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A Systematic Framework for Unsupervised Feature Mining and Fault Detection for Wind Turbine Drivetrain Systems

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2016, MS, University of Cincinnati, Engineering and Applied Science: Mechanical Engineering.
The global installed capacity of wind turbines has been growing rapidly during the past decade. Along with the fast-growing number of wind turbines, the concerns for their maintenance and health management are also accumulating. The repair and maintenance for wind turbines are very expensive and time-consuming due to various reasons including logistics difficulties, distant locations, costly spare parts, and expensive labor force, etc. Prognostics and health management (PHM) technologies are of vital importance to wind turbines operation and maintenance since it can detect incipient faults in early time and predict the trend of their propagation so that the maintenance activities can be planned ahead of time to reduce the downtime and maintenance cost. Drivetrain systems are of the most concern in maintenance as they contribute the most downtime and repair costs. While there are various condition monitoring techniques available for drivetrain systems, vibration-based techniques have been most widely adopted due to its direct access to structure response and capability for early detection of incipient faults. However, there are impeding challenges of its application in wind turbine PHM: how to extract meaningful features from vibration signal when the rotating speed is unknown; how to detect and enhance incipient fault features under dynamic operation regimes and harsh environment; how to convert the multidimensional feature vectors into actionable health indicator to plan maintenance; and how to align these analytical techniques to enable smart, self-contained and unsupervised condition monitoring systems in big data environment. This thesis presents a systematic framework for unsupervised feature mining and fault detection for drivetrain systems. It consists several novel techniques that address the critical issues for vibration-based condition monitoring: A novel method for instantaneous angular speed estimation under non-stationary operation conditions based on enhanced harmonics product spectrum; Resonance band detection and incipient fault feature enhancement based on harmonics-targeting fast kurtogram; And a fleet-based data-driven fault detection method based on clustering and peer-to-fleet similarity assessment techniques. The contributions of the proposed framework to conventional vibration-based monitoring on drivetrain systems are mainly in two aspects, i.e., it can autonomously configure the signal processing techniques according to the signal characters and drivetrain design scheme, which makes the feature extraction process unsupervised and self-contained; the proposed fault detection modeling paradigm enables fleet level prognosis that has no requirement for context information of the health status of historical data. The proposed framework is validated with two case studies, a full-scale wind turbine drivetrain test bed to validate the autonomous fault feature mining techniques, and a wind farm consisting of 48 wind turbines for fleet-based data-driven fault detection.
Jay Lee, Ph.D. (Committee Chair)
Jay Kim, Ph.D. (Committee Member)
Allyn Phillips, Ph.D. (Committee Member)
110 p.

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Citations

  • Liu, Z. (2016). A Systematic Framework for Unsupervised Feature Mining and Fault Detection for Wind Turbine Drivetrain Systems [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1471348052

    APA Style (7th edition)

  • Liu, Zongchang. A Systematic Framework for Unsupervised Feature Mining and Fault Detection for Wind Turbine Drivetrain Systems. 2016. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1471348052.

    MLA Style (8th edition)

  • Liu, Zongchang. "A Systematic Framework for Unsupervised Feature Mining and Fault Detection for Wind Turbine Drivetrain Systems." Master's thesis, University of Cincinnati, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1471348052

    Chicago Manual of Style (17th edition)