Neural network auto-associator 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 signal 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 first signal processing means and further coupled to said sensing means for receiving at least a portion of said frequency spectral components and at least a portion of said set of current values as an input vector and having output terminals for providing an output vector; and
second processing means coupled to said output terminals for comparing said input and output vectors for providing an error metric.
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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; 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 neural network auto-associator. A decision is generated based on the difference between the input and output of the network.
70 Citations
20 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 signal 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 first signal processing means and further coupled to said sensing means for receiving at least a portion of said frequency spectral components and at least a portion of said set of current values as an input vector and having output terminals for providing an output vector; and second processing means coupled to said output terminals for comparing said input and output vectors for providing an error metric. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. 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) and 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 at least a selected portion of said frequency spectral components and at least 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 using a set of current values obtained from a motor known to be operating normally; second processing means coupled to said output terminals for comparing said input and output vectors for providing an error metric. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20)
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Specification