IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v30y2019i1d10.1007_s10845-016-1228-8.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-016-1228-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-016-1228-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    7. 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).
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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).
    13. 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).
    14. 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).
    15. 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.
    16. 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).
    17. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Faccio, M. & Persona, A. & Sgarbossa, F. & Zanin, G., 2014. "Industrial maintenance policy development: A quantitative framework," International Journal of Production Economics, Elsevier, vol. 147(PA), pages 85-93.
    2. Alexandros Bousdekis & Babis Magoutas & Dimitris Apostolou & Gregoris Mentzas, 2018. "Review, analysis and synthesis of prognostic-based decision support methods for condition based maintenance," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1303-1316, August.
    3. Liu, Gia-Shie, 2011. "Dynamic group instantaneous replacement policies for unreliable Markovian service systems," International Journal of Production Economics, Elsevier, vol. 130(2), pages 203-217, April.
    4. Daniela POPESCU & Adriana SCRIOSTEANU & ANDREI POPESCU, 2013. "Total Productive Maintenance, A Central Preoccupation Of The Managers," Management and Marketing Journal, University of Craiova, Faculty of Economics and Business Administration, vol. 0(1), pages 134-140, May.
    5. Özcan, Evren Can & Ünlüsoy, Sultan & Eren, Tamer, 2017. "A combined goal programming – AHP approach supported with TOPSIS for maintenance strategy selection in hydroelectric power plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 1410-1423.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:30:y:2019:i:1:d:10.1007_s10845-016-1228-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.