Application of gamma process and maintenance cost for fatigue damage of wind turbine blade

Zhang, Chizhi; Tee, Kong Fah

doi:10.1016/j.egypro.2019.01.884

Cited by 15 publications

(9 citation statements)

References 3 publications

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“…S-N curve is combined with Bootstrapping to analyze the fatigue in the monopile [93]. Zhang and Tee build a failure prediction model for blade fatigue damage using BEMT, Goodman diagram, and S-N curve [83]. Paris law with HMM can predict the crack state in blades [82].…”

Section: Hybrid Modelmentioning

confidence: 99%

A review of failure prognostics for predictive maintenance of offshore wind turbines

Zhang

Vatn

Rasheed³

2022

J. Phys.: Conf. Ser.

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Offshore wind turbines (OWTs) are important facilities for wind power generation because of their low land use and high electricity output. However, the harsh environment and remote location of offshore sites make it difficult to conduct maintenance on turbines. To upkeep OWTs cost-effectively, predictive maintenance (PdM) is an appealing strategy for offshore wind industry. The heart of PdM is failure prognostics, which aims to predict an asset’s remaining useful life (RUL) based on condition monitoring (CM). To provide references to PdM of OWTs, this paper presents a systematic review of failure prognostic models for wind turbines. In this review, data-driven models, model-based models, and hybrid models are classified and presented for model selection. The findings reveal that it is promising to develop hybrid models in the future and combine the advantages of data-driven and model-based models. Currently, the internal combinations of machine learning methods and statistical approaches in data-driven models are more common than exterior linkages between data-driven models and model-based models. The limitations and strengths of different models are discussed, and opportunities for developing hybrid models are highlighted in the conclusion.

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Section: Hybrid Modelmentioning

confidence: 99%

A review of failure prognostics for predictive maintenance of offshore wind turbines

Zhang

Vatn

Rasheed³

2022

J. Phys.: Conf. Ser.

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“…Zhang et al proposed a fatigue prediction model of the blade to reproduce the fatigue damage evolution in the composite blades subjected to aerodynamic loadings by cyclical winds. The lifetime probability of fatigue failure of the blades was then investigated by stochastic deterioration modeling, and a cost benefit model was finally built to optimize the maintenance cost [163]. Zhu et al investigated new importance measures of evaluating the maintenance values of WT components in terms of increasing the mean of RUL and mean residual system profit over the RUL.…”

Section: Remaining Useful Life Estimationmentioning

confidence: 99%

Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review

et al. 2021

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Modern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms.

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“…Thus, the gamma process is appropriate for modelling irreversible and gradual fatigue damage by continuous use of a wind turbine blade (van Noortwijk 2009). In previous studies (Chen and Alani 2013;Huang et al 2016;Zhang and Tee 2018), the gamma process has been considered as an appropriate stochastic approach to simulate the stochastic deterioration process, such as corrosion in concrete bridge and fatigue damage of wind turbine blades. Gamma process model is an appropriate approach for deterioration since it has been proved to be more versatile and increasingly used in optimal maintenance strategies.…”

Section: Introductionmentioning

confidence: 99%

Reliability-Based Lifetime Fatigue Damage Assessment of Offshore Composite Wind Turbine Blades

et al. 2021

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This paper presents a method for stochastic deterioration modelling and fatigue damage assessment for composite wind turbine blades operating in offshore environments. The fatigue damage of the composite blades is analysed and assessed based on the estimates for the applied loads along the blade span, stress analysis, fatigue crack evolution, and lifetime probability of fatigue failure. The complex stress states of the blade are mainly caused by the aerodynamic loads generated by corrected blade element momentum theory, gravity loads and centrifugal loads. The fatigue of the wind turbine blade is then investigated on the basis of the actual fatigue damage propagation process. The stochastic gamma process is introduced to calculate the probability of fatigue failure of the blade for various critical limits, and these results together with lifecycle cost analysis are employed to determine the optimum maintenance strategy. Finally, a numerical example for an NREL 5 MW wind turbine blade is adopted to demonstrate the applicability of the proposed method. The numerical results show that the proposed approach can provide a reliable tool for estimating stress states, evaluating fatigue damage, analysing lifetime fatigue failure probability and optimising repair time of the composite wind turbine blade.

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Application of gamma process and maintenance cost for fatigue damage of wind turbine blade

Cited by 15 publications

References 3 publications

A review of failure prognostics for predictive maintenance of offshore wind turbines

A review of failure prognostics for predictive maintenance of offshore wind turbines

Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review

Reliability-Based Lifetime Fatigue Damage Assessment of Offshore Composite Wind Turbine Blades

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