Maintenance 4.0 of Wind Turbine

Hammami, Ahmed; Djemal, Fathi; Hmida, Atef; Chaari, Fakher; Haddar, Mohamed

doi:10.1007/978-3-030-46729-6_1

Cited by 2 publications

(2 citation statements)

References 30 publications

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“…Ref. [144] applied the concept of Maintenance 4.0 to wind turbines and proposed an implementation procedure that combines reliability modeling, algorithm design, real-time performance monitoring, failure prediction, and preventive task prescription. For reliability modeling, a robust two-layer programming method was proposed, which fully considers the influence of prediction errors on multi-source system planning schemes [145].…”

Section: Preventive Maintenance Strategymentioning

confidence: 99%

Comprehensive Analysis and Evaluation of the Operation and Maintenance of Offshore Wind Power Systems: A Survey

Yang

Jia

et al. 2023

Energies

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Offshore Wind Power Systems (OWPS) offer great energy and environmental advantages, but also pose significant Operation and Maintenance (O&M) challenges. In this survey, we analyze these challenges and propose some optimization strategies and technologies for OWPS comprehensively. The existing literature review mainly focuses on a certain field of offshore wind power O&M, but lacks a comprehensive introduction to offshore wind power. We consider the energy efficiency, reliability, safety, and economy of OWPS from various aspects, such as offshore wind and wave energy utilization, offshore wind turbine components, and wind power operation parameters, and compare them with onshore wind power systems. We suggest that OWPS can benefit from advanced design optimization, digital twin, monitoring and forecasting, fault diagnosis, and other technologies to enhance their O&M performance. This paper aims to provide theoretical guidance and practical reference for the technological innovation and sustainable development of OWPS.

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Section: Preventive Maintenance Strategymentioning

confidence: 99%

Comprehensive Analysis and Evaluation of the Operation and Maintenance of Offshore Wind Power Systems: A Survey

Yang

Jia

et al. 2023

Energies

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“…Selected References in Literature Following Local Clouds for Wind Farm Monitoring CCL 2021 [14] 2021 [24] 2019 [18] 2019 [11] 2019 [12] 2018 [8] 2018 […”

Section: Paradigmsmentioning

confidence: 99%

Cloud-based Collaborative Learning (CCL) for the Automated Condition Monitoring of Wind Farms

Javed

Deventer

et al. 2022

2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS)

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Modeling Industrial Internet of Things (IIoT) architectures for the automation of wind turbines and farms (WT/F), as well as their condition monitoring (CM) is a growing concept among researchers. Several end-to-end automated cloudbased solutions that digitize CM operations intelligently to reduce manual efforts and costs are being developed. However, establishing robust and secure communication across WT/F is still difficult for the wind energy industry. We propose a fully automated cloud-based collaborative learning (CCL) architecture using the Eclipse Arrowhead Framework and an unsupervised dictionary learning (USDL) CM approach. The scalability of the framework enabled digitization and collaboration across the WT/Fs. Collaborative learning is a novel approach that allows all WT/Fs to learn from each other in real-time. Each turbine has CCL based CM using USDL as micro-services that autonomously perform feature selection and failure prediction to optimize cost, computation, and resources. The fundamental essence of the USDL approach is to enhance the WT/F's learning and accuracy. We use dictionary distances as a metric for analyzing the CM of WT in our proposed USDL approach. A dictionary indicates an anomaly if its distances increased from the dictionary computed at a healthy state of that WT. Using CCL, a WT/F learns all types of failures that could occur in a similar WT/F, predicts any machinery failure, and sends alerts to the technicians to ensure guaranteed proactive maintenance. The results of our research support the notion that when testing a turbine with dictionaries of all the other turbines, every dictionary converges to similar behavior and captures the fault that occurs in that turbine.

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Maintenance 4.0 of Wind Turbine

Cited by 2 publications

References 30 publications

Comprehensive Analysis and Evaluation of the Operation and Maintenance of Offshore Wind Power Systems: A Survey

Comprehensive Analysis and Evaluation of the Operation and Maintenance of Offshore Wind Power Systems: A Survey

Cloud-based Collaborative Learning (CCL) for the Automated Condition Monitoring of Wind Farms

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