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Prognostics and Health Management

In: Risk-Based Engineering

Author

Listed:
  • Prabhakar V. Varde

    (Bhabha Atomic Research Centre)

  • Michael G. Pecht

    (University of Maryland)

Abstract

Nuclear power, with over 430 nuclear power plants (NPPs) operating around the world, is the source of about 17% of the world’s electricity. The nuclear industry has arrived at a point where it is dealing with two major issues. First, it must address life extension for legacy units while complying with present-day safety regulations. Second, new systems must be designed with enhanced safety features so that the core damage frequency meets the target of 10−6 failures per reactor-year or less.

Suggested Citation

  • Prabhakar V. Varde & Michael G. Pecht, 2018. "Prognostics and Health Management," Springer Series in Reliability Engineering, in: Risk-Based Engineering, chapter 0, pages 447-507, Springer.
    • Handle: RePEc:spr:ssrchp:978-981-13-0090-5_13
    DOI: 10.1007/978-981-13-0090-5_13
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    Citations

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    Cited by:

    1. Prashant Kumar & Salman Khalid & Heung Soo Kim, 2023. "Prognostics and Health Management of Rotating Machinery of Industrial Robot with Deep Learning Applications—A Review," Mathematics, MDPI, vol. 11(13), pages 1-37, July.
    2. Liu, Junqiang & Lei, Fan & Pan, Chunlu & Hu, Dongbin & Zuo, Hongfu, 2021. "Prediction of remaining useful life of multi-stage aero-engine based on clustering and LSTM fusion," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    3. Theissler, Andreas & Pérez-Velázquez, Judith & Kettelgerdes, Marcel & Elger, Gordon, 2021. "Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    4. Luca Pinciroli & Piero Baraldi & Guido Ballabio & Michele Compare & Enrico Zio, 2021. "Deep Reinforcement Learning Based on Proximal Policy Optimization for the Maintenance of a Wind Farm with Multiple Crews," Energies, MDPI, vol. 14(20), pages 1-17, October.
    5. Hundi, Prabhas & Shahsavari, Rouzbeh, 2020. "Comparative studies among machine learning models for performance estimation and health monitoring of thermal power plants," Applied Energy, Elsevier, vol. 265(C).
    6. Rauf, Huzaifa & Khalid, Muhammad & Arshad, Naveed, 2022. "Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    7. Mathieu Payette & Georges Abdul-Nour, 2023. "Machine Learning Applications for Reliability Engineering: A Review," Sustainability, MDPI, vol. 15(7), pages 1-22, April.
    8. Matthieu Dubarry & David Beck, 2021. "Analysis of Synthetic Voltage vs. Capacity Datasets for Big Data Li-ion Diagnosis and Prognosis," Energies, MDPI, vol. 14(9), pages 1-24, April.
    9. Lewis, Austin D. & Groth, Katrina M., 2022. "Metrics for evaluating the performance of complex engineering system health monitoring models," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    10. Hamed Sadegh Kouhestani & Xiaoping Yi & Guoqing Qi & Xunliang Liu & Ruimin Wang & Yang Gao & Xiao Yu & Lin Liu, 2022. "Prognosis and Health Management (PHM) of Solid-State Batteries: Perspectives, Challenges, and Opportunities," Energies, MDPI, vol. 15(18), pages 1-26, September.
    11. Irene Niyonambaza & Marco Zennaro & Alfred Uwitonze, 2020. "Predictive Maintenance (PdM) Structure Using Internet of Things (IoT) for Mechanical Equipment Used into Hospitals in Rwanda," Future Internet, MDPI, vol. 12(12), pages 1-23, December.
    12. Tanvir Alam Shifat & Rubiya Yasmin & Jang-Wook Hur, 2021. "A Data Driven RUL Estimation Framework of Electric Motor Using Deep Electrical Feature Learning from Current Harmonics and Apparent Power," Energies, MDPI, vol. 14(11), pages 1-21, May.
    13. Yong Zhu & Mingyi Liu & Lin Wang & Jianxing Wang, 2022. "Potential Failure Prediction of Lithium-ion Battery Energy Storage System by Isolation Density Method," Sustainability, MDPI, vol. 14(12), pages 1-14, June.

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