Fault Identification of Machine Tool Spindle System Based on Attention Mechanism and Deep Learning Algorithm

doi:10.12382/bgxb.2021.0202

Abstract

Abstract: The overall dynamic degradation fault of the numerically-controlled machine tool spindle system with complex nonlinear characteristics is difficultly identified and investigated.An intelligent fault identification method based on attention mechanism and depth learning algorithm is proposed to stud the overall fault identification of spindle system by starting with data analysis.The proposed method is used to design the research framework of attention mechanism，and divide the research problems into global vertical large classification interval dimension and local horizontal fine-grained interval dimension.The gated recurrent unit model with reasoning average absolute error of 0.028 7 after training and tuning is used to identify the global degradation faults in large classification interval.The residual network model with strong robustness and identification accuracy of 99.7% is used to accurately identify the local fine-grained interval faults， based on sym8 wavelet basis adaptive soft threshold noise reduction.The results show that the proposed method is used to quantitatively identify the overall fault of spindle system. The proposed attention mechanism is used to effectively distinguish the faults that cannot be accurately identified in the large classification interval in the fine-grained interval，and the data growth gradient in the category increases from 6.6% to 43.8%. The effectiveness and accuracy of the proposed method are verified by studying the typical faults，such as misalignment and local resonance encountered in the actual use of the machine tool spindle system under no-load，and the fault identification under loading.

Key words: machinetoolspindlesystem, faultidentification, attentionmechanisms, gatedrecurrentunitmodel, ResNetmodel

CLC Number:

TG519.1

WANG Weiping, WANG Qi, YU Yang. Fault Identification of Machine Tool Spindle System Based on Attention Mechanism and Deep Learning Algorithm[J]. Acta Armamentarii, 2022, 43(4): 861-875.

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