Adaptive, on line, statistical method and apparatus for detection of broken bars in motors by passive motor current monitoring and digital torque estimation

摘要

During a learning stage a motor current signal is monitored, and estimated motor torque is used to transform the current signal into a time-frequency spectra including a plurality of 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 broken bar faults are likely to occur in a motor can be estimated using the estimated motor torque, and a weighting function can highlight such frequencies during estimation of the parameter. The current signal can be further subdivided into the segments by monitoring sidebands of the frequency components of current spectrum strips of each segment. 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 for each respective segment, using respective modified Mahalanobis distances to calculate a respective radius about a respective segment mean to define the respective boundary.