基于电压信号深度特征学习的谐波减速器健康状态识别

目前工业机器人谐波减速器健康状态识别多以振动信号为载体,需要外加测试系统,增加了数据获取难度及成本,且其准确性和有效性受传感器安装位置影响。基于此,提出基于电压信号深度特征学习的谐波减速器健康状态识别方法。利用工业机器人电机电压信号对谐波减速器健康状态进行表征,使用连续小波变换将电压信号转换成时频图以获得谐波减速器不同健康状态下电压信号的时频信息,构建出数据样本集。利用卷积神经网络对电压信号时频信息进行自学习,并有监督调整网络参数,在获得谐波减速器不同健康状态下电压信号深度特征的同时实现对其健康状态的识别。实验结果显示,所提方法识别准确率达到了90%以上,证明了该方法能够有效识别谐波减速器健康状态,并具有较好的泛化能力和稳健性。

Health state recognition of harmonic reducer based on depth feature learning of voltage signal

At present, the health state recognition of industrial robot harmonic reducer is mainly based on vibration signals, which requires additional test system, increases the difficulty and cost of data acquisition, and its accuracy and effectiveness are affected by the installation location of sensors. Based on this, the health state recognition method of harmonic reducer based on depth feature learning of voltage signal is proposed. The industrial robot motor voltage signal is used to characterize the health state of harmonic reducer, and the continuous wavelet transform is used to transform the voltage signal into time-frequency diagram to obtain the time-frequency information of voltage signal under different health state of harmonic reducer, and the data sample set is constructed. The convolutional neural network is used to self-learn the time-frequency information of the voltage signal, and the network parameters are supervised to adjust. In this way, the health state of harmonic reducer can be recognized while the depth characteristics of voltage signal under different health state of harmonic reducer are obtained. Experiment results show that the recognition accuracy of the proposed method reaches 90% above, which proves that the proposed method can effectively recognize the health state of harmonic reducer, and has good generalization ability and robustness.