Application of anomaly technique in wind turbine bearing fault detection

Purarjomandlangrudi, Afrooz, Nourbakhsh, Ghavameddin, Ghaemmaghami, Houman, & Tan, Andy (2014) Application of anomaly technique in wind turbine bearing fault detection. In IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society, IEEE, Sheraton Hotel Dallas, Dallas, Texas, USA, pp. 1984-1988.

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Bearing faults are the most common cause of wind turbine failures. Unavailability and maintenance cost of wind turbines are becoming critically important, with their fast growing in electric networks. Early fault detection can reduce outage time and costs. This paper proposes Anomaly Detection (AD) machine learning algorithms for fault diagnosis of wind turbine bearings. The application of this method on a real data set was conducted and is presented in this paper. For validation and comparison purposes, a set of baseline results are produced using the popular one-class SVM methods to examine the ability of the proposed technique in detecting incipient faults.

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ID Code: 82319
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: wind turbine, bearing, fault diagnosis, machine learning, SVM, anomaly detection
DOI: 10.1109/IECON.2014.7048774
Divisions: Current > Schools > School of Chemistry, Physics & Mechanical Engineering
Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 IEEE
Copyright Statement: Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Deposited On: 08 Mar 2015 23:21
Last Modified: 11 Mar 2015 11:06

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