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Anomaly Detection Method Based on Fast Local Subspace Classifier
Author(s) -
SHIBUYA HISAE,
MAEDA SHUNJI
Publication year - 2016
Publication title -
electronics and communications in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.131
H-Index - 13
eISSN - 1942-9541
pISSN - 1942-9533
DOI - 10.1002/ecj.11770
Subject(s) - anomaly detection , subspace topology , pattern recognition (psychology) , artificial intelligence , classifier (uml) , computer science , mathematics
SUMMARY An anomaly detection method based on multidimensional time‐series sensor data and using normal state models has been developed. The local subspace classifier (LSC) method is employed to handle the various normal states and the fast LSC method is proposed to reduce the computation time. Clustering is utilized to reduce the amount of data when searching in the fast LSC (FLSC) method. The effectiveness of the FLSC method is confirmed against data from real equipment. The FLSC method is 1 to 10 times as fast as the LSC method.