Fault Diagnosis and Prediction of New Energy Equipment Based on Large Models

Baoqiang Li

Abstract


With the increasing demand for stability in new energy equipment operations, this paper proposes a dynamic feature extraction and fault prediction algorithm (A-GAN-FP) that integrates the attention mechanism and the generative adversarial network (GAN) for efficient fault diagnosis and prediction. Leveraging the attention mechanism, the algorithm adaptively captures key temporal-spatial features in high-dimensional, non-stationary operation data of new energy equipment. The GAN module enhances feature variability and representativeness through adversarial training, addressing data complexity and class imbalance. Experiments on real wind farm data (covering 100,000 samples across normal/gearbox/generator fault conditions) demonstrate that A-GAN-FP achieves 96.5% fault diagnosis accuracy (15.2%/12.8% improvements over SVM/BP neural networks) and 20–30% RMSE reduction in fault prediction, with an average warning time extension of 2.5 hours.


Full Text:

PDF

References


Liu, H., Song, X., & Zhang, F. (2021). Fault diagnosis of new energy vehicles based on improved machine learning. Soft Computing, 25(18), 12091–12106. https://doi.org/10.1007/s00500-021-05860-9

Xia, M., Shao, H., Ma, X., & De Silva, C. W. (2021). A stacked GRU-RNN-based approach for predicting renewable energy and electricity load for smart grid operation. IEEE Transactions on Industrial Informatics, 17(10), 7050–7059. https://doi.org/10.1109/TII.2021.3056867

Filcek, G., & Miroforidis, J. (2024). A General Framework for Providing Interval Representations of Pareto Optimal Outcomes for Large-Scale Bi- and Tri-Criteria MIP Problems. Informatica, 35(2), 255-282. https://doi.org/10.15388/24-INFOR549

Lu, S., Lu, J., An, K., Wang, X., & He, Q. (2023). Edge computing on IoT for machine signal processing and fault diagnosis: A review. IEEE Internet of Things Journal, 10(13), 11093–11116. https://doi.org/10.1109/JIOT.2023.3239944

Fanjiang, Y., Lee, C., Du, Y., & Horng, S. (2021). Palm Vein Recognition Based on Convolutional Neural Network. Informatica, 32(4), 687-708. https://doi.org/10.15388/21-INFOR462

Zhang, W., Hao, H., & Zhang, Y. (2024). State of charge prediction of lithium-ion batteries for electric aircraft with Swin transformer. IEEE/CAA Journal of Automatica Sinica, 12(3), 645–647. https://doi.org/10.1109/JAS.2023.124020

Fernandes, M., Corchado, J. M., & Marreiros, G. (2022). Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: A systematic literature review. Applied Intelligence, 52(12), 14246–14280. https://doi.org/10.1007/s10489-022-03344-3

Lang, W., Hu, Y., Gong, C., Zhang, X., Xu, H., & Deng, J. (2021). Artificial intelligence-based technique for fault detection and diagnosis of EV motors: A review. IEEE Transactions on Transportation Electrification, 8(1), 384–406. https://doi.org/10.1109/TTE.2021.3110318

Tao, H., Qiu, J., Chen, Y., Stojanovic, V., & Cheng, L. (2023). Unsupervised cross-domain rolling bearing fault diagnosis based on time-frequency information fusion. Journal of the Franklin Institute, 360(2), 1454–1477. https://doi.org/10.1016/j.jfranklin.2022.11.004

Schweidtmann, A. M., Esche, E., Fischer, A., Kloft, M., Repke, J. U., Sager, S., & Mitsos, A. (2021). Machine learning in chemical engineering: A perspective. Chemie Ingenieur Technik, 93(12), 2029–2039. https://doi.org/10.1002/cite.202100083

Huang, K., Wu, S., Li, F., Yang, C., & Gui, W. (2021). Fault diagnosis of hydraulic systems based on deep learning model with multi-rate data samples. IEEE Transactions on Neural Networks and Learning Systems, 33(11), 6789–6801. DOI: 10.1109/TNNLS.2021.3083401

Sridharan, N. V., & Sugumaran, V. (2025). Convolutional neural network based automatic detection of visible faults in a photovoltaic module. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 47(1), 6270–6284. https://doi.org/10.1080/15567036.2021.1905753

Huang, T., Zhang, Q., Tang, X., Zhao, S., & Lu, X. (2022). A novel fault diagnosis method based on CNN and LSTM and its application in fault diagnosis for complex systems. Artificial Intelligence Review, 55(2), 1289–1315. https://doi.org/10.1007/s10462-021-09993-z

Sun, L., & You, F. (2021). Machine learning and data-driven techniques for the control of smart power generation systems: An uncertainty handling perspective. Engineering, 7(9), 1239–1247. https://doi.org/10.1016/j.eng.2021.04.020

Omitaomu, O. A., & Niu, H. (2021). Artificial intelligence techniques in smart grid: A survey. Smart Cities, 4(2), 548–568. https://doi.org/10.3390/smartcities4020029

Mojumder, M. R. H., Hasanuzzaman, M., & Cuce, E. (2022). Prospects and challenges of renewable energy-based microgrid system in Bangladesh: A comprehensive review. Clean Technologies and Environmental Policy, 24(7), 1987–2009. https://doi.org/10.1007/s10098-022-02301-5




DOI: https://doi.org/10.31449/inf.v49i13.9292

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.