부산대학교 도서관

Industrial motors are ubiquitous and used extensively in various industrial applications. The aim of increasing productivity, efficiency, lowering down, and mean time between failures while maintaining safety necessitates the continuous condition monitoring of abnormalities, faults in motors, and even predict them as early as possible to maintain their smooth functioning. In this article, we propose and experimentally demonstrate the use of millimeter-wave (mmW) radar for noncontact, nondestructive detection, characterization, and finally prediction of the probability of failure by interrogating its vibrating shaft at a given set of bearing conditions. Precisely, we obtain experimental raw 1-D time series data from mmW radar and transform them into time–frequency representations using wavelet transformation. Then, the features are extracted from the time–frequency representations, specifically the instantaneous velocity trajectory that distinguishes the vibrating shaft due to faulty bearing from healthy bearing. Furthermore, using maximum likelihood estimation (MLE), the velocity profile of the vibrating shaft follows Gaussian distribution from collected data at different motor shaft rotations per second (rps) (5, 15, and 25 Hz) with healthy and faulty bearings. While the mean of distribution for all the cases remains constant, the variance of latter increases with the increase in rps. Finally, the probabilistic approach and the model parameters are exploited to quantify the probability of motor failure in terms of complementary cumulative distribution function (CCDF). It is observed that the CCDF for the faulty case is greater than that of healthy case for all velocities and rps.