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
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;
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
DOI :
10.1109/NAFIPS.2006.365465
