Application of advanced signal processing techniques in vibration analysis and fault diagnosis of multi-dimensional anthropomorphic wind turbines

[1] Zhang, D., Qian, L., Mao, B., Huang, C., Huang, B., & Si, Y. (2018). A data-driven design for fault detection of wind turbines using random forests and XGboost. Ieee Access, 6, 21020-21031. Search in Google Scholar

[2] Cho, S., Gao, Z., & Moan, T. (2018). Model-based fault detection, fault isolation and fault-tolerant control of a blade pitch system in floating wind turbines. Renewable energy, 120, 306-321. Search in Google Scholar

[3] Ruiz, M., Mujica, L. E., Alferez, S., Acho, L., Tutiven, C., Vidal, Y., ... & Pozo, F. (2018). Wind turbine fault detection and classification by means of image texture analysis. Mechanical Systems and Signal Processing, 107, 149-167. Search in Google Scholar

[4] Helbing, G., & Ritter, M. (2018). Deep Learning for fault detection in wind turbines. Renewable and Sustainable Energy Reviews, 98, 189-198. Search in Google Scholar

[5] Zhang, S., & Lang, Z. Q. (2020). SCADA-data-based wind turbine fault detection: A dynamic model sensor method. Control Engineering Practice, 102, 104546. Search in Google Scholar

[6] Habibi, H., Howard, I., & Simani, S. (2019). Reliability improvement of wind turbine power generation using model-based fault detection and fault tolerant control: A review. Renewable energy, 135, 877-896. Search in Google Scholar

[7] Velandia-Cardenas, C., Vidal, Y., & Pozo, F. (2021). Wind turbine fault detection using highly imbalanced real SCADA data. Energies, 14(6), 1728. Search in Google Scholar

[8] Qu, F., Liu, J., Zhu, H., & Zhou, B. (2020). Wind turbine fault detection based on expanded linguistic terms and rules using non-singleton fuzzy logic. Applied Energy, 262, 114469. Search in Google Scholar

[9] Teng, W., Ding, X., Tang, S., Xu, J., Shi, B., & Liu, Y. (2021). Vibration analysis for fault detection of wind turbine drivetrains-a comprehensive investigation. Sensors, 21(5), 1686. Search in Google Scholar

[10] Jiang, G., Xie, P., He, H., & Yan, J. (2017). Wind turbine fault detection using a denoising autoencoder with temporal information. IEEE/Asme transactions on mechatronics, 23(1), 89-100. Search in Google Scholar

[11] Wang, Y., Ma, X., & Qian, P. (2018). Wind turbine fault detection and identification through PCA-based optimal variable selection. IEEE Transactions on Sustainable Energy, 9(4), 1627-1635. Search in Google Scholar

[12] Vidal, Y., Pozo, F., & Tutiven, C. (2018). Wind turbine multi-fault detection and classification based on SCADA data. Energies, 11(11), 3018. Search in Google Scholar

[13] Nithya, M., Nagarajan, S., & Navaseelan, P. (2017, April). Fault detection of wind turbine system using neural networks. In 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR) (pp. 103-108). IEEE. Search in Google Scholar

[14] Liu, J., Qu, F., Hong, X., & Zhang, H. (2018). A small-sample wind turbine fault detection method with synthetic fault data using generative adversarial nets. IEEE Transactions on Industrial Informatics, 15(7), 3877-3888. Search in Google Scholar

[15] Wu, X., Jiang, G., Wang, X., Xie, P., & Li, X. (2019). A multi-level-denoising autoencoder approach for wind turbine fault detection. Ieee Access, 7, 59376-59387. Search in Google Scholar

[16] Santolamazza, A., Dadi, D., & Introna, V. (2021). A data-mining approach for wind turbine fault detection based on SCADA data analysis using artificial neural networks. Energies, 14(7), 1845. Search in Google Scholar

[17] Peng Xue,Yi Wan,Jun Takahashi & Hiromichi Akimoto. (2024). Structural optimization using a genetic algorithm aiming for the minimum mass of vertical axis wind turbines using composite materials. Heliyon(12),e33185-e33185. Search in Google Scholar

[18] Wang GuiLan,Zhao HongShan,Guo ShuangWei & Mi ZengQiang. (2017). Numeric optimal sensor configuration solutions for wind turbine gearbox based on structure analysis. IET Renewable Power Generation(12),1597-1602. Search in Google Scholar

[19] Catelani Marcantonio,Ciani Lorenzo,Galar Diego & Patrizi Gabriele. (2020). Optimizing maintenance policies for a yaw system using reliability centered maintenance and data-driven condition monitoring. IEEE Transactions on Instrumentation and Measurement(9),1-1. Search in Google Scholar

[20] Meng Zhang. (2024). Multi-resolution short-time Fourier transform providing deep features for 3D CNN to classify rolling bearing fault vibration signals. Engineering Research Express(3). Search in Google Scholar

[21] Olalere Isaac Opeyemi & Olanrewaju Oludolapo Akanni. (2023). Tool and Workpiece Condition Classification Using Empirical Mode Decomposition (EMD) with Hilbert–Huang Transform (HHT) of Vibration Signals and Machine Learning Models. Applied Sciences(4),2248-2248. Search in Google Scholar

[22] You Keshun,Qiu Guangqi & Gu Yingkui. (2023). An efficient lightweight neural network using BiLSTMSCN-CBAM with PCA-ICEEMDAN for diagnosing rolling bearing faults. Measurement Science and Technology(9). Search in Google Scholar