Research on Equipment Fault Prediction of Crusher Based on Improved Ant Colony Algorithm
The characteristic values of faults generated by crushers in harsh environments are difficult to extract,re-sulting in low fault prediction rates and high misjudgment rates.Based on this,an improved ant colony algorithm prediction method is proposed.First,collect basic data and raw data such as vibration signals,temperature,and pres-sure.Second,integrate and classify data,remove noise,fill in missing values,screen for outliers,use normalization processing,and perform data transformation,use standard deviation to characterize the basic characteristic values of data.Finally,combined with the improved ant colony algorithm,a fault prediction and handling model for crusher equipment is designed.Through experiments,the average prediction misjudgment rate of this method is 12.6%.Therefore,the design method effectively improves the fault prediction rate and reduces the misjudgment rate.
Improved ant colony algorithmEquipment sensingFault predictionAbnormal data collection
万方数据
维普
