Paper Info
Title
Drift detection and characterization for fault diagnosis and prognosis of dynamical systems
Abstract
In this paper, we present a methodology for drift detection and characterization. Our methodology is based on extracting indicators that reflect the health state of a system. It is situated in an architecture of fault diagnosis/prognosis of dynamical system that we present in this paper. A dynamical clustering algorithm is used as a major tool. The feature vectors are clustered and then the parameters of these clusters are updated as each feature vector arrives. The cluster parameters serve to compute indicators for drift detection and characterization. Then, a prognosis block uses these drift indicators to estimate the remaining useful life. The architecture is tested on a case study of a tank system with different scenarios of single and multiple faults, and with different dynamics of drift.
Year
DOI
Venue
2012
10.1007/978-3-642-33362-0_9
SUM
Keywords
Field
DocType
fault diagnosis,dynamical clustering algorithm,drift indicator,prognosis block,different dynamic,tank system,dynamical system,different scenario,drift detection,feature vector,case study,drift
Situated,Data mining,Cluster (physics),Feature vector,Computer science,Dynamical systems theory,Drift detection,Cluster analysis,Dynamical system
Conference
Citations 
PageRank 
References 
2
0.60
13
Authors
4
Name
Order
Citations
PageRank
Antoine Chammas141.23
Moamar Sayed-Mouchaweh2173.95
Eric Duviella32311.69
Stéphane Lecoeuche45713.03