Title :
Unsupervised clustering for fault diagnosis
Author :
Baraldi, Piero ; Maio, Francesco Di ; Zio, Enrico
Author_Institution :
Dept. of Energy, Politec. di Milano, Milano, Italy
Abstract :
We develop an unsupervised clustering method for the classification of transient data. A fuzzy-based technique is employed to measure the similarity among the transients; a spectral clustering technique, embedding the unsupervised Fuzzy C-Means (FMC) algorithm, is applied to the matrix of similarity values so that the clusters are formed by patterns most similar to each other. The performance of the proposed technique is tested with respect to a case study with data artificially generated.
Keywords :
fault diagnosis; feature extraction; fuzzy set theory; matrix algebra; pattern classification; signal classification; spectral analysis; fault diagnosis; feature extraction; fuzzy-based technique; pattern clustering; plant operation; similarity value matrix; spectral clustering technique; transient data classification; transient similarity measure; unsupervised clustering method; unsupervised fuzzy C-means algorithm; Degradation; fault diagnosis; fuzzy c-means; fuzzy similarity; spectral analysis; transient data; unsupervised clustering;
Conference_Titel :
Prognostics and System Health Management (PHM), 2012 IEEE Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4577-1909-7
Electronic_ISBN :
2166-563X
DOI :
10.1109/PHM.2012.6228844
