Research on feature fusion of intelligent power plant equipment fault data based on improved decision tree algorithm
If the collection and extraction process of power plant equipment fault data is affected by environmental noise,it is eas-y to reduce the accuracy of data feature fusion.To address this issue,a smart power plant equipment fault data feature fusion technol-ogy based on improved decision tree algorithm is proposed.Firstly,use multi-source sensor data to collect abnormal data during the operation of power equipment,and collect real-time fault information between devices to establish a device fault dataset;Then,using semi soft threshold function technology,the device dataset is denoised,and the denoised device data is classified using an improved decision tree algorithm.Based on the classified electrical equipment fault data,the features of the electrical equipment fault data are extracted;Finally,a composite judgment model is constructed to achieve fusion processing of data features for equipment faults in smart power plants.The experimental results indicate that the intelligent power plant equipment fault data feature fusion technology designed in this study can significantly improve the accuracy of equipment fault data feature fusion.
improve the decision tree algorithmsmart power plant equipmentfault data feature fusionsemi soft threshold functionnoise reduction processingmulti source sensors
万方数据
维普
