Abstract:
Vibration monitoring has been a reliable source of information for machine fault diagnosis. Several methods are available for bearing fault diagnosis under constant speed condition. A fault diagnosis framework consisting of a novel pre-processing tool, named cumulative distribution sharpness (CDS) profiling is proposed for variable speed conditions. We first provide evidences suggesting that the bearing fault signals follow Laplace distribution. Under the influence of Gaussian noise, however, the sharpness of the distribution decreases. This is helpful in separating the periodic fault and noise regions in the time-domain vibration signal by calculating local CDS values. The proposed CDS profiling (CDSP) is thus obtained by sweeping a window over the vibration signal and estimating the sharpness of cumulative distribution of such windowed signal. For changing noise variance, the monotonous and continuous nature of CDS is ensured to obtain a profile that retains the fault periodicity. A short-time Fourier transform of the CDSP is then calculated, followed by multiple time-frequency curves extraction (MTFCE). Two important fault features - Prominence and Compliance - are proposed in this paper. Finally, a fuzzy inference system is used to obtain diagnosis percentages from the features. Thus, the proposed method can classify the faults into healthy, inner race and outer race faults. The results are then validated on experimental data with variable operating speed. We show that prominence of the fault characteristic frequency improves due to CDSP. The accuracy of the proposed method is found to be better than the benchmark method of MTFCE.