Abstract: A computer-based system and method for ascertaining anomalies in an electric motor, includes computing and processing a set of Fast Fourier Transforms (FFT's) of the supply current waveforms for a motor known to be in a normal condition to create a feature set of input vectors. The input vector assignments for all possible cluster groupings 1 through n are derived using Ward's method. The method includes computing the Approximate Weight of Evidence (AWE) for each cluster grouping then selecting that count associated with a maximum AWE and designating this count as s. For each cluster in each grouping 1 through s, the method finds the member vector farthest from the cluster's centroid and defines this as the cluster's radius.

Abstract: A method for detecting a departure from normal operation of an electric motor comprises obtaining a set of normal current measurements for a motor being monitored; training a neural network auto-associator using the set of normal current measurements; making current measurements for the motor in operation; comparing the current measurements with the normal current measurements; and indicating abnormal operation whenever the current measurements deviate more than a predetermined amount from the normal current measurements. The method models a set of normal current measurements for the motor being monitored, and indicates a potential failure whenever measurements from the motor deviate significantly from a model. The model takes the form of an neural network auto-associator which is "trained"--using current measurements collected while the motor is known to be in a normal operating condition--to reproduce the inputs on the output.

Abstract: Given a set of objects (A, B, C, . . . ), each described by a set of attribute values, and given a classification of these objects into categories, a similarity function accounts well for this classification when only a small number of objects are not correctly classified.