Neural network auto-associator and method for induction motor monitoring
US5576632A · kind A · utility
Assignee
Inventors
Key dates
| Filing date | Jun 30, 1994 |
| Grant date | Nov 19, 1996 |
| Priority date | — |
| Expiry date | Jun 30, 2014 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH02H1/0092
- 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; 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. A new set 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 neu…
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.