AN ADAPTIVE, ON LINE, STATISTICAL METHOD AND APPARATUS FOR MOTOR BEARING FAULT DETECTION BY PASSIVE MOTOR CURRENT MONITORING

摘要

A motor current signal is monitored during a learning stage and divided into a plurality of statistically homogeneous segments representative of good operating modes. A representative parameter and a respective boundary of each segment is estimated. The current signal is monitored during a test stage to obtain test data, and the test data is compared with the representative parameter and the respective boundary of each respective segment to detect the presence of a fault in a motor. Frequencies at which bearing faults are likely to occur in a motor can be estimated, and a weighting function can highlight such frequencies during estimation of the parameter. The current signal can be divided into the segments by dividing the current signal into portions each having a specified length of time; calculating a spectrum strip for each portion; and statistically comparing current spectra of adjacent ones of the strips to determine edge positions for the segments. Estimating the parameter and the boundary of each segment can include calculating a segment mean (the representative parameter) and variance for each frequency component in each respective segment; calculating a modified Mahalanobis distance for each strip of each respective segment; and calculating the modified Mahalanobis mean and the variance for each respective segment. Each modified Mahalanobis mean can form a respective radius about a respective segment mean to define a respective boundary.