Leveraging Label Information in a Knowledge-Driven Approach for Rolling-Element Bearings Remaining Useful Life Prediction
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- Mario Tovar & Miguel Robles & Felipe Rashid, 2020. "PV Power Prediction, Using CNN-LSTM Hybrid Neural Network Model. Case of Study: Temixco-Morelos, México," Energies, MDPI, vol. 13(24), pages 1-15, December.
- Jun Peng & Zhiyong Zheng & Xiaoyong Zhang & Kunyuan Deng & Kai Gao & Heng Li & Bin Chen & Yingze Yang & Zhiwu Huang, 2020. "A Data-Driven Method with Feature Enhancement and Adaptive Optimization for Lithium-Ion Battery Remaining Useful Life Prediction," Energies, MDPI, vol. 13(3), pages 1-20, February.
- Wang, Jinjiang & Liang, Yuanyuan & Zheng, Yinghao & Gao, Robert X. & Zhang, Fengli, 2020. "An integrated fault diagnosis and prognosis approach for predictive maintenance of wind turbine bearing with limited samples," Renewable Energy, Elsevier, vol. 145(C), pages 642-650.
- Zhenyu He & Xiaochen Zhang & Chao Liu & Te Han, 2020. "Fault Prognostics for Photovoltaic Inverter Based on Fast Clustering Algorithm and Gaussian Mixture Model," Energies, MDPI, vol. 13(18), pages 1-20, September.
- Charaf Eddine Khamoudj & Fatima Benbouzid-Si Tayeb & Karima Benatchba & Mohamed Benbouzid & Abdenaser Djaafri, 2020. "A Learning Variable Neighborhood Search Approach for Induction Machines Bearing Failures Detection and Diagnosis," Energies, MDPI, vol. 13(11), pages 1-30, June.
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Cited by:
- Guy Clerc, 2022. "Failure Diagnosis and Prognosis of Induction Machines," Energies, MDPI, vol. 15(4), pages 1-2, February.
- Mohamed Benbouzid & Tarek Berghout & Nur Sarma & Siniša Djurović & Yueqi Wu & Xiandong Ma, 2021. "Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review," Energies, MDPI, vol. 14(18), pages 1-33, September.
- Tarek Berghout & Mohamed Benbouzid & Toufik Bentrcia & Xiandong Ma & Siniša Djurović & Leïla-Hayet Mouss, 2021. "Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects," Energies, MDPI, vol. 14(19), pages 1-24, October.
- Berghout, Tarek & Benbouzid, Mohamed, 2022. "EL-NAHL: Exploring labels autoencoding in augmented hidden layers of feedforward neural networks for cybersecurity in smart grids," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
- Zhenen Li & Xinyan Zhang & Tusongjiang Kari & Wei Hu, 2021. "Health Assessment and Remaining Useful Life Prediction of Wind Turbine High-Speed Shaft Bearings," Energies, MDPI, vol. 14(15), pages 1-19, July.
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Keywords
bearings; prognosis; remaining useful life; data-driven; knowledge-driven; transfer learning; labels information; exploiting labels; denoising autoencoder; convolutional LSTM;All these keywords.
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