IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i19p6909-d920806.html
   My bibliography  Save this article

PHM SURVEY: Implementation of Prognostic Methods for Monitoring Industrial Systems

Author

Listed:
  • Abdenour Soualhi

    (Laboratoire LASPI, University Lyon, UJM-Saint Etienne, 42100 Saint-Etienne, France)

  • Mourad Lamraoui

    (Laboratoire LASPI, University Lyon, UJM-Saint Etienne, 42100 Saint-Etienne, France)

  • Bilal Elyousfi

    (Laboratoire LASPI, University Lyon, UJM-Saint Etienne, 42100 Saint-Etienne, France)

  • Hubert Razik

    (Laboratoire Ampère, University Lyon, 69007 Lyon, France)

Abstract

Prognostics and Health Management (commonly called PHM) is a field that focuses on the degradation mechanisms of systems in order to estimate their health status, anticipate their failure and optimize their maintenance. PHM uses methods, tools and algorithms for monitoring, anomaly detection, cause diagnosis, prognosis of the remaining useful life (RUL) and maintenance optimization. It allows for permanently monitoring the health of the system and provides operators and managers with relevant information to decide on actions to be taken to maintain the system in optimal operational conditions. This paper aims to present the emergence of the PHM thematically to describe the subjacent processes, particularly prognosis, how it supplies the different maintenance strategies and to explain the benefits that can be anticipated. More specifically, this paper establishes a state of the art in prognostic methods used today in the PHM strategy. In addition, this paper shows the multitude of possible prognostic approaches and the choice of one among them that will help to provide a framework for industrial companies.

Suggested Citation

  • Abdenour Soualhi & Mourad Lamraoui & Bilal Elyousfi & Hubert Razik, 2022. "PHM SURVEY: Implementation of Prognostic Methods for Monitoring Industrial Systems," Energies, MDPI, vol. 15(19), pages 1-24, September.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:6909-:d:920806
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/19/6909/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/19/6909/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tian, Zhigang & Jin, Tongdan & Wu, Bairong & Ding, Fangfang, 2011. "Condition based maintenance optimization for wind power generation systems under continuous monitoring," Renewable Energy, Elsevier, vol. 36(5), pages 1502-1509.
    2. Kwok L. Tsui & Nan Chen & Qiang Zhou & Yizhen Hai & Wenbin Wang, 2015. "Prognostics and Health Management: A Review on Data Driven Approaches," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-17, May.
    3. Kallen, M.J. & van Noortwijk, J.M., 2005. "Optimal maintenance decisions under imperfect inspection," Reliability Engineering and System Safety, Elsevier, vol. 90(2), pages 177-185.
    4. Nan Chen & Kwok Tsui, 2013. "Condition monitoring and remaining useful life prediction using degradation signals: revisited," IISE Transactions, Taylor & Francis Journals, vol. 45(9), pages 939-952.
    5. Bressel, Mathieu & Hilairet, Mickael & Hissel, Daniel & Ould Bouamama, Belkacem, 2016. "Extended Kalman Filter for prognostic of Proton Exchange Membrane Fuel Cell," Applied Energy, Elsevier, vol. 164(C), pages 220-227.
    6. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
    7. Biagetti, Tatiana & Sciubba, Enrico, 2004. "Automatic diagnostics and prognostics of energy conversion processes via knowledge-based systems," Energy, Elsevier, vol. 29(12), pages 2553-2572.
    8. van Noortwijk, J.M., 2009. "A survey of the application of gamma processes in maintenance," Reliability Engineering and System Safety, Elsevier, vol. 94(1), pages 2-21.
    9. Zio, Enrico & Di Maio, Francesco, 2010. "A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system," Reliability Engineering and System Safety, Elsevier, vol. 95(1), pages 49-57.
    10. Linkan Bian & Nagi Gebraeel & Jeffrey P. Kharoufeh, 2015. "Degradation modeling for real-time estimation of residual lifetimes in dynamic environments," IISE Transactions, Taylor & Francis Journals, vol. 47(5), pages 471-486, May.
    11. Moradi, Ramin & Groth, Katrina M., 2020. "Modernizing risk assessment: A systematic integration of PRA and PHM techniques," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    12. Hu, Chao & Youn, Byeng D. & Wang, Pingfeng & Taek Yoon, Joung, 2012. "Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 120-135.
    Full references (including those not matched with items on IDEAS)

    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. Le Son, Khanh & Fouladirad, Mitra & Barros, Anne & Levrat, Eric & Iung, Benoît, 2013. "Remaining useful life estimation based on stochastic deterioration models: A comparative study," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 165-175.
    2. Alaswad, Suzan & Xiang, Yisha, 2017. "A review on condition-based maintenance optimization models for stochastically deteriorating system," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 54-63.
    3. Hai-Kun Wang & Yan-Feng Li & Yu Liu & Yuan-Jian Yang & Hong-Zhong Huang, 2015. "Remaining useful life estimation under degradation and shock damage," Journal of Risk and Reliability, , vol. 229(3), pages 200-208, June.
    4. Guo, Chiming & Wang, Wenbin & Guo, Bo & Si, Xiaosheng, 2013. "A maintenance optimization model for mission-oriented systems based on Wiener degradation," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 183-194.
    5. Ling, M.H. & Ng, H.K.T. & Tsui, K.L., 2019. "Bayesian and likelihood inferences on remaining useful life in two-phase degradation models under gamma process," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 77-85.
    6. Xi, Zhimin & Jing, Rong & Wang, Pingfeng & Hu, Chao, 2014. "A copula-based sampling method for data-driven prognostics," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 72-82.
    7. Lee, Juseong & Mitici, Mihaela, 2020. "An integrated assessment of safety and efficiency of aircraft maintenance strategies using agent-based modelling and stochastic Petri nets," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    8. Fauriat, William & Zio, Enrico, 2020. "Optimization of an aperiodic sequential inspection and condition-based maintenance policy driven by value of information," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    9. Lee, Juseong & Mitici, Mihaela, 2022. "Multi-objective design of aircraft maintenance using Gaussian process learning and adaptive sampling," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    10. Wang, Zhaoqiang & Hu, Changhua & Wang, Wenbin & Zhou, Zhijie & Si, Xiaosheng, 2014. "A case study of remaining storage life prediction using stochastic filtering with the influence of condition monitoring," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 186-195.
    11. Miguel A. Rodríguez-López & Luis M. López-González & Luis M. López-Ochoa & Jesús Las-Heras-Casas, 2018. "Methodology for Detecting Malfunctions and Evaluating the Maintenance Effectiveness in Wind Turbine Generator Bearings Using Generic versus Specific Models from SCADA Data," Energies, MDPI, vol. 11(4), pages 1-22, March.
    12. Liu, Xingheng & Matias, José & Jäschke, Johannes & Vatn, Jørn, 2022. "Gibbs sampler for noisy Transformed Gamma process: Inference and remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    13. Ali Rohan, 2022. "Holistic Fault Detection and Diagnosis System in Imbalanced, Scarce, Multi-Domain (ISMD) Data Setting for Component-Level Prognostics and Health Management (PHM)," Mathematics, MDPI, vol. 10(12), pages 1-22, June.
    14. Costa, Nahuel & Sánchez, Luciano, 2022. "Variational encoding approach for interpretable assessment of remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    15. M D Pandey & T Cheng & J A M van der Weide, 2011. "Finite-time maintenance cost analysis of engineering systems affected by stochastic degradation," Journal of Risk and Reliability, , vol. 225(2), pages 241-250, June.
    16. de Jonge, Bram & Teunter, Ruud & Tinga, Tiedo, 2017. "The influence of practical factors on the benefits of condition-based maintenance over time-based maintenance," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 21-30.
    17. Hong, H.P. & Zhou, W. & Zhang, S. & Ye, W., 2014. "Optimal condition-based maintenance decisions for systems with dependent stochastic degradation of components," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 276-288.
    18. Esposito, Nicola & Mele, Agostino & Castanier, Bruno & GIORGIO, Massimiliano, 2023. "A hybrid maintenance policy for a deteriorating unit in the presence of three forms of variability," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    19. Son, Junbo & Zhou, Shiyu & Sankavaram, Chaitanya & Du, Xinyu & Zhang, Yilu, 2016. "Remaining useful life prediction based on noisy condition monitoring signals using constrained Kalman filter," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 38-50.
    20. Hazra, Indranil & Pandey, Mahesh D. & Manzana, Noldainerick, 2020. "Approximate Bayesian computation (ABC) method for estimating parameters of the gamma process using noisy data," Reliability Engineering and System Safety, Elsevier, vol. 198(C).

    More about this item

    Keywords

    PHM; prognosis; maintenance; RUL;
    All these keywords.

    Statistics

    Access and download statistics

    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:gam:jeners:v:15:y:2022:i:19:p:6909-:d:920806. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.