Application of machine learning technique in wind turbine fault diagnosis

Purarjomandlangrudi, Afrooz (2014) Application of machine learning technique in wind turbine fault diagnosis. Masters by Research by Publication, Queensland University of Technology.

Abstract

In this study, a machine learning technique called anomaly detection is employed for wind turbine bearing fault detection. Basically, the anomaly detection algorithm is used to recognize the presence of unusual and potentially faulty data in a dataset, which contains two phases: a training phase and a testing phase. Two bearing datasets were used to validate the proposed technique, fault-seeded bearing from a test rig located at Case Western Reserve University to validate the accuracy of the anomaly detection method, and a test to failure data of bearings from the NSF I/UCR Center for Intelligent Maintenance Systems (IMS). The latter data set was used to compare anomaly detection with SVM, a previously well-known applied method, in rapidly finding the incipient faults.

Impact and interest:

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

651 since deposited on 16 May 2014
293 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 70624
Item Type: QUT Thesis (Masters by Research by Publication)
Supervisor: Nourbakhsh, Ghavameddin, Tan, Andy, & Mishra, Yateendra
Keywords: Wind turbine, condition monitoring, bearing, machine learning, anomaly detection
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Institution: Queensland University of Technology
Deposited On: 16 May 2014 04:30
Last Modified: 09 Sep 2015 06:04

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page