Monitoring diagnosis device for electrical appliance
First Claim
1. A monitoring diagnosis device for an electrical appliance, comprising:
- sensor means for detecting a parameter of the electrical appliance and generating an output corresponding to the parameter indicative of a cause of abnormality of the electrical appliance said sensor means including a partial discharge sensor;
a neural network means including an input layer, an intermediate layer, and an output layer, the input layer, intermediate layer, and output layer each consisting of a plurality of neural elements each simulating a living neuron, wherein the neural elements of the input layer are coupled to the neural elements of the intermediate layer via respective connection weights, and the neural elements of the intermediate layer are coupled to the neural elements of the output layer via respective connection weights, and wherein said connection weights between the input layer and intermediate layer and between the intermediate layer and the output layer are adjusted on the basis of learning data consisting of causes of abnormality and instances of the output of said sensor means, such that a neural element of the output layer corresponding to a cause of abnormality has a high logic output in response to the output of said sensor means indicative of the existence of an abnormality while other neural elements of the output layer have a low logic output;
a preprocessor means for preprocessing output waveform samples of the output of said sensor means to obtain characteristic waveforms consisting of peaks each corresponding to an abrupt variation in the waveform samples;
an averaging means for averaging the characteristic waveforms to obtain an average characteristic waveform; and
normalizer means for normalizing a peak level of the averaged characteristic waveform to unity and dividing a time interval of the waveform into a plurality of subintervals, to obtain a characteristic waveform histogram;
wherein levels of said characteristic waveform histogram are input to corresponding neural elements of the input layer of said neural network means.
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Accused Products
Abstract
A monitoring diagnostic device for an electrical appliance such as gas insulated switchgear includes a sensor, such as an acceleration sensor, and a neural network including an input layer, an intermediate layer, and an output layer, each consisting of a plurality of neural elements. The input, intermediate and output layers are coupled to each other via a plurality of connection weights. The output of the sensor is first processed and then is supplied to the neural elements of the input layer. The connection weights are adjusted by means of learning data such that the output from the neural elements of the output layer of the neural network correctly identifies the causes of abnormality of the electrical appliance.
30 Citations
5 Claims
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1. A monitoring diagnosis device for an electrical appliance, comprising:
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sensor means for detecting a parameter of the electrical appliance and generating an output corresponding to the parameter indicative of a cause of abnormality of the electrical appliance said sensor means including a partial discharge sensor; a neural network means including an input layer, an intermediate layer, and an output layer, the input layer, intermediate layer, and output layer each consisting of a plurality of neural elements each simulating a living neuron, wherein the neural elements of the input layer are coupled to the neural elements of the intermediate layer via respective connection weights, and the neural elements of the intermediate layer are coupled to the neural elements of the output layer via respective connection weights, and wherein said connection weights between the input layer and intermediate layer and between the intermediate layer and the output layer are adjusted on the basis of learning data consisting of causes of abnormality and instances of the output of said sensor means, such that a neural element of the output layer corresponding to a cause of abnormality has a high logic output in response to the output of said sensor means indicative of the existence of an abnormality while other neural elements of the output layer have a low logic output; a preprocessor means for preprocessing output waveform samples of the output of said sensor means to obtain characteristic waveforms consisting of peaks each corresponding to an abrupt variation in the waveform samples; an averaging means for averaging the characteristic waveforms to obtain an average characteristic waveform; and normalizer means for normalizing a peak level of the averaged characteristic waveform to unity and dividing a time interval of the waveform into a plurality of subintervals, to obtain a characteristic waveform histogram; wherein levels of said characteristic waveform histogram are input to corresponding neural elements of the input layer of said neural network means. - View Dependent Claims (2, 3)
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4. A monitoring diagnosis device for an electrical appliance, comprising:
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an acceleration sensor which detects mechanical vibrations and generates an output waveform; a Fast Fourier Transform means for generating spectrum curves of the output waveform by performing a Fast Fourier Transform on the waveform; means for averaging the spectrum curves to obtain an averaged spectrum curve; first means for generating a histogram of the averaged spectrum curve; a partial discharge sensor for detecting variations in potential and for generating an output waveforms; a preprocessor means for preprocessing the output waveforms of said partial discharge sensor to obtain characteristic waveforms consisting of a plurality of peaks; an averaging means for averaging the characteristic waveforms to obtain an averaged characteristic waveform; and second means for generating a histogram of the averaged characteristic waveform; and a neural network having first and second inputs connected to said first and second means for generating a histogram, said neural network including an input layer, an intermediate layer, and an output layer, the input layer, intermediate layer, and output layer each consisting of a plurality of neural elements each simulating a living neuron, wherein the neural elements of the input layer are coupled to the neural elements of the intermediate layer via respective connection weights, and the neural elements of the intermediate layer are coupled to the neural elements of the output layer via respective connection weights, and wherein said connection weights between the input layer and intermediate layer and between the intermediate layer and the output layer are adjusted on the basis of learning data consisting of causes of abnormality and instances of the output of said acceleration sensor and said partial discharge sensor, such that a neural element of the output layer corresponding to a cause of abnormality has a high logic output in response to the output waveform of said acceleration sensor and in response to the output waveform of said partial discharge sensor indicative of the existence of an abnormality while other neural elements of the output layer have a low logic output. - View Dependent Claims (5)
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Specification