Patent ID: 11869492
Assignee: HITACHI, LTD.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 5:
6. An anomaly detection method comprising:
generating a first hierarchical conditional vector including type information indicating a type of a device and identifier information indicating an identifier of the device based on device identification information for identifying a specific device for each type of the device;
obtaining a first acoustic signal from a first target device;
extracting a first target device feature amount vector;
generating a first noise signal by a noise generator;
indicating a feature amount of the first acoustic signal by analyzing the first acoustic signal;
generating a first noise vector based on a baseline acoustic signal of the first target device, and the first noise signal;
generating, using a generation network, a first false vector simulating the first target device feature amount vector based on the first hierarchical conditional vector and the first noise vector;
performing true or false determination to determine true or false status of the first target device feature amount vector by using an identification network, and generating true or false determination information;
performing background noise determination to determine a background noise level of a surrounding environment of the first target device by analyzing the first hierarchical conditional vector and the first target device feature amount vector by using the identification network, and generating first background noise level information indicating a first background noise level;
calculating a first loss indicating a cross entropy loss by the true or false determination;
calculating a second loss indicating a cross entropy loss by the background noise determination;
training the generation network and the identification network using the first loss and the second loss to generate a trained generation network and a trained identification network;
generating a second hierarchical conditional vector based on the device identification information;
obtaining a second acoustic signal from a second target device;
extracting a second target device feature amount vector indicating a feature amount of the second acoustic signal by analyzing the second acoustic signal;
generating a second noise vector based on a baseline acoustic signal of the second target device;
performing background noise determination to determine a background noise level of a surrounding environment of the second target device by analyzing the second hierarchical conditional vector and the second target device feature amount vector using the trained identification network, and generating second background noise level information indicating a second background noise level;
generating a second false vector simulating the second target device feature amount vector based on the second hierarchical conditional vector and the second noise vector using the trained generation network;
calculating an anomaly score for the second target device feature amount vector based on the second target device feature amount vector and the second false vector using the trained generation network;
selecting an anomaly determination threshold based on the second background noise level; and
generating an anomaly detection result indicating whether an anomaly exists in the second target device feature amount vector based on the anomaly score and the anomaly determination threshold.