Transfer learning and direct probability integral method based reliability analysis for offshore wind turbine blades under multi-physics coupling

Zhang, Kejia; Xiao, Yang; Fazeres-Ferradosa, Tiago; Zhu, Shun‐Peng

doi:10.1016/j.renene.2023.02.026

Cited by 9 publications

(2 citation statements)

References 57 publications

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“…A methodology for estimating the dependability of offshore wind turbine blades with high availability and cost-effectiveness was presented by Zhang et al (2023) [112] by integrating the direct probability integral method (DPIM) with transfer learning (TL). Based on the Physics-of-Failure (PoF) theory, Mulenga et al (2022) [113] presented a methodology for forecasting the emergence of a fracture in corroded structures.…”

Section: Physics Of Failure (Pof)mentioning

confidence: 99%

A review of reliability techniques for the evaluation of Programmable logic controller

Obele,

Aikhuele,

Nwosu

2024

WJEEE

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PLCs, or programmable logic controllers, are essential parts of contemporary industrial automation systems and are responsible for managing and keeping an eye on a variety of operations. PLC reliability is critical to maintaining industrial systems' continuous and secure operation. A wide range of reliability strategies were used to improve the reliability of Programmable Logic Controllers, and this article methodically looks at them all. The evaluation classified PLC reliability techniques into Root Cause Analysis (RCA), Reliability Centered Maintenance (RCM), Hazard analysis (HA), Reliability block diagram (RBD), Fault tree analysis (FTA), Physics of failure (PoF) and FMEA/FMECA, after thoroughly reviewing the body of literature. The proportion of reviewed papers using either RCA, RCM, FMEA/FMECA, FTA, RBD, RCM, PoF, or Hazard analysis to increase the reliability of PLCs showed that RCA, which makes up 20% of the publications reviewed, has been used the most to increase the reliability of the PLC, followed by HA, RCM, RBD, FTA, and PoF, which account for 17%, 16%, 16%,13%, 10%, and 8% of the articles reviewed, respectively. The paper discusses new developments and trends in PLC reliability, such as the application of machine learning (ML) and artificial intelligence (AI) to fault detection and predictive maintenance.

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Section: Physics Of Failure (Pof)mentioning

confidence: 99%

A review of reliability techniques for the evaluation of Programmable logic controller

Obele,

Aikhuele,

Nwosu

2024

WJEEE

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“…Liu et al combined meta-learning and contrast learning to realize few shot fault diagnosis of wind turbines [19]. Zhang et al used TL to realize reliability estimation for offshore wind turbine blade [20]. Li et al brought forward a cross-attribute adaptation networks to realize WTs' fault transfer [21].…”

Section: Introductionmentioning

confidence: 99%

A novel multi-task transfer model to realize unsupervised fault diagnosis of newly constructed wind turbines under variable conditions

liu,

Sha,

Zhang

et al. 2024

Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023)

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Fault diagnosis can effectively improve the power generation of the wind turbines. Deep learning has promoted the intelligent development of wind turbine fault diagnosis. Traditional deep learning usually requires a sufficient amount of labeled data. However, for newly constructed wind turbines, there are problems such as insufficient samples, limited labels, variable operating conditions. Transfer learning provides a new way to solve these problems. Establishing appropriate models to reduce the distribution differences between existing and newly built units is the key to improving unsupervised fault diagnosis accuracy for newly built units. To address these challenges, a novel multi-task transfer model based on improved model agnostic meta learning (MT-TL-MAML) was proposed to realize unsupervised fault diagnosis of newly constructed wind turbines. The proposed model utilizes the advantages of MAML in generalization of new tasks and small sample diagnosis, and can adapt to randomly changing working conditions. By means of iterative learning, the gap between source domain and target domain is further narrowed, and the classifier can realize more accurate diagnosis of target domain data. This article takes the SCADA and CMS data of two wind farms as case studies to conduct unsupervised fault diagnosis and compare it with other literatures. The results validate the advantages of the proposed model in unsupervised fault diagnosis of newly constructed wind turbines.

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A probabilistic fatigue life assessment method for wind turbine blade based on Bayesian GPR with the effects of pitch angle

Zhang,

Chen

2024

Quality & Reliability Eng

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The fatigue behavior of large wind turbine blades is complex and stochastic due to their complex structure and operating environment. This paper focuses on developing a probabilistic fatigue life assessment method for wind turbine blades considering the uncertainties from wind velocity, material mechanical properties, pitch angle, and layer thickness. To improve the efficiency of stochastic fatigue behavior analysis of wind turbine blade, unidirectional fluid‐structure coupling (UFSC) and bidirectional fluid‐structure coupling (BFSC) analysis are employed to analyze the stochastic response. Then, Gaussian process regression (GPR) and Bayesian updating are combined to establish the stochastic fatigue behavior prediction model for wind turbine blade. On this basis, a modified S‐N curve formulation is proposed, and the fatigue life of wind turbine blade is analyzed by the modified S‐N curve and compared with the three‐parameter Weibull model. The results indicate that the proposed method for fatigue life assessment has better accuracy. The proposed probabilistic fatigue life assessment method with high accuracy and high efficiency, which is beneficial for the fatigue reliability design of wind turbine blades.

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Transfer learning and direct probability integral method based reliability analysis for offshore wind turbine blades under multi-physics coupling

Cited by 9 publications

References 57 publications

A review of reliability techniques for the evaluation of Programmable logic controller

A review of reliability techniques for the evaluation of Programmable logic controller

A novel multi-task transfer model to realize unsupervised fault diagnosis of newly constructed wind turbines under variable conditions

A probabilistic fatigue life assessment method for wind turbine blade based on Bayesian GPR with the effects of pitch angle

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