A Data-Centric Machine Learning Methodology: Application on Predictive Maintenance of Wind Turbines

Garan, Maryna; Tidriri, Khaoula; Kovalenko, Iaroslav

doi:10.3390/en15030826

Cited by 26 publications

(14 citation statements)

References 24 publications

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“…There was no DT or any feedback mechanism in this work [ 15 ]. Another work [ 16 ] applied a data-driven approach (decision trees) with a focus on the data pre-processing using hyper-parameter tuning to detect failures from five components of a wind turbine, mainly the generator, hydraulic, generator bearing, transformer, and gearbox. They showed how a good pre-processing strategy in data-driven models can outperform a model-driven approach for PdM.…”

Section: Literature Reviewmentioning

confidence: 99%

Towards a Distributed Digital Twin Framework for Predictive Maintenance in Industrial Internet of Things (IIoT)

Abdullahi,

Longo,

Samie

2024

Sensors

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This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.

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Section: Literature Reviewmentioning

confidence: 99%

Towards a Distributed Digital Twin Framework for Predictive Maintenance in Industrial Internet of Things (IIoT)

Abdullahi,

Longo,

Samie

2024

Sensors

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“…Big data are promising for modeling wind energy, including wind speed forecasting [ 11 , 12 ], power prediction and optimization [ 12 ], power curve monitoring [ 13 ], and predictive maintenance [ 14 , 15 ]. Appropriate use of condition monitoring (CM) can reduce turbine repair and maintenance costs by detecting faults at an early stage [ 2 ].…”

Section: State Of the Artmentioning

confidence: 99%

Exploring the Limits of Early Predictive Maintenance in Wind Turbines Applying an Anomaly Detection Technique

Jankauskas

Serackis

Šapurov

et al. 2023

Sensors

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The aim of the presented investigation is to explore the time gap between an anomaly appearance in continuously measured parameters of the device and a failure, related to the end of the remaining resource of the device-critical component. In this investigation, we propose a recurrent neural network to model the time series of the parameters of the healthy device to detect anomalies by comparing the predicted values with the ones actually measured. An experimental investigation was performed on SCADA estimates received from different wind turbines with failures. A recurrent neural network was used to predict the temperature of the gearbox. The comparison of the predicted temperature values and the actual measured ones showed that anomalies in the gearbox temperature could be detected up to 37 days before the failure of the device-critical component. The performed investigation compared different models that can be used for temperature time-series modeling and the influence of selected input features on the performance of temperature anomaly detection.

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“…The probability is calculated through Fault Tree Analysis and Binary Decision Diagrams to reduce computational costs. Garan et al [51] utilized reinforcement learning methods, utilizing information provided by the system to optimize wind turbine operation and maintenance schedules. They compared it with common strategies such as predictive and planned maintenance, finding that the use of reinforcement learning methods results in higher average profits.…”

Section: Literature Reviewmentioning

confidence: 99%

Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting

Zhou,

Ke,

Zhu

et al. 2023

Sustainability

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Offshore wind farms are becoming a pivotal solution to address the increasing energy demand worldwide and reduce carbon emissions to achieve a sustainable energy sector. Considering the higher operational and maintenance cost of offshore wind farms, it is important to make a good maintenance plan to guarantee the system’s reliability and reduce the total cost related to maintenance activities at the same time. Because maintenance planning is a long-term decision problem and the wind force is random, long-term wind force prediction is needed to help managers evaluate the loss caused by maintenances to be executed in the future. However, long-term wind force prediction is naturally complicated, which is much harder than the short-term (e.g., day-ahead) prediction widely investigated in the literature. In order to overcome this difficulty, we design a deep learning framework combining variational mode decomposition, a convolution neural network, long short-term memory network, and full-connected network. Using the public data from the city of Leeds, the prediction accuracy of the above framework is validated by comparing it with other prediction techniques. Then, the predicted wind force is input into the established optimization model determining preventive maintenances during a predefined period. Because the uncertainty of wind force is replaced by the prediction value, the optimization model can be established as a mixed-integer linear programing model, which only contains limited variables and can be solved quickly. Lastly, an abundance of numerical experiments are conducted to validate the effectiveness of the proposed optimization model, based on which some managerial insights are provided to the managers of offshore wind farms about the optimal operations and maintenance strategy. The research outcome will greatly promote the development of the wind power industry in the future.

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A Data-Centric Machine Learning Methodology: Application on Predictive Maintenance of Wind Turbines

Cited by 26 publications

References 24 publications

Towards a Distributed Digital Twin Framework for Predictive Maintenance in Industrial Internet of Things (IIoT)

Towards a Distributed Digital Twin Framework for Predictive Maintenance in Industrial Internet of Things (IIoT)

Exploring the Limits of Early Predictive Maintenance in Wind Turbines Applying an Anomaly Detection Technique

Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting

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