Design And Implementation Of SCARA Robot Fault Diagnosis System Based On Machine Learning

SCARA robot has been one of the important members in the industrial production automation,the abnormal operation status of SCARA robot caused by mechanical fault would have a major impact on the safety of industrial production.At present,the maintenance of the operating status of industrial robots still mainly adopts manual inspection methods,which is not only time-consuming and labor-intensive,but will also cause the stagnant of production line,increase the economic loss of enterprises,and even make new faults during the maintenance.In regard to this,a SCARA robot fault diagnosis system based on machine learning was designed to provide a new maintenance plan for equipment fault diagnosis of SCARA robots.The SCARA robot vibration signal of 152 samples in three operating states were obtained by using the data acquisition system in this research.Firstly,the signal was preprocessed sequentially by using wavelet threshold denoising,wavelet packet decomposition and signal reconstruction,finally obtained the envelope.Secondly,the wavelet packet-characteristic entropy of signal in each state was obtained by using the equal-energy segmentation method.And three traditional machine learning algorithms were used to build SCARA robot fault diagnosis model based on the proposed features:BP neural network,support vector machine(SVM),random forest(RF).The test dataset was used to evaluate the performance of the model,the SVM model performed the best,which diagnostic accuracy rate reached 91.7%.Based on the SVM model,genetic algorithm(GA)and particle swarm optimization(PSO)were used to optimize its parameters.The diagnostic accuracy of the SVM model increased from 91.7% to 100%after PSO optimization.Finally,the PSO-SVM model was selected as the final model of SCARA robot fault diagnosis,and a SCARA robot fault diagnosis system was designed combined with the PSO-SVM model,which realized the visualization and systematization of the system.The results have showed that the system could be applied to the field of mechanical faults of SCARA robots effectively,and put forward a practical and effective testing method for the maintenance and repair of SCARA robots.