Radial basis function neural network autoassociator and method for induction motor monitoring
First Claim
1. Apparatus for detecting a departure from normal operation of an electric motor, comprising:
- sensing means for measuring a set of current values for a motor being monitored;
first processing means coupled to said sensing means for deriving frequency spectral components associated with said set of current values;
a neural network auto-associator coupled to said signal processing means and further coupled to said sensing means for receiving at least one of at least a portion of said spectral components and at least a portion of said current values as an input vector and having output terminals for providing an output vector, said neural network having undergone a training phase; and
second processing means coupled to said output terminals for comparing said input and output vectors for providing an error metric, wherein said first processing means clusters said frequency spectral components into a smaller number of clusters during said training phase.
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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.
73 Citations
19 Claims
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1. Apparatus for detecting a departure from normal operation of an electric motor, comprising:
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sensing means for measuring a set of current values for a motor being monitored; first processing means coupled to said sensing means for deriving frequency spectral components associated with said set of current values; a neural network auto-associator coupled to said signal processing means and further coupled to said sensing means for receiving at least one of at least a portion of said spectral components and at least a portion of said current values as an input vector and having output terminals for providing an output vector, said neural network having undergone a training phase; and second processing means coupled to said output terminals for comparing said input and output vectors for providing an error metric, wherein said first processing means clusters said frequency spectral components into a smaller number of clusters during said training phase. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 13, 14, 17, 18)
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11. Apparatus for detecting a departure from normal operation of an electric motor, comprising:
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sensing means for measuring a set of current values for a motor being monitored; first processing means coupled to said sensing means for deriving frequency spectral components associated with said set of current values in accordance with a Fast Fourier Transform (FFT), for scaling said frequency spectral components in accordance with predetermined weights; a neural network auto-associator coupled to said signal processing means and further coupled to said sensing means for receiving at least one of a selected portion of said frequency spectral components and a portion of said current values as an input vector and having output terminals for providing an output vector, and including a hidden layer, said autoassociator having been trained in a training phase, using a set of current values obtained from a motor known to be operating normally, said first processing means having clustered said frequency spectral components into a smaller number of clusters during said training phase; and second processing means coupled to said output terminals for comparing said input and output vectors for providing an error metric. - View Dependent Claims (12, 15, 16, 19)
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Specification