Study On Fault Diagnosis Of Mine Main Fan Based On Fusion Of FPSO-BPNN And DBN

The mine main fan sends the ground fresh air underground to reduce the concentration of harmful gas underground,which provides an important guarantee for the safe production of on-site operators.Once the fan fails,the air will stop,which will endanger the life safety of underground workers.Therefore,the fault diagnosis of mine main fan is of great significance.Taking the main fan of coal mine as the research object,this paper analyzes the vibration mechanism of six common faults such as fan rotor imbalance.Taking the vibration signal as the starting point,a fault diagnosis method of main fan based on vibration data drive is proposed.This method extracts the relevant characteristics of vibration signal and inputs it into the classifier to identify the running state of fan.When extracting the features of vibration signals,aiming at the problem of insufficient mining of signal features by single domain features,a method of multi domain feature extraction and dimension reduction fusion of vibration signals of main fan is proposed:extract the time domain features,frequency domain features and IMF energy features of vibration signals,obtain the corresponding comprehensive high viterbilt collection,and then screen the features based on the feature evaluation mechanism of standard deviation within and between classes,eliminate the features that are invalid to the classification and have no obvious effect,and screen out the high-efficiency features such as kurtosis and center of gravity frequency.Finally,the kernel principal component analysis is used to reduce the 7-dimensional high-efficiency feature set to the 3-dimensional fusion feature set,so as to eliminate the redundancy between features.The three-dimensional fusion features are input into BPNN and deep confidence network(DBN)respectively to establish the fault diagnosis model of main fan.Among them,aiming at the problem that the traditional BPNN falls into the local optimal solution and the slow convergence speed leads to the low accuracy of diagnosis results and long diagnosis time,the fractional particle algorithm(FPSO)is used to optimize the weight and threshold of BPNN,which not only solves the problem of BPNN falling into the local optimal solution,but also improves the accuracy of fault diagnosis and reduces the diagnosis time.On this basis,the output results of FPSO-BPNN and DBN fault diagnosis models are fused by D-S evidence theory to obtain the final diagnosis results.The simulation results show that the final diagnosis accuracy is as high as 98.63%,which is about 5%higher than that of FPSO-BPNN and DBN models.Based on the simulation model,the fault diagnosis system of mine main fan is designed and tested,which can effectively identify the fault types of mine main fan,and has good theoretical research value and engineering practical value for promoting the safety production of intelligent coal mine.