Detecting occurrence of abnormality

摘要

A method, apparatus and computer program for detecting occurrence of an anomaly. The method can exclude arbitrariness and objectively judge whether a variation of a physical quantity to be detected is abnormal or not even when an external environment is fluctuating. The method includes acquiring multiple primary measurement values from a measurement target. Further, calculating and a reference value for each of the multiple primary measurement values by optimal learning. The method further includes calculating a relationship matrix which indicates mutual relationships between the multiple secondary measurement values. Further the method includes calculating an anomaly score for each of the secondary measurement value which indicates the degree of the measurement target being abnormal. The anomaly score is calculated by comparing the secondary measurement value with a predictive value which is calculated based on the relationship matrix and other secondary measurement values.

主权项

1. A method for detecting an occurrence of an abnormality in a measurement target, the method comprising:
acquiring multiple primary measurement values from the measurement target; calculating multiple reference values so that a reference value is calculated for each primary measurement value using optimal learning; calculating multiple secondary measurement values with the multiple primary measurement values and the corresponding multiple reference values, wherein the multiple secondary measurement values are calculated by subtracting the corresponding multiple reference values from the multiple primary measurement values; calculating a relationship matrix indicating a plurality of mutual relationships among the multiple secondary measurement values; and calculating an anomaly score for each secondary measurement value, which indicates the degree the measurement target is abnormal by comparing a predictive value with the secondary measurement value, wherein the predictive value is calculated based on the relationship matrix and other multiple secondary measurement values.