Name
Abstract | ||
|---|---|---|
In this paper, a fault diagnosis architecture based on a dynamical clustering algorithm is developed to detect and isolate faults in wind turbines. The challenge is to deal with different kinds of faults. Constraints on the time of detection are also added in the sense that a fault must be detected as soon as possible. Also, limited historical data corresponding only to normal operating modes are available. Our methodology is based on a data-driven model and is therefore not dependent of the physical models in the wind turbine. It consists of extracting, from sensor measurements, features that are fed into a dynamical clustering algorithm. The latter learns process behaviors characterized by clusters with the ability to update, recursively, the parameters of these clusters. These parameters are used to create detection signals and health indicators used for diagnosis. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CDC.2013.6760779 | Decision and Control |
Keywords | Field | DocType |
electric drives,electric sensing devices,fault diagnosis,pattern clustering,power generation faults,power system measurement,power transmission (mechanical),signal detection,wind turbines,data-driven model,dynamical clustering algorithm,fault detection,fault diagnosis architecture,fault isolation,health indicator,physical model,sensor measurement,signal detection,wind turbine drive train fault | Stuck-at fault,Data mining,Fault coverage,Detection theory,Control theory,Computer science,Control engineering,Turbine,Cluster analysis,Drivetrain,Wind power,Fault indicator | Conference |
ISSN | ISBN | Citations |
0743-1546 | 978-1-4673-5714-2 | 2 |
PageRank | References | Authors |
0.47 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antoine Chammas | 1 | 2 | 0.47 |
| Eric Duviella | 2 | 23 | 11.69 |
| Stéphane Lecoeuche | 3 | 16 | 2.45 |