| 摘要 |
PROBLEM TO BE SOLVED: To provide an abnormality detecting method and an abnormality detecting system which can reduce a burden of a user and detect an abnormality early with high sensitivity, by using a learning data generation method where learning data of normal case examples for detecting the abnormality of observed data is sorted out to enhance degree of precision.SOLUTION: An abnormality detecting system 1 comprises the steps of: (1) generating compact learning data composed of normal case examples, by paying attention to similarity among data; (2) adding new data to the learning data on the basis of the similarity and presence of abnormality; (3) removing an alarm generation interval from the learning data; (4) modeling the learning data which is updated as needed by using a subspace method, and detecting abnormality candidates on the basis of distance relation between observed data and subspace; (5) detecting the abnormality among the abnormality candidates by combining analyses that target event information; and (6) calculating deviation of the observed data, and identifying an abnormal element (a sensor signal) of the observed data on the basis of utilization frequency distribution of the learning data. |
