Artificial intelligence-based condition monitoring and predictive maintenance framework for wind turbines

Maron, Janine; Anagnostos, Dimitrios; Brodbeck, Bernhard; Meyer, Angela

doi:10.1088/1742-6596/2151/1/012007

Cited by 10 publications

(8 citation statements)

References 7 publications

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“…This can be done by using clustering algorithms, for example, k-nearest neighbors (see (Black et al, 2022)). An alternative technique is simply removing the observations with missing data (see (Maron et al, 2022), (Miele et al, 2022), (Cui et al, 2018) and (Bangalore et al, 2017)). This can be difficult when time series modeling is used.…”

Section: Preprocessing Techniquesmentioning

confidence: 99%

“…-10-minutes: (Bermúdez et al, 2022), (Black et al, 2022), (Campoverde et al, 2022), (Chesterman et al, 2022), (Maron et al, 2022), (Mazidi et al, 2017), (Miele et al, 2022), (Peter et al, 2022), (Takanashi et al, 2022), (Beretta et al, 2021), (Beretta M. and J., 2020), (Catellani et al, 2021), (Chen et al, 2021), (Chesterman et al, 2021), (Meyer, 2021), (Turnbull et al, 2021), (Udo and Yar, 2021), (Beretta M. and J., 2020), (Liu et al, 2020), (McKinnon et al, 2020, (Renström et al, 2020), (Zhao et al, 2018), (Bangalore et al, 2017), (Dienst and Beseler, 2016), (Bangalore and Tjernberg, 2015), https://doi.org/10.5194/wes-2022-120 Preprint. Discussion started: 21 February 2023 c Author(s) 2023.…”

Section: The Data and Signalsmentioning

confidence: 99%

“…However, they require in general more training data and training time. Examples of algorithms that are used in the current state-of-the-art are tree-based models like random forest (see (Chesterman et al, 2022), (Turnbull et al, 2021), (Kusiak and Li, 2011)) and gradient boosting (see (Chesterman et al, 2022), (Maron et al, 2022), (Beretta et al, 2021), (Udo and Yar, 2021), (Beretta M. and J., 2020), https://doi.org/10.5194/wes-2022-120 Preprint. Discussion started: 21 February 2023 c Author(s) 2023.…”

Section: Normal Behavior Modeling Algorithmsmentioning

confidence: 99%

“…If they clearly outperform the elastic net model then that is evidence for the existence of non-linear relations. The performance of the elastic net will be compared with that of an SVR with a radial kernel (also used in (Catellani et al, 2021)) and a light GBM (which is similar to the Gradient Boosting algorithms used in (Udo and Yar, 2021), (Maron et al, 2022) and (Beretta et al, 2021)).…”

Section: The Experimentsmentioning

confidence: 99%

See 3 more Smart Citations

Overview of normal behavior modeling approaches for SCADA-based wind turbine condition monitoring demonstrated ondata from operational wind farms

Chesterman

Verstraeten

Daems

et al. 2023

Preprint

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Abstract. Condition monitoring and failure prediction for wind turbines is currently a hot research topic. This follows from the fact that investments in the wind energy sector have increased dramatically due to the transition to renewable energy production. This paper reviews and implements several techniques from state-of-the-art research on condition monitoring for wind turbines using SCADA data and the Normal Behavior Modelling framework. The first part of the paper consists of an in-depth overview of 5 the current state-of-the-art. In the second part, several techniques from the overview are implemented and compared using data (SCADA and failure data) from five operational wind farms. To this end, 6 demonstration experiments are designed. The first 5 experiments test different techniques for the modeling of the normal behavior. The sixth experiment compares several techniques that can be used for identifying anomalous patterns in the prediction error. The paper concludes with several directions for future work.

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Section: Preprocessing Techniquesmentioning

confidence: 99%

Section: The Data and Signalsmentioning

confidence: 99%

Section: Normal Behavior Modeling Algorithmsmentioning

confidence: 99%

Section: The Experimentsmentioning

confidence: 99%

See 2 more Smart Citations

Overview of normal behavior modeling approaches for SCADA-based wind turbine condition monitoring demonstrated ondata from operational wind farms

Chesterman

Verstraeten

Daems

et al. 2023

Preprint

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“…WinJi was looking to learn from the challenge in order to further their knowledge indirectly. Their current method is based on a Normal Behaviour Model and is described further in [28]. Fig.…”

mentioning

confidence: 99%

Best Practice Data Sharing Guidelines for Wind Turbine Fault Detection Model Evaluation

Barber¹,

Aizpitarte²,

Serradilla³

et al. 2023

Preprint

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The digital era offers many opportunities to the wind energy industry and research community. Digitalisation is one of the key drivers for reducing costs and risks over the whole wind energy project life cycle. One of the largest challenges in successfully implementing digitalisation is the lack of data sharing and collaboration between organisations in the sector. In order to overcome this challenge, a new collaboration method called WeDoWind was developed in recent work. The main innovation of this method is the way it creates tangible incentives to motivate and empower different types of people from all over the world to actually share data and knowledge in practice. In this present paper, the challenges related to comparing and evaluating different SCADA data based wind turbine fault detection models are investigated by carrying out a new case study, the "WinJi Gearbox Fault Detection Challenge", based on the WeDoWind Method. Six new solutions were submitted to the challenge, and a comparison and evaluation of the results show that, in general, some of the approaches (Particle Swarm Optimisation algorithm for constructing health indicators, performance monitoring using Deep Neural Networks, Combined Ward Hierarchical Clustering and Novelty Detection with Local Outlier Factor and Time-to-failure prediction using Random Forest Regression) appear to have a high potential to reach the goals of the Challenge. However, there are a number of concrete things that would have to have been done by the Challenge providers and the Challenge moderators in order to ensure success. This includes enabling access to more details of the different failure types, access to multiple data sets from more wind turbines experiencing gearbox failure, provision of a model or rule relating fault detection times or a remaining useful lifetime to the estimated costs for repairs, replacements and inspections, provision of a clear strategy for training and test periods in advance, as well as provision of a pre-defined template or requirements for the results. These learning outcomes are used directly to define a set of best practice data sharing guidelines for wind turbine fault detection model evaluation. They can be used by the sector in order to improve model evaluation and data sharing in the future.

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Reconstruction-Based Anomaly Detection in Wind Turbine Operation Time Series Using Generative Models

Abanda,

Pujana,

Del Ser

2024

Lecture Notes in Computer Science

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Artificial intelligence-based condition monitoring and predictive maintenance framework for wind turbines

Cited by 10 publications

References 7 publications

Overview of normal behavior modeling approaches for SCADA-based wind turbine condition monitoring demonstrated ondata from operational wind farms

Overview of normal behavior modeling approaches for SCADA-based wind turbine condition monitoring demonstrated ondata from operational wind farms

Best Practice Data Sharing Guidelines for Wind Turbine Fault Detection Model Evaluation

Reconstruction-Based Anomaly Detection in Wind Turbine Operation Time Series Using Generative Models

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