Fatigue Reliability Analysis of Wind Turbine Drivetrain Considering Strength Degradation and Load Sharing Using Survival Signature and FTA

Li, Yao; Zhu, Caichao; Chen, X.; Tan, Jianjun

doi:10.3390/en13082108

Cited by 7 publications

(7 citation statements)

References 32 publications

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“…10. During operation, the wind turbine drivetrain suffers significant impact load, which severely influences its reliability and safety [97]. The fluctuating environmental load poses a challenge to the fatigue resistance of all elementary units of the drivetrain [98,99].…”

Section: Fatigue Reliability On Drivetrainsmentioning

confidence: 99%

Fatigue reliability of wind turbines: historical perspectives, recent developments and future prospects

et al. 2022

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Section: Fatigue Reliability On Drivetrainsmentioning

confidence: 99%

Fatigue reliability of wind turbines: historical perspectives, recent developments and future prospects

et al. 2022

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“…As one of the most popularly used lifetime degradation distribution models, the Weibull distribution has been widely studied in many real-world applications [43][44][45]. It is a versatile distribution with two parameter η and β that could fit the reasonable distribution in reliabilityengineering.…”

Section: Knowledge-based Degradation Modelmentioning

confidence: 99%

Predictive Maintenance for Remanufacturing Based on Hybrid-Driven Remaining Useful Life Prediction

et al. 2022

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Remanufacturing is an activity of the circular economy model whose purpose is to keep the high value of products and materials. As opposed to the currently employed linear economic model, remanufacturing targets the extension of products and reduces the unnecessary and wasteful use of resources. Remanufacturing, along with health status monitoring, constitutes a key element for lifetime extension and reuse of large industrial equipment. The major challenge is to determine if a machine is worth remanufacturing and when is the optimal time to perform remanufacturing. The present work proposes a new predictive maintenance framework for the remanufacturing process based on a combination of remaining useful life prediction and condition monitoring methods. A hybrid-driven approach was used to combine the advantages of the knowledge model and historical data. The proposed method has been verified on the realistic run-to-failure rolling bearing degradation dataset. The experimental results combined with visualization analysis have proven the effectiveness of the proposed method.

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“…The survival signature and fault tree analysis (FTA) were used to examine the system reliability level of wind turbine drive trains under various wind conditions in Ref. [16]. To maintain maximum power output, a doubly fed induction generator (DFIG) was chosen since it includes more than 50% of all major onshore wind power plants worldwide [17].…”

Section: Introductionmentioning

confidence: 99%

Condition Assessment and Analysis of Bearing of Doubly Fed Wind Turbines Using Machine Learning Technique

et al. 2023

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Condition monitoring of wind turbines is progressively increasing to maintain the continuity of clean energy supply to power grids. This issue is of great importance since it prevents wind turbines from failing and overheating, as most wind turbines with doubly fed induction generators (DFIG) are overheated due to faults in generator bearings. Bearing fault detection has become a main topic targeting the optimum operation, unscheduled downtime, and maintenance cost of turbine generators. Wind turbines are equipped with condition monitoring devices. However, effective and reliable fault detection still faces significant difficulties. As the majority of health monitoring techniques are primarily focused on a single operating condition, they are unable to effectively determine the health condition of turbines, which results in unwanted downtimes. New and reliable strategies for data analysis were incorporated into this research, given the large amount and variety of data. The development of a new model of the temperature of the DFIG bearing versus wind speed to identify false alarms is the key innovation of this work. This research aims to analyze the parameters for condition monitoring of DFIG bearings using SCADA data for k-means clustering training. The variables of k are obtained by the elbow method that revealed three classes of k (k = 0, 1, and 2). Box plot visualization is used to quantify data points. The average rotation speed and average temperature measurement of the DFIG bearings are found to be primary indicators to characterize normal or irregular operating conditions. In order to evaluate the performance of the clustering model, an analysis of the assessment indices is also executed. The ultimate goal of the study is to be able to use SCADA-recorded data to provide advance warning of failures or performance issues.

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Fatigue Reliability Analysis of Wind Turbine Drivetrain Considering Strength Degradation and Load Sharing Using Survival Signature and FTA

Cited by 7 publications

References 32 publications

Fatigue reliability of wind turbines: historical perspectives, recent developments and future prospects

Fatigue reliability of wind turbines: historical perspectives, recent developments and future prospects

Predictive Maintenance for Remanufacturing Based on Hybrid-Driven Remaining Useful Life Prediction

Condition Assessment and Analysis of Bearing of Doubly Fed Wind Turbines Using Machine Learning Technique

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