An adaptive, on-line, statistical method for bearing fault detection using stator current

It is well-known that motor current is a nonstationary signal whose properties vary with respect to the time varying operating conditions of the motor. As a result Fourier analysis makes it difficult to recognize fault conditions from the normal operating conditions of the motor. Time-frequency analysis, on the other hand, unambiguously represents the motor current which makes signal properties related to fault detection more evident in the transform domain. In this paper, we present an adaptive, statistical, time-frequency method for the detection of bearing faults. Due to the time varying normal operating conditions of the motor and the effect of motor geometry on the current, we employ a training base approach in which the algorithm is trained to recognize the normal operating conditions of the motor before the actual testing starts. The experimental results from our study suggests that the proposed method provides a powerful, and a general approach to the motor current based fault detection.