| Rolling bearings are the most common and important general components in all kinds of machinery, whose working condition can directly affect the entire producing system's efficiency and safety. That's why research on fault diagosis for rolling bearings is always the key technology of both domestic and overseas engineering field. The key of fault diagnosis for rolling bearing lies in the acquisition of fault feature frequency. When rolling bearing is running with loads, the damage point on component element's surface will strike other components in the bearing which come into contact with it peariodically. That will produde low-frequency vibrations, whose frequency is so called fault feature frequency. In order to extract fault feature, we usually use acceleration sensors to capture vibration signal of bearings. The signal caused by the vibration of rolling bearings is often non-smooth and transilient. Traditional Fourier Analysis is not capable of processing this kind of signals.Walet analysis is a rapidly developing curriculum in recent years, whis is widely used in the field of signal analysis and processing. Wavelet has adaptive time-frequency quality, which makes it suitable to process the faulty signal of rolling bearings. Wavelet packets is based on the wavelet analysis. It provides a more elaborate ability of decomposing signal than wavelet, in the meantime, it maintains the good time-frequency quality of wavelet. Using MATLAB, this paper analyse the fault signals of outer rings an rollers of bearings respectively, which justifies the effectivity of wavelet and wavelet packets being applied in the fault feature extraction of rolling bearings.Resonant demodulation is widely used in engineering for fault diagnosis of roling bearings. Its basic idea is that: using band-pass filter to separate the high-frequency natural vibration of rollings first, then applying Fourier Transform on the envelope of the high-frequency signal, thus we can acquire the fault feature of rolling bearings. This paper puts forward a new way of diagnosing the breakdown of rolling bearings, which combines wavelet analysis and resonance demodulation method. The author uses YVS-2 multi-funcion vibrating table to capture the fault signals of rolling bearings, then apply the new method to extract fault feature of outer ring and rollers respectively, and the final result is good. This proves that the new diagnostic method has application value in some degree. |
