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.


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.

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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 > QUT Faculties and Divisions > Science & Engineering Faculty
Institution: Queensland University of Technology
Deposited On: 16 May 2014 04:30
Last Modified: 21 Jun 2017 02:01

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