DocumentCode
2682450
Title
Real Time Novelty Detection Modeling for Machine Health Prognostics
Author
Filev, Dimitar P. ; Tseng, Finn
Author_Institution
Dept. of KBS & Control, Ford Motor Co., Dearborn, MI
fYear
2006
fDate
3-6 June 2006
Firstpage
529
Lastpage
534
Abstract
The paper deals with a real time algorithm for modeling and prediction of machine health status. It utilizes the concepts of fuzzy k-nearest neighbor clustering and the Gaussian mixture model to model the machine feature space as a loose collection of clusters representing the dynamics of the main operating modes
Keywords
Gaussian processes; condition monitoring; pattern clustering; Gaussian mixture model; fuzzy k-nearest neighbor clustering; machine feature space; machine health prognostics; real time novelty detection modeling; Clustering algorithms; Condition monitoring; Fault diagnosis; Feature extraction; Hidden Markov models; Knowledge based systems; Machine learning; Mathematical model; Predictive models; Signal processing;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Information Processing Society, 2006. NAFIPS 2006. Annual meeting of the North American
Conference_Location
Montreal, Que.
Print_ISBN
1-4244-0362-6
Electronic_ISBN
1-4244-0363-4
Type
conf
DOI
10.1109/NAFIPS.2006.365465
Filename
4216858
Link To Document