Radial basis function neural network autoassociator and method for induction motor monitoring
US5640103A · kind A · utility
Assignee
Inventors
Key dates
| Filing date | May 22, 1996 |
| Grant date | Jun 17, 1997 |
| Priority date | — |
| Expiry date | May 22, 2016 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY10S706/912
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
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 sub…
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