Radial basis function neural network autoassociator and method for induction motor monitoring
First Claim
1. A method for detecting a departure from normal operation of an electric motor, comprising:
- obtaining a set of current measurements for a motor being monitored during a training phase, said motor being known to be operating normally;
processing said set of current measurements so as to provide current measurement training vectors;
forming clusters of said current measurement vectors during said training phase;
applying said clusters of current measurement vector to a neural network auto-associator and training said neural network auto-associator using said set of normal current measurements;
making a set of operational current measurements for said motor in actual operation;
processing said of operational current measurements so as to provide an operational current measurement vector;
applying said operational current measurement vector to said neural network auto-associator;
comparing said set of current measurements with the output of said neural network auto-associator; and
indicating abnormal operation whenever said comparing produces a result in accordance with predetermined criteria.
0 Assignments
0 Petitions
Accused Products
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.
-
Citations
4 Claims
-
1. A method for detecting a departure from normal operation of an electric motor, comprising:
-
obtaining a set of current measurements for a motor being monitored during a training phase, said motor being known to be operating normally; processing said set of current measurements so as to provide current measurement training vectors; forming clusters of said current measurement vectors during said training phase; applying said clusters of current measurement vector to a neural network auto-associator and training said neural network auto-associator using said set of normal current measurements; making a set of operational current measurements for said motor in actual operation; processing said of operational current measurements so as to provide an operational current measurement vector; applying said operational current measurement vector to said neural network auto-associator; comparing said set of current measurements with the output of said neural network auto-associator; and indicating abnormal operation whenever said comparing produces a result in accordance with predetermined criteria. - View Dependent Claims (2, 3, 4)
-
Specification