Failure Diagnosis of Wind Turbine Bearing Using Feature Extraction and a Neuro-Fuzzy Inference System (ANFIS)

Document Type

Conference Proceeding

Publication Date

1-1-2019

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

11506 LNCS

First Page

545

Keywords

Failure diagnosis, Feature extraction, Neuro fuzzy

Last Page

556

Abstract

Bearing failures are the most common type of malfunction in wind turbines. As such, isolating these defects enables maintenance scheduling in advance; hence, preventing further damage to turbines. This paper introduces a new fault detection and diagnosis (FDD) method to isolate two types of bearing failures in Wind turbines (WTs). The proposed FDD method consists of a feature extraction/feature selection and an adaptive neuro-fuzzy inference system (ANFIS) method. The feature extraction and selection phase identifies proper features to capture the nonlinear dynamics of the failure. Then, the ANFIS classifier diagnoses the failure type using the extracted features. Several experimental test studies with the historical data of wind farms in South-western Ontario are performed to evaluate the performance of the FDD system. Test results indicate that the proposed monitoring system is accurate and effective.

DOI

10.1007/978-3-030-20521-8_45

ISSN

03029743

E-ISSN

16113349

ISBN

9783030205201

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