Framework of Designing an Adaptive and Multi-Regime Prognostics and Health Management for Wind Turbine Reliability and Efficiency Improvement

Abstract

Abstract-Wind turbine systems are increasing in technical complexity, and tasked with operating and degrading in highly dynamic and unpredictable conditions. Sustaining the reliability of such systems is a complex and difficult task. In spite of extensive efforts, current prognostics and health management (PHM) methodologies face many challenges, due to the complexity of the degradation process and the dynamic operating conditions of a wind turbine. This research proposed a novel adaptive and multi-regime prognostics and health management (PHM) approach with the aim to tackle the challenges of traditional methods. With this approach, a scientific and systematic solution is provided for health assessment, diagnosis and prognosis of critical components of wind turbines under varying environmental, operational and aging processes. The system is also capable of adaptively selecting the tools suitable for a component under a certain health status and a specific operating condition. The adopted relevant health assessment, diagnosis and prognosis tools and techniques for wind turbines are warranted by the intensive research of PHM models by the IMS center for common rotary machinery components. Some sub-procedures, such as information reconstruction, regime clustering approach and the prognostics of rotating elements, were validated by the best score performance in PHM Data Challenge 2008 (student group) and 2009 (professional group). The success of the proposed wind turbine PHM system would greatly benefit current wind turbine industry.

Construction of Wind Turbine Bearing Vibration Monitoring and Performance Assessment System

“…When possible fault of bearing is occurred, the SVM algorithm is adopted to classify and diagnose the possible fault of bearing. The relevant technique for setting up intelligent monitoring system is relatively mature and there are many successful application examples, the required technique, such as assessment, diagnosis, prediction, classification and data mining can be found in [10][11][12][13][14][15][16][17][18]. The monitoring system developed by this study will select suitable and stable algorithms to conduct the development, integration, and test analysis.…”

A new intelligent fault diagnosis and prognosis method for wind turbine doubly-fed induction generator

“…Prognosis of high-speed shaft bearings through spectral Kurtosis-derived indices and Support Vector Regression (SVR) is introduced in Saidi et al (2017). A prognosis analysis of vibration data, based on the Markov decision process, is used in Song and Lee (2013). Different methods are proposed based on oil CM to predict the gearbox faults (Dupuis, 2010; Zhu et al, 2013a, 2013b, 2015).…”

An adaptive method for health trend prediction of rotating bearings

“…Sun et al develop a state-space-based degradation model to reduce failure prognostics uncertainty [17]. Bearing degradation has great uncertainty and the dynamic degradation states have significant influence on the PHM models effectiveness [18,19]. Although there are some adaptive methods, which can adjust their modeling by changing the parameters to follow different degradation dynamics, the results are not always satisfactory under some other circumstance [20,21].…”