Abstract：The operating condition of industrial robot harmonic reducer is repeated circularly, so it is difficult to describe its operating state only by a single sensor, and it will lead to high uncertainty of health state assessment results. To this end, a method of evaluating the health state of harmonic reducer with multi-sensor information fusion deeply is proposed. Firstly, continuous wavelet transform is applied to the vibration signal of harmonic reducer, and the time-frequency diagram is constructed to describe its operating characteristics. Then the image fusion based on wavelet transform is used to fuse the time-frequency information of multiple sensors to describe the running state of harmonic reducer comprehensively. Finally, the fused time-frequency diagram is automatically learned by using convolutional neural network to obtain the depth characteristics that can accurately represent the health state of harmonic reducer, and the health state assessment is realized by adding full connection layer at the end of the convolutional neural network. The feasibility and effectiveness of the proposed method are proved by the experiment of evaluating the health state of the harmonic reducer under different health state and different working time, and it has good generalization ability and robustness.
陈仁祥1,张勇1，胡小林2，杨黎霞1，陈才3，谢文举1. 多传感器信息深度融合的谐波减速器健康状态评估[J]. 振动与冲击, 2022, 41(7): 139-144.
CHEN Renxiang1, ZHANG Yong1, HU Xiaolin2, YANG Lixia1, CHEN Cai3, XIE Wenju1. Health state evaluation of harmonic reducer based on multi-sensor information deep fusion. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(7): 139-144.
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