METHOD AND DEVICE FOR MONITORING THE STATE OF A FACILITY
First Claim
1. A method for monitoring the state of facility that detects an anomaly based on a time-series sensor signal output from the facility or an apparatus, comprising:
- a learning process of extracting a feature vector based on the sensor signal, selecting a feature to be used based on data check of the feature vector, selecting learning data to be used based on data check of the feature vector, creating a normal model based on the selected learning data, checking sufficiency of the learning data used for creating the normal model, and setting a threshold in accordance with the sufficiency of the learning data; and
an anomaly detecting process of extracting the feature vector based on the sensor signal, computing an anomaly measurement through the comparison between the normal model and the feature vector, and identifying the anomaly through the comparison between the anomaly measurement and the threshold.
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Abstract
This invention provides method for detecting advance signs of anomalies, event signals outputted from the facility are used to create a separate mode for each operating state, a normal model is created for each mode, the sufficiency of learning data for each mode is checked, a threshold is set according to the results of said check, and anomaly identification is performed using said threshold. Also, for diagnosis, a frequency matrix is created in advance, with result events on the horizontal axis and cause events on the vertical axis, and the frequency matrix is used to predict malfunctions. Malfunction events are inputted as result events, and quantized sensor signals having anomaly measures over the threshold are inputted as cause events.
290 Citations
21 Claims
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1. A method for monitoring the state of facility that detects an anomaly based on a time-series sensor signal output from the facility or an apparatus, comprising:
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a learning process of extracting a feature vector based on the sensor signal, selecting a feature to be used based on data check of the feature vector, selecting learning data to be used based on data check of the feature vector, creating a normal model based on the selected learning data, checking sufficiency of the learning data used for creating the normal model, and setting a threshold in accordance with the sufficiency of the learning data; and an anomaly detecting process of extracting the feature vector based on the sensor signal, computing an anomaly measurement through the comparison between the normal model and the feature vector, and identifying the anomaly through the comparison between the anomaly measurement and the threshold. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method for monitoring the state of facility that detects an anomaly based on a time-series sensor signal and an event signal output from the facility or an apparatus, comprising:
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a learning process of dividing a mode for each operating state based on the event signal, extracting a feature vector based on the sensor signal, selecting a feature to be used based on data check of the feature vector, selecting learning data to be used based on data check of the feature vector, creating a normal model for each mode based on the selected learning data, checking sufficiency of the learning data used for creating the normal model for each mode, and setting a threshold in accordance with the sufficiency of the learning data; and an anomaly detecting process of dividing the mode for each operating state based on the event signal, extracting the feature vector based on the sensor signal, computing an anomaly measurement by comparing the feature vector with the normal model, and identifying the anomaly by comparing the threshold with the anomaly measurement. - View Dependent Claims (9)
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10. A method for monitoring the state of facility that detects an anomaly based on a time-series sensor signal and an event signal output from the facility or an apparatus, comprising:
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a learning process of dividing a mode for each operating state based on the event signal, extracting a feature vector based on the sensor signal, creating a normal model for each mode based on the feature vector, checking sufficiency of the learning data used for creating the normal model for each mode, and setting a threshold in accordance with the sufficiency of the learning data; and an anomaly detecting process of dividing the mode for each operating state based on the event signal, extracting the feature vector based on the sensor signal, computing an anomaly measurement by comparing the feature vector with the normal model, and identifying the anomaly by comparing the threshold with the anomaly measurement. - View Dependent Claims (11, 12, 13)
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14. A method for monitoring the state of facility, comprising:
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mode-dividing a time-series event signal output from the facility or an apparatus in accordance with an operating state of the facility or apparatus; acquiring a feature vector from a time-series sensor signal output from the facility or apparatus; creating a normal model for each divided mode by using the mode dividing information and information on the feature vector acquired from the sensor signal; computing an anomaly measurement of the feature vector for each divided mode by using the created normal model; judging an anomaly by comparing the computed anomaly measurement with a predetermined threshold; and diagnosing whether the facility or apparatus is anomalistic by using the judged anomaly information and the sensor signal. - View Dependent Claims (15, 16, 17)
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18. A device for monitoring the state of facility, comprising:
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a mode dividing means inputting a time-series event signal output from the facility or an apparatus to mode-divide the event signal in accordance with an operating state of the facility or apparatus; a feature-vector computation means inputting the time-series sensor signal output from the facility or apparatus to acquire a feature vector from the input sensor signal; a normal-model creation means creating a normal model for each divided mode by using the mode dividing information from the mode dividing means and information on the feature vector of the sensor signal acquired by the feature-vector computation means; an anomaly-measurement computation means computing an anomaly measurement of the feature vector acquired by the feature-vector computation means for each divided mode by using the normal model created by the normal-model creation means; an anomaly judgment means judging an anomaly by comparing the anomaly measurement computed by the anomaly measurement computation means with a predetermined threshold; and an anomaly diagnosis means diagnosing whether the facility or apparatus is anomalistic by using the information on the anomaly judged by the anomaly judgment means and the time-series sensor signal output from the facility or apparatus. - View Dependent Claims (19, 20, 21)
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Specification