Title
Multiple imputation of missing residuals for fault classification: A wind turbine application
Document Type
Conference Proceeding
Publication Date
3-2-2016
Publication Title
Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
First Page
677
Keywords
Fault diagnosis, Multiple imputation, Sensor faults, Wind turbine
Last Page
680
Abstract
Handling the missing data is considered as a crucial requirement for the performance of diagnostic systems. In the proposed diagnostic system, the preprocessing module receives sets of residuals generated by a combined set of observers, and feeds the proceeded residuals to a fault classification module. It is necessary for the fault classification module to receive complete feature sets. Multiple missing data imputation techniques have been devised in the preprocessing module to guarantee feeding complete sets of features to the fault classification module. The proposed diagnostic scheme is validated using incomplete batch of residuals for sensor fault diagnosis in a doubly fed induction generator (DFIG) of a wind turbine.
DOI
10.1109/ICMLA.2015.145
ISBN
9781509002870
Recommended Citation
Nejad, Eman M.; Razavi-Far, Roozbeh; Wu, Q. M.Jonathan; and Saif, Mehrdad. (2016). Multiple imputation of missing residuals for fault classification: A wind turbine application. Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015, 677-680.
https://scholar.uwindsor.ca/electricalengpub/161