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US 12,460,994 B2Granted
Abnormality detection device
AssigneeDenso Corporation
InventorAtsushi Moribe
Life of this patent
Estimating the term…
Abstract
An abnormality detection device, method, or a storage medium acquires learning target data and monitoring target data, generates a state observer by using a variable in an input variable configuration, generates a threshold, calculates an abnormality degree by combining a second state observation value and the monitoring target data and inputting a combined result to the competitive neural network, and calculates a determination result.
Claims17
An abnormality detection device comprising:
- a signal acquisition portion configured to acquire learning target data and monitoring target data, the learning target data is data used for machine learning and the monitoring target data is data of a monitoring target;
- a state observer generation portion configured to generate a state observer by using a variable in an input variable configuration;
- a normal model generation portion configured to generate a threshold by combining a first state observation value obtained by input of the learning target data to the state observer and the learning target data and inputting a combined result to a competitive neural network;
- an abnormality degree calculation portion configured to calculate an abnormality degree by combining a second state observation value obtained by input of the monitoring target data to the state observer and the monitoring target data and inputting a combined result to the competitive neural network;
- and
- a determination portion configured to calculate a determination result by comparing the threshold with the abnormality degree, wherein
- the abnormality degree is a difference between the learning target data and the second state observation value, which are input to the competitive neural network, and neuron weight data of a winning unit of the competitive neural network.
4 more elements
An abnormality detection method comprising:
- acquiring learning target data, the learning target data is data used for machine learning;
- generating a state observer by using a variable in an input variable configuration;
- generating a threshold by combining a first state observation value obtained by input of the learning target data to the state observer and the learning target data and inputting a combined result to a competitive neural network;
- acquiring monitoring target data, the monitoring target data is data of a monitoring target;
- calculating an abnormality degree by combining a second state observation value obtained by input of the monitoring target data to the state observer and the monitoring target data and inputting a combined result to the competitive neural network;
- and
- calculating a determination result by comparing the threshold with the abnormality degree, wherein
- the abnormality degree is a difference between the learning target data and the second state observation value, which are input to the competitive neural network, and neuron weight data of a winning unit of the competitive neural network.
5 more elements
A computer-readable non-transitory storage medium storing an abnormality detection program configured to cause a computer to:
- acquire learning target data, the learning target data is data used for machine learning;
- generate a state observer by using a variable in an input variable configuration;
- generate a threshold by combining a first state observation value obtained by input of the learning target data to the state observer and the learning target data and inputting a combined result to a competitive neural network;
- acquire monitoring target data, the monitoring target data is data of a monitoring target;
- calculate an abnormality degree by combining a second state observation value obtained by input of the monitoring target data to the state observer and the monitoring target data and inputting a combined result to the competitive neural network;
- and
- calculate a determination result by comparing the threshold with the abnormality degree, wherein
- the abnormality degree is a difference between the learning target data and the second state observation value, which are input to the competitive neural network, and neuron weight data of a winning unit of the competitive neural network.
5 more elements
Dependencies are inferred from the claim text.