mlsanityData to 7 Oct 2026

Scientometric Review of Artificial Intelligence for Operations & Maintenance of Wind Turbines: The Past, Present and Future

arXiv 2204.02360 ↗First posted 30 Mar 2022cs.AIcited by 4

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Abstract (arXiv)

Wind energy has emerged as a highly promising source of renewable energy in recent times. However, wind turbines regularly suffer from operational inconsistencies, leading to significant costs and challenges in operations and maintenance (O&M). Condition-based monitoring (CBM) and performance assessment/analysis of turbines are vital aspects for ensuring efficient O&M planning and cost minimisation. Data-driven decision making techniques have witnessed rapid evolution in the wind industry for such O&M tasks during the last decade, from applying signal processing methods in early 2010 to artificial intelligence (AI) techniques, especially deep learning in 2020. In this article, we utilise statistical computing to present a scientometric review of the conceptual and thematic evolution of AI in the wind energy sector, providing evidence-based insights into present strengths and limitations of data-driven decision making in the wind industry. We provide a perspective into the future and on current key challenges in data availability and quality, lack of transparency in black box-natured AI models, and prevailing issues in deploying models for real-time decision support, along with possible strategies to overcome these problems. We hope that a systematic analysis of the past, present and future of CBM and performance assessment can encourage more organisations to adopt data-driven decision making techniques in O&M towards making wind energy sources more reliable, contributing to the global efforts of tackling climate change.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 28 of its 211 references that are in the map. First posted on arXiv 30 Mar 2022.

Builds on

28 of 211 references.

References · 28

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  5. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  6. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  7. 2019Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelismcited by 1,829
  8. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  9. 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,024
  10. 2017Deep Reinforcement Learning: An Overviewcited by 327
  11. 2019A Multiscale Visualization of Attention in the Transformer Modelcited by 292
  12. 2019Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019cited by 270
16 more
  1. 2015MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systemscited by 195
  2. 2019Deep learning with noisy labels: exploring techniques and remedies in medical image analysiscited by 173
  3. 2018A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspectivecited by 111
  4. 2018The Blessings of Multiple Causescited by 103
  5. 2017MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networkscited by 92
  6. 2019Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerializationcited by 90
  7. 2018On the Impact of Various Types of Noise on Neural Machine Translationcited by 76
  8. 2019Towards Explainable Deep Neural Networks (xDNN)cited by 61
  9. 2019Few-Shot NLG with Pre-Trained Language Modelcited by 52
  10. 2019Benchmarking TPU, GPU, and CPU Platforms for Deep Learningcited by 51
  11. 2019PruneTrain: Fast Neural Network Training by Dynamic Sparse Model Reconfigurationcited by 41
  12. 2018A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generationcited by 20
  13. 2019Memory-Efficient Adaptive Optimizationcited by 19
  14. 2017Real-time predictive maintenance for wind turbines using Big Data frameworkscited by 7
  15. 2019Spatiotemporal Attention Networks for Wind Power Forecastingcited by 3
  16. 2018Impact of Corpora Quality on Neural Machine Translationcited by 1

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 1.

4 papers.

  1. 2023An XAI framework for robust and transparent data-driven wind turbine power curve modelscited by 4
  2. 2024Transfer learning applications for anomaly detection in wind turbinescited by 1
  3. 2022XAI for transparent wind turbine power curve modelscited by 0
  4. 2023A Novel Correlation-optimized Deep Learning Method for Wind Speed Forecastcited by 0