The Research Of Vibration Fault Diagnosis For Shearer Based On Chaotic Oscillator And Wavelet Transform

In recent years,with the increase of mechanization and power complex of the structure,the failure of the shearer rate which is the leading mechanical in coal mining enterprises is increasing,and also directly affect the safety in production and economic benefit.Therefore,the effective fault diagnosis technology can not only reduce the unnecessary loss, and truly pre- maintenance.So, the research for fault signal analysis, detection and feature extraction is of great significance in the coal mining enterprises.The vibration fault of the gear and the bearing is researched in-depth which is mainly aimed at shearer cutting unit in this Master Thesis.The common weak signal detection method and the fault feature extraction theory are analyzed and compared, and the defects of their application in practical engineering are studied. Finally, a multinational fault diagnosis technology of the early fault weak amplitude of shearer under strong noise background detection based on the Duffing chaotic oscillator and fault feature extraction based on convolution type of wavelet are put forward.The research work been has carried out in this paper is as follows:Firstly,the basic structure of the shearer is introduced, the structure of driving part and motor is analyzed,and the vibration mechanism and fault characteristics of rolling bearings and gears are emphatically studied.Then, the selection, analysis and the comparison of the vibration fault signal of the coal mining machine are analyzed,and the advantages of the chaotic Duffing oscillator and the wavelet analysis method in the weak signal detection and feature extraction of the incipient fault vibration of coal mining machine are obtained,respectively.Secondly, a weak vibration signals detection model is established which is based on chaotic Duffing oscillator, and its detection principle is analyzed,that the chaotic criterion is concluded by Melnikov function.At the same time, the principle and method of detecting known and unknown vibration amplitudes is analyzed which is based on chaotic Duffing oscillator.The measuring value reached 10-7 between the true and detective value is concludedand through the simulation of Matlab Simulink,which is proved feasibility and effectiveness of vibration fault diagnosis for shearer.To furt her illustrate the advantage of the chaotic Duffing oscillator in the application of this article,the noise signal is added to the original Duffing oscillator model, through the analysis principle and simulation,we can obtain its immune performance for noise.Finally, the feature extraction method of vibration fault of coal mining machine based on convolution type of wavelet packet is proposed on the basis of basic wavelet transform, multi-resolution analysis and discrete convolution type wavelet transform,then the fault frequency information of bearing and gear is obtained through the simulation experiment,the existence and accuracy of fault detection is validated in further by comparing to normal and fault energy value distribution.