An abnormality detection device comprising: a deviation inclination calculation unit configured to calculate first deviation scores by using, as an input, evaluation data that is obtained from a subject apparatus, wherein the evaluation data comprises operation data of the subject apparatus, and to calculate deviation inclination information from the calculated first deviation scores, the first deviation scores respectively being calculated in a plurality of outlier detection methods to specify data that is deviated from other data from among subject data, and to calculate a second deviation score indicating an abnormality judgment rate, being a degree to be judged abnormal, from a deviation degree of the specified data, and the deviation inclination information being data in which the first deviation scores are integrated; and an abnormality detection unit configured to calculate, for each of a plurality of abnormality patterns, a similarity degree between deviation sensitivity information indicating a sensitivity, being a level of an abnormality detection ability, of each of the plurality of outlier detection methods with respect to each of the plurality of abnormality patterns, and the deviation inclination information calculated by the deviation inclination calculation unit, to add the calculated similarity degree to a similarity degree vector, and to detect all abnormality patterns having a similarity degree surpassing a prescribed threshold value with respect to each element of the similarity degree vector, as abnormality patterns having probability of occurrence, wherein each of the plurality of abnormality patterns is a pattern that may occur in the subject apparatus, each of the plurality of outlier detection methods is a method that is capable of detecting at least one abnormality pattern from among the plurality of abnormality patterns, the second deviation score is extracted from the first deviation scores, characterized in that the operation data at time t is L × k matrix data, the deviation inclination information is a column vector data St consisting of N pieces of elements, the first deviation scores St(i) are integrated for the number of times N of the outlier detection methods, the deviation inclination information is column vector data St wherein the first deviation scores St(i) are arranged in a comparison order of the outlier detection methods, the first deviation score St(i) is f/L, f is a number of abnormality scores, L is a number of times at which sensing is performed at regular intervals in a time length m, and k is a number of variables of operation data at the time t. a deviation inclination calculation unit configured to calculate first deviation scores by using, as an input, evaluation data that is obtained from a subject apparatus, wherein the evaluation data comprises operation data of the subject apparatus, and to calculate deviation inclination information from the calculated first deviation scores, the first deviation scores respectively being calculated in a plurality of outlier detection methods to specify data that is deviated from other data from among subject data, and to calculate a second deviation score indicating an abnormality judgment rate, being a degree to be judged abnormal, from a deviation degree of the specified data, and the deviation inclination information being data in which the first deviation scores are integrated; and an abnormality detection unit configured to calculate, for each of a plurality of abnormality patterns, a similarity degree between deviation sensitivity information indicating a sensitivity, being a level of an abnormality detection ability, of each of the plurality of outlier detection methods with respect to each of the plurality of abnormality patterns, and the deviation inclination information calculated by the deviation inclination calculation unit, to add the calculated similarity degree to a similarity degree vector, and to detect all abnormality patterns having a similarity degree surpassing a prescribed threshold value with respect to each element of the similarity degree vector, as abnormality patterns having probability of occurrence, wherein each of the plurality of abnormality patterns is a pattern that may occur in the subject apparatus, each of the plurality of outlier detection methods is a method that is capable of detecting at least one abnormality pattern from among the plurality of abnormality patterns, the second deviation score is extracted from the first deviation scores, characterized in that the operation data at time t is L × k matrix data, the deviation inclination information is a column vector data St consisting of N pieces of elements, the first deviation scores St(i) are integrated for the number of times N of the outlier detection methods, the deviation inclination information is column vector data St wherein the first deviation scores St(i) are arranged in a comparison order of the outlier detection methods, the first deviation score St(i) is f/L, f is a number of abnormality scores, L is a number of times at which sensing is performed at regular intervals in a time length m, and k is a number of variables of operation data at the time t.