Zhang, C., Hu, D., & Yang, T. (2022). Anomaly detection and diagnosis for wind turbines using long short-term memory-based stacked denoising autoencoders and XGBoost. Reliability engineering & system safety, 222, 108445. https://doi.org/10.1016/j.ress.2022.108445
Chicago-Zitierstil (17. Ausg.)Zhang, Chen, Di Hu, und Tao Yang. "Anomaly Detection and Diagnosis for Wind Turbines Using Long Short-term Memory-based Stacked Denoising Autoencoders and XGBoost." Reliability Engineering & System Safety 222 (2022): 108445. https://doi.org/10.1016/j.ress.2022.108445.
MLA-Zitierstil (9. Ausg.)Zhang, Chen, et al. "Anomaly Detection and Diagnosis for Wind Turbines Using Long Short-term Memory-based Stacked Denoising Autoencoders and XGBoost." Reliability Engineering & System Safety, vol. 222, 2022, p. 108445, https://doi.org/10.1016/j.ress.2022.108445.