Research On Fault Classification And Location Of Mine Hoist

The safe and reliable operation of mine hoist is the key to ensure the safe production of coal mine enterprises.In order to ensure its normal work,it is necessary to carry out real-time monitoring and status assessment,so as to timely and accurately judge the type and point of failure of equipment,so that on-site operation and maintenance personnel can quickly and accurately find and solve the fault.Therefore,this paper proposes and designs a desktop computer fault accurate location system based on MFCC and improved MUSIC,which is of great significance to improve the stability and reliability of hoist equipment operation.By combining the common faults of mine hoist,The overall scheme of the system is analyzed and designed.Secondly,in view of various interference signals existing in complex mine environment,spectral subtraction is adopted for noise reduction processing of mine hoist audio signals monitored,effectively avoiding the influence of environmental noise on it.Then,a series of characteristic parameters were obtained by the combination of Meir frequency cepstrum coefficient(MFCC)and Meir frequency cepstrum coefficient first order difference(ΔMFCC).The model was identified and trained by support vector machine(SVM),and the parameters in the model were optimized by introducing adaptive particle swarm optimization algorithm(APSO).Greatly improve the accuracy of fault judgment of hoist.In addition,in view of the low positioning accuracy of traditional MUSIC fault location algorithm,this paper proposes and designs a fault location algorithm based on SMN-MUSIC.By comparing the improved MUSIC fault location algorithm before and after the experiment,the results show that SMN-MUSIC algorithm has a small amount of computation,low error value,and higher positioning accuracy.Therefore,this paper uses SMN-MUSIC algorithm to locate the fault signal of hoist equipment.On the basis of the above research,this paper takes the Zhangji coal mine hoist of Huainan Mining Group as the research object to test and verify the function of the system.The results show that the combination of MFCC+ΔMFCC method can better extract different audio signal features,and APSO can improve the accuracy of fault classification in SVM searching network.The improved MUSIC algorithm significantly improves the accuracy of fault location of hoist.The performance of the designed elevator fault accurate location system based on MFCC and improved MUSIC can meet the requirements,and can realize the elevator fault accurate location,has theoretical and practical highly value.Fig.[52] Table [13] Reference [81]...