A Novelty Detection Technique fo Machine Condition Monitoring USing S.O.M

This paper presents a novelty detection based method for machine condition monitoring (MCM) using Kohonen's self-organising map (S.O.M.). As the fault data set is difficult to acquire in MCM problems, the methods requires only the knowledge of normal condition data set. By exploiting S.O.M.'s ability of multi-dimensional mapping, the Euclidean distance between the S.O.M and the data under test is used to discriminate anomaly from normal condition. A set of real world condition monitoring data is used to evaluate the method presented. Experimental result shows high accuracy and reliability of this method