Research On Rolling Element Bearing Defect Diagnosis Based On The Multi-Scale Stochastic Resonance Spectrogram

Rolling elements count among the most important components of the machines in industry applications.The normal operation of the machinery system would be influenced if a bearing in the system is faulty,and the faulty bearing may also cause some unexpected and dangerous consequences.Therefore,precise condition monitoring and diagnosis are needed to discover early defects occurred on the bearings.Vibration analysis is generally used for this purpose,but it is difficult to identify incipient defects because of the background noise.The background noise often comes from the working environment as well as some other coupled machine components,which increases difficulty in recognizing the weak signal of incipient defects.Hence,the challenge of recognizing the bearing defect is to find a more effective and efficient way to enhance the weak and imperceptible defect information from the noisy background.In weak signal detection,stochastic resonance(SR)is a useful tool that can utilize the noise for weak periodic signal enhancement.Considering the non-stationary property of the defective bearing vibration signals,this thesis investigates the SR performance in the time—frequency distribution(TFD).In this study,a new method called Multi-Scale Stochastic Resonance Spectrogram(MSSRS)is proposed to improve the effectiveness in detecting weak signal of incipient defects.The main motivation of the new method is as follows:(1)the defect-induced transients mainly locate at a specific band in the TFD,thus only the noise in this band can actuate the SR effect;(2)the TFD at each frequency corresponds to an envelope modulated on a specific frequency,so there is a modulation system for each frequency scale in the TFD.The new method thus treats every scale of the TFD as a modulation system at a specific frequency.Since useful information is just contained in specific scales of the measured signal,the noise in different modulation systems will have different effectiveness in enhancing the defect information with SR technique.The experimental vibration data of the defective bearings have been analyzed by the new MSSRS method.As compared to classical SR method and the MSTSR method,it is clear to see that the proposed method shows more advantages in identifying the defective frequencies of the rolling element bearing.The proposed MSSRS method also shows a good potential to diagnose the mixed faults in the vibration signals.This thesis explored two ways of software implementation of the mechanical fault diagnosis algorithms.One way is to apply the algorithms on smartphone APP,the other way is to implement the algorithms on a server and visualize the diagnosis results on the web.These two ways of software implementation can diagnose the machine faults on-line and off-line respectively.