基于小波包能量谱的工业机器人智能故障诊断

实际工业机器人在恶劣工作环境中易出现故障,传统的故障诊断大多都是通过振动信号进行,但是振动数据在实际工厂难以采集,给工业机器人的故障诊断造成了极大困扰。针对这一问题,提出一种基于小波包能量谱(WPES)与卷积神经网络(CNN)的工业机器人电流数据的智能故障诊断模型。该模型通过小波包将原始电流信号分解为多个子频带,计算每个子频带对应的能量特征,当工业机器人出现故障时,能量特征会发生变化,并将能量谱特征转化为二维矩阵用于设计、训练和测试所提出的模型。实验结果表明:采用WPES-CNN模型进行故障诊断,故障识别率达到了99.9%以上。

Intelligent Fault Diagnosis of Industrial Robot Based on Wavelet Packet Energy Spectrum

Actual industrial robots are prone to failures in harsh working environments.Traditionally,vibration signals are used for fault diagnosis.However,vibration data is difficult to collect in actual factories,which causes great trouble in the fault diagnosis of industrial robots.To solve this problem,an intelligent fault diagnosis model of industrial robots current data based on wavelet packet energy spectrum (WPES) and convolutional neural network (CNN) was proposed.The original current signal was decomposed into multiple sub-bands by using wavelet packets,and the corresponding energy characteristics of each sub-band were calculated.When an industrial robot failed,the energy characteristics would change,and the energy spectrum characteristics were converted into a two-dimensional matrix for the design,training and testing of the proposed model.The experimental results show that using WPES-CNN model for fault diagnosis,the fault recognition rate reaches over 99.9%.