase of the useful life of CNC machine tools. In order to solve the problems that traditional modelling method faces the complex process and low accuracy, a new method to predict the spindle bearing remaining useful life combining the neural network and weighted average de-nosing method is proposed in this paper. First, the method uses a convolutional neural network to extract features from the vibration signals of bearings; secondly, a gated cyclic unit is used to perform regression analysis on the extracted features to estimate the remaining useful life of the bearing; finally, the weighted average de-nosing method is used enhance the accuracy of the predictions. The effectiveness of the proposed method is verified by experimental data.

Citation Download Citation

Jizhuang Hui, Jian Huang, Fuqiang Zhang, Yaqian Zhang, and Yuan Tian "Research on remaining life prediction of machine tool spindle bearing combining neural network and weighted average de-noising method", Proc. SPIE 12454, International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022), 124542L (23 November 2022); https://doi.org/10.1117/12.2658824

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KEYWORDS

Denoising

Data modeling

Neural networks

Convolution

Process modeling

Statistical modeling

Failure analysis

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