Document Type

Article

Publication Date

10-15-2014

Publication Title

Expert Systems with Applications

Volume

41

Issue

14

First Page

6386

Keywords

Fault diagnosis, Latent residuals, New class faults, NIPALS, Wind turbine, Wold cross-validation

Last Page

6399

Abstract

This paper focuses on the development of a pre-processing module to generate the latent residuals for sensor fault diagnosis in a doubly fed induction generator of a wind turbine. The pre-processing module bridges a gap between the residual generation and decision modules. The inputs of the pre-processing module are batches of residuals generated by a combined set of observers that are robust to operating point changes. The outputs of the pre-processing module are the latent residuals which are progressively fed into the decision module, a dynamic weighting ensemble of fault classifiers that incrementally learns the residuals-faults relationships and dynamically classifies the faults including multiple new classes. The pre-processing module consists of the Wold cross-validation algorithm along with the non-linear iterative partial least squares (NIPALS) that projects the residual to the new feature space, extracts the latent information among the residuals and estimates the optimal number of principal components to form the latent residuals. Simulation results confirm the effectiveness of this approach, even in the incomplete scenarios, i.e.; the missing data in the batches of generated residuals due to sensor failures. © 2014 Elsevier Ltd. All rights reserved.

DOI

10.1016/j.eswa.2014.03.056

ISSN

09574174

Download

Share

COinS