Radial basis function neural network autoassociator and method f

Details Radial basis function neural network autoassociator and method f Radial basis function neural network autoassociator and method f

1996-05-22

1997-06-17

Wieder, Kenneth A.

Electricity: measuring and testing

Impedance, admittance or other quantities representative of...

Lumped type parameters

395915, 128733, G01R 104, G06K 962, A61B 505

Patent

active

056401035

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; forming clusters of the normal current measurements; training a neural network auto-associator using the set of normal current measurements; making current measurements for the motor in operation; comparing the input and output of the auto-associator; 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 clusters of current measurements collected while the motor is known to be in a normal operating condition--to reproduce the inputs on the output. A new set of FFT's of current measurements are classified as "good" or "bad" by first transforming the measurement using a Fast Fourier Transform (FFT) and an internal scaling procedure, and then applying a subset of the transformed measurements as inputs to the neural network auto-associator. A decision is generated based on the difference between the input and output of the network.

REFERENCES:
patent: 5092343 (1992-03-01), Spitzer et al.
patent: 5214715 (1993-05-01), Carpenter et al.

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