Intelligent Classification Method for Equipment Fault Status Based on Multi Feature Fusion Analysis
The increase in equipment complexity and automation level has led to an increasing frequency and types of equipment failures,resulting in production interruptions,product quality degradation,and even safety accidents.In this context,a method for intelligent classification of equipment fault states based on multi feature fusion analysis is studied.By integrating multiple sensors and data acquisition devices,comprehensive monitoring and real-time data collection of device operating status can be achieved.Extract feature vectors with key value for fault classification from collected data and achieve multi feature fusion by assigning weights.On this basis,a classification model based on multi class support vector machine is constructed.By training and optimizing the model parameters,it can accurately identify and classify various complex equipment faults.The results showed that under the application of the research method,the vast majority of test samples overlapped with the actual fault categories,and a very small number were misclassified,thus proving the accuracy of the research method.
multi feature fusion analysisequipment malfunction statusmulti class support vector machineintelligent classification methodrunning state
万方数据
维普
