Incremental design of a decision system for residual evaluation: A wind turbine application
IFAC Proceedings Volumes (IFAC-PapersOnline)
Classifiers, Fault diagnosis, Sensor faults, State observers, Wind turbine
This paper presents an incremental way to design the decision module of a diagnostic system by resorting to dynamic weighting ensembles of classifiers. The method is applied for sensor fault detection and isolation in a doubly fed induction generator for a wind turbine application. A bank of observers generates a set of residuals. These signals are progressively fed into a dynamic weighting ensembles algorithm, called Learn++NC, for fault classification. The proposed algorithm incrementally learns the residuals-faults relationships and classifies the faults including multiple new classes, based on a dynamically weighted consult and vote mechanism that combines the outputs of the base-classifiers of the ensemble. © 2012 IFAC.
Razavi-Far, Roozbeh and Kinnaert, Michel. (2012). Incremental design of a decision system for residual evaluation: A wind turbine application. IFAC Proceedings Volumes (IFAC-PapersOnline), 8 (PART 1), 343-348.