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A review of diagnostic and prognostic capabilities and best practices for manufacturing

Author

Listed:
  • Gregory W. Vogl

    (National Institute of Standards and Technology (NIST))

  • Brian A. Weiss

    (National Institute of Standards and Technology (NIST))

  • Moneer Helu

    (National Institute of Standards and Technology (NIST))

Abstract

Prognostics and health management (PHM) technologies reduce time and costs for maintenance of products or processes through efficient and cost-effective diagnostic and prognostic activities. PHM systems use real-time and historical state information of subsystems and components to provide actionable information, enabling intelligent decision-making for improved performance, safety, reliability, and maintainability. However, PHM is still an emerging field, and much of the published work has been either too exploratory or too limited in scope. Future smart manufacturing systems will require PHM capabilities that overcome current challenges, while meeting future needs based on best practices, for implementation of diagnostics and prognostics. This paper reviews the challenges, needs, methods, and best practices for PHM within manufacturing systems. This includes PHM system development of numerous areas highlighted by diagnostics, prognostics, dependability analysis, data management, and business. Based on current capabilities, PHM systems are shown to benefit from open-system architectures, cost-benefit analyses, method verification and validation, and standards.

Suggested Citation

  • Gregory W. Vogl & Brian A. Weiss & Moneer Helu, 2019. "A review of diagnostic and prognostic capabilities and best practices for manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 79-95, January.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:1:d:10.1007_s10845-016-1228-8
    DOI: 10.1007/s10845-016-1228-8
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    References listed on IDEAS

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    1. Waeyenbergh, Geert & Pintelon, Liliane, 2009. "CIBOCOF: A framework for industrial maintenance concept development," International Journal of Production Economics, Elsevier, vol. 121(2), pages 633-640, October.
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    Cited by:

    1. Matteo Barbieri & Khan T. P. Nguyen & Roberto Diversi & Kamal Medjaher & Andrea Tilli, 2021. "RUL prediction for automatic machines: a mixed edge-cloud solution based on model-of-signals and particle filtering techniques," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1421-1440, June.
    2. Kamble, Sachin S. & Gunasekaran, Angappa & Ghadge, Abhijeet & Raut, Rakesh, 2020. "A performance measurement system for industry 4.0 enabled smart manufacturing system in SMMEs- A review and empirical investigation," International Journal of Production Economics, Elsevier, vol. 229(C).
    3. Shashi Bhushan Jha & Radu F. Babiceanu & Remzi Seker, 2020. "Formal modeling of cyber-physical resource scheduling in IIoT cloud environments," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1149-1164, June.
    4. Fan, Yuantao & Nowaczyk, Sławomir & Rögnvaldsson, Thorsteinn, 2020. "Transfer learning for remaining useful life prediction based on consensus self-organizing models," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    5. Lia Tirabeni & Paola De Bernardi & Canio Forliano & Mattia Franco, 2019. "How Can Organisations and Business Models Lead to a More Sustainable Society? A Framework from a Systematic Review of the Industry 4.0," Sustainability, MDPI, vol. 11(22), pages 1-23, November.
    6. Shi, Jiayu & Zhong, Jingshu & Zhang, Yuxuan & Xiao, Bin & Xiao, Lei & Zheng, Yu, 2024. "A dual attention LSTM lightweight model based on exponential smoothing for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    7. Tae San Kim & So Young Sohn, 2021. "Multitask learning for health condition identification and remaining useful life prediction: deep convolutional neural network approach," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2169-2179, December.
    8. Vrignat, Pascal & Kratz, Frédéric & Avila, Manuel, 2022. "Sustainable manufacturing, maintenance policies, prognostics and health management: A literature review," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    9. Shanmugasivam Pillai & Prahlad Vadakkepat, 2022. "Deep learning for machine health prognostics using Kernel-based feature transformation," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1665-1680, August.
    10. Joaquín Ordieres-Meré & Tomás Prieto Remón & Jesús Rubio, 2020. "Digitalization: An Opportunity for Contributing to Sustainability From Knowledge Creation," Sustainability, MDPI, vol. 12(4), pages 1-21, February.
    11. Sanuri Ishak & Chong Tak Yaw & Siaw Paw Koh & Sieh Kiong Tiong & Chai Phing Chen & Talal Yusaf, 2021. "Fault Classification System for Switchgear CBM from an Ultrasound Analysis Technique Using Extreme Learning Machine," Energies, MDPI, vol. 14(19), pages 1-21, October.
    12. Hussein A. Taha & Soumaya Yacout & Yasser Shaban, 2023. "Autonomous self-healing mechanism for a CNC milling machine based on pattern recognition," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2185-2205, June.
    13. Pedersen, Tom Ivar & Vatn, Jørn, 2022. "Optimizing a condition-based maintenance policy by taking the preferences of a risk-averse decision maker into account," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    14. Florian, Eleonora & Sgarbossa, Fabio & Zennaro, Ilenia, 2021. "Machine learning-based predictive maintenance: A cost-oriented model for implementation," International Journal of Production Economics, Elsevier, vol. 236(C).
    15. 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).
    16. Yiwei Wang & Jian Zhou & Lianyu Zheng & Christian Gogu, 2022. "An end-to-end fault diagnostics method based on convolutional neural network for rotating machinery with multiple case studies," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 809-830, March.
    17. Tian, Meng & Chen, Yang & Tian, Guanghao & Huang, Wei & Hu, Chuan, 2023. "The role of digital transformation practices in the operations improvement in manufacturing firms: A practice-based view," International Journal of Production Economics, Elsevier, vol. 262(C).
    18. Zuo, Tao & Zhang, Kai & Zheng, Qing & Li, Xianxin & Li, Zhixuan & Ding, Guofu & Zhao, Minghang, 2023. "A hybrid attention-based multi-wavelet coefficient fusion method in RUL prognosis of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 237(C).

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