Unsupervised Clustering for Fault Diagnosis - CentraleSupélec Access content directly
Conference Papers Year : 2012

Unsupervised Clustering for Fault Diagnosis


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.


No file

Dates and versions

hal-00777451 , version 1 (17-01-2013)


  • HAL Id : hal-00777451 , version 1


Enrico Zio, Francesco Di Maio. Unsupervised Clustering for Fault Diagnosis. Prognostics and System Health Management Conference - PHM2012, May 2012, China. ⟨hal-00777451⟩
57 View
0 Download


Gmail Mastodon Facebook X LinkedIn More