Research On Fault Diagnosis Method And Application For Rolling Element Bearing Based On Wavelet Analysis

In recent years, the technique of mechanical fault diagnosis, which plays an important role in national manufacture, is quickly developed in domestic and oversea, and is deeply implied in many fields. In this paper, characteristic information for faults are gained from rolling element bearing outside vibration signals by using wavelet translation analysis methods, on the basis of former achievements, and valid results are obtained.First, from the starting point of formation of vibration signals, the rolling element bearing instinct vibration, nonlinear stiffness of inner, outer race and rolling elements, and vibration caused by assemble error, and the characteristic of vibration of each fault. Analysis the demodulated resonance technique (DRT), the result shows that it is effective and feasible to diagnosis faults of bearing un-disassembly using surface vibration signal. Builds vibration signal model with single defect on each component and analysis fault characters of the vibration signal, gives theory base to rolling element bearing fault diagnosis.Then, analyzes and compares methods of processing vibration signal in used, involves pretreatment methods and secondary treatment methods. Here, secondary treatment methods are introduced in detail, include time domain signal statistics character, frequency analysis fast Fourier transform (FFT), and short time Fourier transform (STFT). Using wavelet method adapts to analyze non-linear bearing system and non-stationary signal from outside surface. Simply introduces wavelet theory, its satisfied condition, and continuous wavelet translation (CWT), discrete wavelet translation (DWT), wavelet packet translation (WPT) analysis, with simulated application to show their advantages. Also introduces several base wavelet in use and their characteristic, for the difference between wavelet translation and other signal translation methods is that its base function can be changed, one can choose the most suitable base wavelet function for analysis object.At last, based on rolling element bearing vibration theory model, builds simulated signal with single defect on outer race, inner race and rolling elements, uses WPT method to take out their characteristic frequency, and diagnosis their states, the results are correct. Next, analyze two real vibration signals with wavelet method. One is gearbox low-speed shaft near motor bearing. Comparing the DWT decomposed coefficient of inner race defect vibration signal and good bearing vibration signal, gets that inner race defect signal has greater energy in highfrequency band. Using WPT diagnosis method draws out the characteristic bearing defect frequency of inner race, and gets the conclusion that the bearing has inner defect. The other application is bearing in gearbox high-speed shaft near motor position. Using WPT diagnosis method gets the frequency of our race. Comparison of the bearing's early fault and late fault vibration signal decomposed results, proves that WPT diagnosis method used in incipient rolling element bearing fault is effective.