Abstract
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.
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