Industrial Robot Anomaly Detection Based on IWOA-SVDD
Aiming at the non-stationarity of industrial robot operation data and the difficulty of signal feature extraction,a signal denoising method based on the combination of ensemble empirical mode decomposition and discrete wavelet decomposition is proposed.The denoising signal is extracted in time domain,and the support vector data(SVDD)parameters and features are optimized by the improved whale optimization algorithm(IWOA)to form a multi-objective optimization anomaly detection algorithm.First,the high-dimensional data is subjected to ensemble empirical mode decomposition,and the critical point between the pure modal component and the noise-containing mode is found according to the continuous mean square error,and the discrete wavelet is used to denoise the noise mode to reconstruct the signal and then extract the time domain feature.Then,the IWOA is used to optimize the multimodal features and SVDD kernel parameters.Then an anomaly detection model is constructed to detect abnormalities in signals such as feedback current and feedback torque during the operation of industrial robots.The results show that the model can effectively judge the abnormal situation of industrial robots,and the accuracy can reach 97％-99％,which is 4％-5％higher than other methods.
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