Adaptive online detection on dynamic characteristics of arc welding power supply based on complicated dimensionality reduction of correlation and time consumption

A complicated dimensionality reduction of correlation and the time consumption was put forward,which realized the adaptive online detection on different dynamic characteristics of arc welding power supply.The main idea of this method was to select some certain features from the complete feature set which were the closest to the detection targets.The correlation among features and the efficiency of the online detection were fully taken into account in the use of this method.A perfect welding data collection platform was set up,the samples of the voltage and current data were collected in the 189 welding processes,and the artificial evaluation results were taken as the teacher's signals,namely the cluster labels,in the dimension reduction.150 samples were randomly selected as training set,while the remaining 39 samples were used as the test suite.The results of the experiments showed that the automatic evaluation accuracy of the chosen one reached 97.435 9% and satisfied the application requirements when the optimal feature subset was chosen based on the dimension reduction method.