Abstract: Provided are an abnormality detection system and an abnormality detection method capable of performing more stable abnormality detection. An abnormality detection system that detects an abnormality of the target machine by a computer includes a communication unit configured to acquire first data from a first sensor attached to the target machine and second data from a second sensor attached to the target machine, an arithmetic unit, and a memory unit. The arithmetic unit includes an encoding unit trained to generate latent expressions including a predetermined latent expression that estimates the second data on the basis of the first data, a decoding unit trained to restore the first data from the latent expressions, and an abnormality detection unit configured to detect the abnormality of the target machine on the basis of a restoration error between the first data and the first data restored by the decoding unit.

Abstract: An abnormality degree calculation system includes a concept classification assignment unit that assigns a predetermined concept classification based on an identification number of a target device, a feature value vector extraction unit that extracts a feature value vector based on sensor data of a sensor corresponding to the target device, a likelihood calculation unit that calculates a likelihood of the feature value vector by using a machine learning model obtained from a learning database, a loss calculation unit that calculates a loss using a loss function as a function of the likelihood, a model update unit that updates the model by using the loss and a model, a re-learning necessity determination unit that determines whether re-learning is necessary from the calculated likelihood when an abnormality of the target device is detected, and an abnormality degree calculation unit that calculates an abnormality degree when the re-learning is unnecessary.