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Research of artificial intelligence operations for wind turbines considering anomaly detection, root cause analysis, and incremental training

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  • Zhang, Chen
  • Hu, Di
  • Yang, Tao

Abstract

Artificial intelligence operations (AIOps) is emerging as a novel technology in industrial automation to improve operation and maintenance (O&M) efficiency through machine learning methods. In this study, the AIOps for wind turbines based on SCADA data considering anomaly detection, root cause analysis, and incremental training is proposed and researched. A long short-term memory-based asymmetric variational autoencoding Gaussian mixture model (LSTM-AVAGMM) was established and used to detect early anomalies, and the threshold was set with a 99.7% confidence interval for the distribution curve fitted by kernel density estimation (KDE). Moreover, to provide guidance on O&M once an anomaly warning signal is issued, the SHapley Additive exPlanations (SHAP) was introduced to conduct root cause analysis by assigning each feature an importance value for a particular prediction. Finally, a novel incremental training strategy based on deep generative replay and fine-tuning was demonstrated to improve model adaptability in complex environments. Two real cases from a wind farm located in northeast China were presented. Comparative studies showed the robustness and competitiveness of the proposed methods in predicting failures, locating anomalies, and adaptively updating wind turbines, and the effects of various applied parameters were also described.

Suggested Citation

  • Zhang, Chen & Hu, Di & Yang, Tao, 2024. "Research of artificial intelligence operations for wind turbines considering anomaly detection, root cause analysis, and incremental training," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    • Handle: RePEc:eee:reensy:v:241:y:2024:i:c:s0951832023005483
    DOI: 10.1016/j.ress.2023.109634
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    1. Yan, Jianhai & He, Zhen & He, Shuguang, 2023. "Multitask learning of health state assessment and remaining useful life prediction for sensor-equipped machines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    2. Xiang, Ling & Yang, Xin & Hu, Aijun & Su, Hao & Wang, Penghe, 2022. "Condition monitoring and anomaly detection of wind turbine based on cascaded and bidirectional deep learning networks," Applied Energy, Elsevier, vol. 305(C).
    3. Costa, Nahuel & Sánchez, Luciano, 2022. "Variational encoding approach for interpretable assessment of remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    4. Saleh, Ali & Chiachío, Manuel & Salas, Juan Fernández & Kolios, Athanasios, 2023. "Self-adaptive optimized maintenance of offshore wind turbines by intelligent Petri nets," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    5. Baraldi, Piero & Razavi-Far, Roozbeh & Zio, Enrico, 2011. "Classifier-ensemble incremental-learning procedure for nuclear transient identification at different operational conditions," Reliability Engineering and System Safety, Elsevier, vol. 96(4), pages 480-488.
    6. Ma, Yan & Shan, Ce & Gao, Jinwu & Chen, Hong, 2023. "Multiple health indicators fusion-based health prognostic for lithium-ion battery using transfer learning and hybrid deep learning method," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    7. Liu, Junqiang & Pan, Chunlu & Lei, Fan & Hu, Dongbin & Zuo, Hongfu, 2021. "Fault prediction of bearings based on LSTM and statistical process analysis," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    8. Chi, Chia-Fen & Sigmund, Davin & Astardi, Martin Octavianus, 2020. "Classification Scheme for Root Cause and Failure Modes and Effects Analysis (FMEA) of Passenger Vehicle Recalls," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    9. Bakir, I. & Yildirim, M. & Ursavas, E., 2021. "An integrated optimization framework for multi-component predictive analytics in wind farm operations & maintenance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    10. Korkos, Panagiotis & Linjama, Matti & Kleemola, Jaakko & Lehtovaara, Arto, 2022. "Data annotation and feature extraction in fault detection in a wind turbine hydraulic pitch system," Renewable Energy, Elsevier, vol. 185(C), pages 692-703.
    11. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2022. "In-situ condition monitoring of wind turbine blades: A critical and systematic review of techniques, challenges, and futures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    12. González-Muñiz, Ana & Díaz, Ignacio & Cuadrado, Abel A. & García-Pérez, Diego, 2022. "Health indicator for machine condition monitoring built in the latent space of a deep autoencoder," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    13. Zhang, Chen & Hu, Di & Yang, Tao, 2022. "Anomaly detection and diagnosis for wind turbines using long short-term memory-based stacked denoising autoencoders and XGBoost," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    14. Coraça, Eduardo M. & Ferreira, Janito V. & Nóbrega, Eurípedes G.O., 2023. "An unsupervised structural health monitoring framework based on Variational Autoencoders and Hidden Markov Models," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    15. Yoo, Yeongmin & Jung, Ui-Jin & Han, Yong Ha & Lee, Jongsoo, 2021. "Data Augmentation-Based Prediction of System Level Performance under Model and Parameter Uncertainties: Role of Designable Generative Adversarial Networks (DGAN)," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    16. Zhou, Chengyu & Fang, Xiaolei, 2023. "A convex two-dimensional variable selection method for the root-cause diagnostics of product defects," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
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