Research on Pick Loss Diagnosis Based on EEMD and AGA-SVM
Aiming at the difficulty of traditional methods to accurately identify the loss status of mining equipment picks in the working process,a coal mining based on the combination of ensemble empirical mode decomposition(EEMD)and adaptive genetic algorithm optimization support vector machine(AGA-SVM)is proposed Diagnosis method of pick loss degree of machine and roadheader.First,using EEMD to decompose the vibration and acoustic emission signals of the pick under different wear conditions to obtain the intrinsic mode function(IMF),and then input the IMF component as a feature vector into the AGA-SVM diagnostic device.Finally,the kernel function Optimize the parameters and penalty coefficients,and use the model proposed in this paper to classify the feature vectors.The results showed that this method can accurately diagnose the loss of shearer picks.Compared with SVM and GA-SVM,it has superior timeliness and accuracy.
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