Paper Info
Title
Prognosis Based on Handling Drifts in Dynamical Environments: Application to a Wind Turbine Benchmark
Abstract
In this paper, we present a prognosis architecture that allows the computation of the Remaining Useful Life (RUL) of a failing process. A process subject to an incipient fault experiments slowly developing degradation. Sensor measurements and Condition Monitoring (CM) data extracted from the system allow to follow up the process drift. The prognosis architecture we propose makes use of a dynamical clustering algorithm to model the data in a feature space. This algorithm uses a sliding window scheme on which the model is iteratively updated. Metrics applied on the parameters of this model are used to compute a drift severity indicator, which is also an indicator of the health of the system. The architecture for prognosis is applied on a benchmark of wind turbine. The used benchmark has been constructed to serve as a realistic wind turbine model. It was used in the context of a global scale fault diagnosis and fault tolerant control competition. The benchmark also proposed a drifting fault scenario that we used to test our approach.
Year
DOI
Venue
2012
10.1109/ICMLA.2012.131
ICMLA (2)
Keywords
Field
DocType
process drift,realistic wind turbine model,wind turbine benchmark,dynamical environments,process subject,fault scenario,prognosis architecture,used benchmark,fault tolerant control competition,drift severity indicator,global scale fault diagnosis,incipient fault experiment,handling drifts,sensors,benchmark testing,fault tolerance,wind turbines
Computer science,Real-time computing,Condition monitoring,Artificial intelligence,Turbine,Cluster analysis,Benchmark (computing),Wind power,Feature vector,Sliding window protocol,Simulation,Fault tolerance,Machine learning
Conference
Citations 
PageRank 
References 
2
0.63
9
Authors
3
Name
Order
Citations
PageRank
Antoine Chammas141.23
Eric Duviella22311.69
Stephane Leceouche320.63