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| Research on the Separation and Noise Reduction Method of Spindle Radial Rotation Error Based on LSTM-GRU Neural Network |
| CHI Yulun,LI Ximing,ZHU Wenbo,YU Jianhua |
| Research on the Separation and Noise Reduction Method of Spindle Radial Rotation Error Based on LSTM-GRU Neural Network |
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Abstract The frequency domain three-probe method is a common method for separating spindle rotation errors. Its error separation accuracy is greatly affected by the noise in the measured signal. Inappropriate noise reduction methods will distort the test results. To this end, a spindle rotation error separation and noise reduction method based on LSTM-GRU neural network is proposed.First, a test system is built using the optimized sensor angle by genetic algorithms and the spindle rotation error signal is acquired.Then, the Kalman filter is configured to reduce the noise of the three sensor signals, the synchronous rotation error and asynchronous rotation error are separated by the three-probe method in frequency domain.Finally, the LSTM-GRU model is used to reduce the noise of synchronous and asynchronous rotation error respectively.The noise reduction results of the LSTM-GRU model are compared with the results of LSTM-LSTM model、Kalman filtering and Wavelet threshold denoise methods.The Allan variance is calculated to evaluate the noise reduction effect of different methods.The experimental result shows that after noise reduction using the LSTM-GRU model, the Allan variance of the synchronous rotation error is 2.014×10-8mm2 and the Allan variance of the asynchronous rotation error is 3.967×10-8mm2, which are both less than the results of Kalman filtering and Wavelet threshold noise reduction.The noise reduction effect of the LSTM-GRU model is optimal.The asynchronous rotation error of the spindle at the test speed of 6000r/min is 2.42μm and the asynchronous rotation error is 3.21μm, which meets the actual situation.
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Received: 25 December 2023
Published: 29 November 2024
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2024, Vol. 45 
