Sensor Fault Diagnosis for Aero Engine Based on Online Sequential Extreme Learning Machine with Memory Principle
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- Hang Liu & Youyuan Wang & Yi Yang & Ruijin Liao & Yujie Geng & Liwei Zhou, 2017. "A Failure Probability Calculation Method for Power Equipment Based on Multi-Characteristic Parameters," Energies, MDPI, vol. 10(5), pages 1-15, May.
- Jie Liu & Qiu Tang & Wei Qiu & Jun Ma & Junfeng Duan, 2021. "Probability-Based Failure Evaluation for Power Measuring Equipment," Energies, MDPI, vol. 14(12), pages 1-16, June.
- Jiao Liu & Jinfu Liu & Daren Yu & Myeongsu Kang & Weizhong Yan & Zhongqi Wang & Michael G. Pecht, 2018. "Fault Detection for Gas Turbine Hot Components Based on a Convolutional Neural Network," Energies, MDPI, vol. 11(8), pages 1-18, August.
- Ferhat Ucar & Omer F. Alcin & Besir Dandil & Fikret Ata, 2018. "Power Quality Event Detection Using a Fast Extreme Learning Machine," Energies, MDPI, vol. 11(1), pages 1-14, January.
- Xu, Maojun & Liu, Jinxin & Li, Ming & Geng, Jia & Wu, Yun & Song, Zhiping, 2022. "Improved hybrid modeling method with input and output self-tuning for gas turbine engine," Energy, Elsevier, vol. 238(PA).
- Yanfeng He & Zhijie Guo & Xiang Wang & Waheed Abdul, 2023. "A Hybrid Approach of the Deep Learning Method and Rule-Based Method for Fault Diagnosis of Sucker Rod Pumping Wells," Energies, MDPI, vol. 16(7), pages 1-19, March.
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Keywords
extreme learning machine (ELM); memory principle; online learning; aero engine; sensor fault diagnosis;All these keywords.
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