EQUIPMENT ANOMALY DETECTION METHOD, COMPUTER READABLE STORAGE MEDIUM, CHIP, AND DEVICE
A detection device is provided in the disclosure. The device uses unsupervised or self-supervised neural networks to learn nominal conditions of a target system, such as a device or a machine. The trained neural networks can reproduce sensory signals of the target system as a neural-network-reconstr...
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| creator | WEIDEL, Philipp JEANNINGROS, Loic MUIR, Richard Dylan |
| description | A detection device is provided in the disclosure. The device uses unsupervised or self-supervised neural networks to learn nominal conditions of a target system, such as a device or a machine. The trained neural networks can reproduce sensory signals of the target system as a neural-network-reconstructed version of the sensory signals in the nominal conditions of a target system. The equipment anomaly detection device may analyze and predict operation conditions of the target system based on the neural-network-reconstructed version exceeding a certain level. When signal difference between the sensory signals and the neural-network-reconstructed version exceeds a certain level, the equipment anomaly detection device may issue an alert signal to reflect abnormal operation conditions of the target system. |
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The device uses unsupervised or self-supervised neural networks to learn nominal conditions of a target system, such as a device or a machine. The trained neural networks can reproduce sensory signals of the target system as a neural-network-reconstructed version of the sensory signals in the nominal conditions of a target system. The equipment anomaly detection device may analyze and predict operation conditions of the target system based on the neural-network-reconstructed version exceeding a certain level. 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The device uses unsupervised or self-supervised neural networks to learn nominal conditions of a target system, such as a device or a machine. The trained neural networks can reproduce sensory signals of the target system as a neural-network-reconstructed version of the sensory signals in the nominal conditions of a target system. The equipment anomaly detection device may analyze and predict operation conditions of the target system based on the neural-network-reconstructed version exceeding a certain level. When signal difference between the sensory signals and the neural-network-reconstructed version exceeds a certain level, the equipment anomaly detection device may issue an alert signal to reflect abnormal operation conditions of the target system.</abstract><oa>free_for_read</oa></addata></record> |
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| language | eng ; fre ; ger |
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| subjects | ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVEREDIN A SINGLE OTHER SUBCLASS CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING CONTROL OR REGULATING SYSTEMS IN GENERAL CONTROLLING COUNTING FUNCTIONAL ELEMENTS OF SUCH SYSTEMS MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC ORINFRASONIC WAVES MEASURING MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS PHYSICS REGULATING TARIFF METERING APPARATUS TESTING |
| title | EQUIPMENT ANOMALY DETECTION METHOD, COMPUTER READABLE STORAGE MEDIUM, CHIP, AND DEVICE |
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