Diagnosis of Bearing System using Minimum Variance Cepstrum

Various bearings are commonly used in rotating machines. The noise and vibration signals that can be obtained from the machines often convey the information of faults and these locations. Monitoring conditions for bearings have received considerable attention for many years, because the majority of problems in rotating machines are caused by faulty bearings. Thus failure alarm for the bearing system is often based on the detection of the onset of localized faults. Many methods are available for detecting faults in the bearing system. The majority of these methods assume that faults in bearings produce impulses. Impulse events can be attributed to bearing faults in the system. McFadden and Smith used the bandpass filter to filter the noise signal and then obtained the envelope by using the envelope detector. D. Ho and R. B Randall also tried envelope spectrum to detect faults in the bearing system, but it is very difficult to find resonant frequency in the noisy environments. S. -K. Lee and P. R. White used improved ANC (adaptive noise cancellation) to find faults. The basic idea of this technique is to remove the noise from the measured vibration signal, but they are not able to show the theoretical foundation of the proposed algorithms. Y.-H. Kim et al. used a moving window. This algorithm is quite powerful in the early detection of faults in a ball bearing system, but it is difficult to decide initial time and step size of the moving window. The early fault signal that is caused by microscopic cracks is commonly embedded in noise. Therefore, the success of detecting fault signal is completely determined by a method's ability to distinguish signal and noise. In 1969, Capon coined maximum likelihood (ML) spectra which estimate a mixed spectrum consisting of line spectrum, corresponding to a deterministic random process, plus arbitrary unknown continuous spectrum. The unique feature of these spectra is that it can detect sinusoidal signal from noise. Our idea essentially comes from this method. In this paper, a technique, which can detect impulse embedded in noise, is introduced. The theory of this technique is derived and the improved ability to detect the faults in a ball bearing system is demonstrated theoretically as well as experimentally