Research On The Identification Methods Of Vibrosieve Abnormal Vibrations

The vibrosieve is one of the important sizing devices in coal washeries. Using the vibrations of sieve, it carries out coal dressing. It works on the vibrations, acoustic noise is loud, working atmosphere is bad, working time is long, so it is hard to avoid a good many faults. It's hard to find the faults when they occur, so that the faults go bad, finally that brings serious results. That makes us stop producing, refit the machine, also increase the maintenance cost of vibrosieve and influence the productive tempo and economic benefit. Therefore, the research on the identification methods of vibrosieve abnormal vibrations seems to have certain practical meanings and values. Through the identification of vibrosieve abnormal vibrations, find the faults as soon as possible, avoid the faults go bad, maintain the machine in time, go down the cost of maintenance. That is important to insure the producing.Through the analytical study of vibration data of normal and abnormal, the thesis has distinguish the fault signals from normal signals and finds the identification ways. Along the processing of the data, use the ways of corresponding processing. That gives guarantee to the validity of the data.First, do the processing of calibration transform and polynomial trend item elimination to the obtained data. Through the calibration transform, change the voltage signals into the acceleration signals that we needed. And through the polynomial trend item elimination, eliminate the problem that the signals deviate the baseline.Second, do the denoise processing by wavelet denoise. The noise signals always include in the high frequency minutiae. Through the wavelet dissociation to the signals, do the processing of wavelet coefficients using the threshold values, then do the processing of wavelet reorganization. Finally , that carries out signal denoise.Third, do the processing of the denoised signals in time domain and frequency domain. Using MATLAB, work out the virtual values of the signals, present the figures of time domain and power spectrum that belong to the denoised signals.Finally, using the virtual values, time domain figures and power spectrum figures of normal and abnormal signals on X, Y, Z axises in the four sensors that have processed, contrast them, then find the faults, and research the ways that distinguish the faults. At the end, present the corresponding advisable ways of vibrosieve fault diagnosis.