IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v234y2023ics0951832023001199.html
   My bibliography  Save this article

Maintenance optimization in industry 4.0

Author

Listed:
  • Pinciroli, Luca
  • Baraldi, Piero
  • Zio, Enrico

Abstract

This work reviews maintenance optimization from different and complementary points of view. Specifically, we systematically analyze the knowledge, information and data that can be exploited for maintenance optimization within the Industry 4.0 paradigm. Then, the possible objectives of the optimization are critically discussed, together with the maintenance features to be optimized, such as maintenance periods and degradation thresholds. The main challenges and trends of maintenance optimization are, then, highlighted and the need is identified for methods that do not require a-priori selection of a predefined maintenance strategy, are able to deal with large amounts of heterogeneous data collected from different sources, can properly treat all the uncertainties affecting the behavior of the systems and the environment, and can jointly consider multiple optimization objectives, including the emerging ones related to sustainability and resilience.

Suggested Citation

  • Pinciroli, Luca & Baraldi, Piero & Zio, Enrico, 2023. "Maintenance optimization in industry 4.0," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    • Handle: RePEc:eee:reensy:v:234:y:2023:i:c:s0951832023001199
    DOI: 10.1016/j.ress.2023.109204
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832023001199
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2023.109204?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. Xian Zhao & Xinqian Huang & Jinglei Sun, 2020. "Reliability modeling and maintenance optimization for the two-unit system with preset self-repairing mechanism," Journal of Risk and Reliability, , vol. 234(2), pages 221-234, April.
    2. Dursun, İpek & Akçay, Alp & van Houtum, Geert-Jan, 2022. "Age-based maintenance under population heterogeneity: Optimal exploration and exploitation," European Journal of Operational Research, Elsevier, vol. 301(3), pages 1007-1020.
    3. Syan, Chanan S. & Ramsoobag, Geeta, 2019. "Maintenance applications of multi-criteria optimization: A review," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    4. Compare, Michele & Antonello, Federico & Pinciroli, Luca & Zio, Enrico, 2022. "A general model for life-cycle cost analysis of Condition-Based Maintenance enabled by PHM capabilities," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    5. Carazas, F.G. & Souza, G.F.M., 2010. "Risk-based decision making method for maintenance policy selection of thermal power plant equipment," Energy, Elsevier, vol. 35(2), pages 964-975.
    6. Shi, Yue & Zhu, Weihang & Xiang, Yisha & Feng, Qianmei, 2020. "Condition-based maintenance optimization for multi-component systems subject to a system reliability requirement," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    7. Antonello, Federico & Baraldi, Piero & Shokry, Ahmed & Zio, Enrico & Gentile, Ugo & Serio, Luigi, 2021. "Association rules extraction for the identification of functional dependencies in complex technical infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    8. Compare, M. & Martini, F. & Zio, E., 2015. "Genetic algorithms for condition-based maintenance optimization under uncertainty," European Journal of Operational Research, Elsevier, vol. 244(2), pages 611-623.
    9. Shen, Yilan & Zhang, Xi & Shi, Leyuan, 2022. "Joint optimization of production and maintenance for a serial–parallel hybrid two-stage production system," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    10. Wang, Hongzhou, 2002. "A survey of maintenance policies of deteriorating systems," European Journal of Operational Research, Elsevier, vol. 139(3), pages 469-489, June.
    11. Ghorbani, Milad & Nourelfath, Mustapha & Gendreau, Michel, 2022. "A two-stage stochastic programming model for selective maintenance optimization," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    12. Schouten, Thijs Nicolaas & Dekker, Rommert & Hekimoğlu, Mustafa & Eruguz, Ayse Sena, 2022. "Maintenance optimization for a single wind turbine component under time-varying costs," European Journal of Operational Research, Elsevier, vol. 300(3), pages 979-991.
    13. Zhang, Chengjie & Qi, Faqun & Zhang, Ning & Li, Yong & Huang, Hongzhong, 2022. "Maintenance policy optimization for multi-component systems considering dynamic importance of components," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    14. Andriotis, C.P. & Papakonstantinou, K.G., 2019. "Managing engineering systems with large state and action spaces through deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    15. Waeyenbergh, Geert & Pintelon, Liliane, 2004. "Maintenance concept development: A case study," International Journal of Production Economics, Elsevier, vol. 89(3), pages 395-405, June.
    16. Fallahi, F. & Bakir, I. & Yildirim, M. & Ye, Z., 2022. "A chance-constrained optimization framework for wind farms to manage fleet-level availability in condition based maintenance and operations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    17. S.A. Oke, 2005. "An analytical model for the optimisation of maintenance profitability," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 54(2), pages 113-136, March.
    18. Erkoyuncu, John Ahmet & Khan, Samir & Eiroa, Alexandre López & Butler, Nigel & Rushton, Keith & Brocklebank, Simon, 2017. "Perspectives on trading cost and availability for corrective maintenance at the equipment type level," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 53-69.
    19. Marais, Karen B. & Saleh, Joseph H., 2009. "Beyond its cost, the value of maintenance: An analytical framework for capturing its net present value," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 644-657.
    20. Nguyen, Van-Thai & Do, Phuc & Vosin, Alexandre & Iung, Benoit, 2022. "Artificial-intelligence-based maintenance decision-making and optimization for multi-state component systems," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    21. Yang, Zhe & Baraldi, Piero & Zio, Enrico, 2022. "A method for fault detection in multi-component systems based on sparse autoencoder-based deep neural networks," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    22. Nguyen, Hoang-Phuong & Baraldi, Piero & Zio, Enrico, 2021. "Ensemble empirical mode decomposition and long short-term memory neural network for multi-step predictions of time series signals in nuclear power plants," Applied Energy, Elsevier, vol. 283(C).
    23. Wang, Wenbin, 2012. "A stochastic model for joint spare parts inventory and planned maintenance optimisation," European Journal of Operational Research, Elsevier, vol. 216(1), pages 127-139.
    24. Jin, Xiaoning & Li, Lin & Ni, Jun, 2009. "Option model for joint production and preventive maintenance system," International Journal of Production Economics, Elsevier, vol. 119(2), pages 347-353, June.
    25. Liu, Bin & Wu, Shaomin & Xie, Min & Kuo, Way, 2017. "A condition-based maintenance policy for degrading systems with age- and state-dependent operating cost," European Journal of Operational Research, Elsevier, vol. 263(3), pages 879-887.
    26. George Kozanidis & Andreas Gavranis & Eftychia Kostarelou, 2012. "Mixed integer least squares optimization for flight and maintenance planning of mission aircraft," Naval Research Logistics (NRL), John Wiley & Sons, vol. 59(3‐4), pages 212-229, April.
    27. Zhao, Yunfei & Smidts, Carol, 2022. "Reinforcement learning for adaptive maintenance policy optimization under imperfect knowledge of the system degradation model and partial observability of system states," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    28. Bismut, Elizabeth & Pandey, Mahesh D. & Straub, Daniel, 2022. "Reliability-based inspection and maintenance planning of a nuclear feeder piping system," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    29. Y. S. Sherif & M. L. Smith, 1981. "Optimal maintenance models for systems subject to failure–A Review," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 28(1), pages 47-74, March.
    30. Fan, Lin & Su, Huai & Wang, Wei & Zio, Enrico & Zhang, Li & Yang, Zhaoming & Peng, Shiliang & Yu, Weichao & Zuo, Lili & Zhang, Jinjun, 2022. "A systematic method for the optimization of gas supply reliability in natural gas pipeline network based on Bayesian networks and deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    31. Vu, Hai Canh & Do, Phuc & Barros, Anne & Bérenguer, Christophe, 2014. "Maintenance grouping strategy for multi-component systems with dynamic contexts," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 233-249.
    32. Gary Linnéusson & Amos H. C. Ng & Tehseen Aslam, 2018. "Quantitative analysis of a conceptual system dynamics maintenance performance model using multi-objective optimisation," Journal of Simulation, Taylor & Francis Journals, vol. 12(2), pages 171-189, April.
    33. Giovanni Rinaldi & Philipp R. Thies & Lars Johanning, 2021. "Current Status and Future Trends in the Operation and Maintenance of Offshore Wind Turbines: A Review," Energies, MDPI, vol. 14(9), pages 1-28, April.
    34. Caballé, N.C. & Castro, I.T. & Pérez, C.J. & Lanza-Gutiérrez, J.M., 2015. "A condition-based maintenance of a dependent degradation-threshold-shock model in a system with multiple degradation processes," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 98-109.
    35. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    36. Quan, Gang & Greenwood, Garrison W. & Liu, Donglin & Hu, Sharon, 2007. "Searching for multiobjective preventive maintenance schedules: Combining preferences with evolutionary algorithms," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1969-1984, March.
    37. John J. McCall, 1965. "Maintenance Policies for Stochastically Failing Equipment: A Survey," Management Science, INFORMS, vol. 11(5), pages 493-524, March.
    38. Briš, Radim, 2008. "Parallel simulation algorithm for maintenance optimization based on directed Acyclic Graph," Reliability Engineering and System Safety, Elsevier, vol. 93(6), pages 874-884.
    39. Robelin, Charles-Antoine & Madanat, S M, 2006. "Dynamic Programing based Maintenance and Replacement Optimization for Bridge Decks using History-Dependent Deterioration Models," University of California Transportation Center, Working Papers qt5k01s7x9, University of California Transportation Center.
    40. Li, Y.F. & Valla, S. & Zio, E., 2015. "Reliability assessment of generic geared wind turbines by GTST-MLD model and Monte Carlo simulation," Renewable Energy, Elsevier, vol. 83(C), pages 222-233.
    41. 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.
    42. Maliheh Aramon Bajestani & Dragan Banjevic, 2016. "Calendar-based age replacement policy with dependent renewal cycles," IISE Transactions, Taylor & Francis Journals, vol. 48(11), pages 1016-1026, November.
    43. Jannie Sønderkær Nielsen & John Dalsgaard Sørensen, 2014. "Methods for Risk-Based Planning of O&M of Wind Turbines," Energies, MDPI, vol. 7(10), pages 1-20, October.
    44. Shafiee, Mahmood & Sørensen, John Dalsgaard, 2019. "Maintenance optimization and inspection planning of wind energy assets: Models, methods and strategies," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    45. Bode, Gerrit & Thul, Simon & Baranski, Marc & Müller, Dirk, 2020. "Real-world application of machine-learning-based fault detection trained with experimental data," Energy, Elsevier, vol. 198(C).
    46. Liu, Yu & Chen, Yiming & Jiang, Tao, 2020. "Dynamic selective maintenance optimization for multi-state systems over a finite horizon: A deep reinforcement learning approach," European Journal of Operational Research, Elsevier, vol. 283(1), pages 166-181.
    47. Deng, Qichen & Santos, Bruno F. & Curran, Richard, 2020. "A practical dynamic programming based methodology for aircraft maintenance check scheduling optimization," European Journal of Operational Research, Elsevier, vol. 281(2), pages 256-273.
    48. Wang, Jingjing & Zhao, Xian & Guo, Xiaoxin, 2019. "Optimizing wind turbine's maintenance policies under performance-based contract," Renewable Energy, Elsevier, vol. 135(C), pages 626-634.
    49. Hu, Yang & Miao, Xuewen & Si, Yong & Pan, Ershun & Zio, Enrico, 2022. "Prognostics and health management: A review from the perspectives of design, development and decision," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    50. Rocchetta, R. & Bellani, L. & Compare, M. & Zio, E. & Patelli, E., 2019. "A reinforcement learning framework for optimal operation and maintenance of power grids," Applied Energy, Elsevier, vol. 241(C), pages 291-301.
    51. Dao, Cuong D. & Zuo, Ming J., 2017. "Selective maintenance of multi-state systems with structural dependence," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 184-195.
    52. de Jonge, Bram & Klingenberg, Warse & Teunter, Ruud & Tinga, Tiedo, 2015. "Optimum maintenance strategy under uncertainty in the lifetime distribution," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 59-67.
    53. Alejandro Blanco-M. & Pere Marti-Puig & Karina Gibert & Jordi Cusidó & Jordi Solé-Casals, 2019. "A Text-Mining Approach to Assess the Failure Condition of Wind Turbines Using Maintenance Service History," Energies, MDPI, vol. 12(10), pages 1-20, May.
    54. Wang, Xiaoyue & Zhao, Xian & Wang, Siqi & Sun, Leping, 2020. "Reliability and maintenance for performance-balanced systems operating in a shock environment," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    55. Xiaofeng Wang & Shu Guo & Jian Shen & Yang Liu, 2020. "Optimization of preventive maintenance for series manufacturing system by differential evolution algorithm," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 745-757, March.
    56. Aghezzaf, El-Houssaine & Khatab, Abdelhakim & Tam, Phuoc Le, 2016. "Optimizing production and imperfect preventive maintenance planning׳s integration in failure-prone manufacturing systems," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 190-198.
    57. Li, Mingxin & Jiang, Xiaoli & Carroll, James & Negenborn, Rudy R., 2022. "A multi-objective maintenance strategy optimization framework for offshore wind farms considering uncertainty," Applied Energy, Elsevier, vol. 321(C).
    58. Zio, Enrico & Compare, Michele, 2013. "Evaluating maintenance policies by quantitative modeling and analysis," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 53-65.
    59. William P. Pierskalla & John A. Voelker, 1976. "A survey of maintenance models: The control and surveillance of deteriorating systems," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 23(3), pages 353-388, September.
    60. Bogumił Kamiński & Michał Jakubczyk & Przemysław Szufel, 2018. "A framework for sensitivity analysis of decision trees," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(1), pages 135-159, March.
    61. Irawan, Chandra Ade & Ouelhadj, Djamila & Jones, Dylan & Stålhane, Magnus & Sperstad, Iver Bakken, 2017. "Optimisation of maintenance routing and scheduling for offshore wind farms," European Journal of Operational Research, Elsevier, vol. 256(1), pages 76-89.
    62. Waeyenbergh, Geert & Pintelon, Liliane, 2002. "A framework for maintenance concept development," International Journal of Production Economics, Elsevier, vol. 77(3), pages 299-313, June.
    63. Wang, Ling & Chu, Jian & Wu, Jun, 2007. "Selection of optimum maintenance strategies based on a fuzzy analytic hierarchy process," International Journal of Production Economics, Elsevier, vol. 107(1), pages 151-163, May.
    64. Riccardo Manzini & Alberto Regattieri & Hoang Pham & Emilio Ferrari, 2010. "Maintenance for Industrial Systems," Springer Series in Reliability Engineering, Springer, number 978-1-84882-575-8, January.
    65. de Jonge, Bram & Scarf, Philip A., 2020. "A review on maintenance optimization," European Journal of Operational Research, Elsevier, vol. 285(3), pages 805-824.
    66. Cho, Danny I. & Parlar, Mahmut, 1991. "A survey of maintenance models for multi-unit systems," European Journal of Operational Research, Elsevier, vol. 51(1), pages 1-23, March.
    67. M. Assid & A. Gharbi & A. Hajji, 2015. "Joint production, setup and preventive maintenance policies of unreliable two-product manufacturing systems," International Journal of Production Research, Taylor & Francis Journals, vol. 53(15), pages 4668-4683, August.
    68. Richardson, Steven & Kefford, Adrian & Hodkiewicz, Melinda, 2013. "Optimised asset replacement strategy in the presence of lead time uncertainty," International Journal of Production Economics, Elsevier, vol. 141(2), pages 659-667.
    69. Indre Siksnelyte-Butkiene & Edmundas Kazimieras Zavadskas & Dalia Streimikiene, 2020. "Multi-Criteria Decision-Making (MCDM) for the Assessment of Renewable Energy Technologies in a Household: A Review," Energies, MDPI, vol. 13(5), pages 1-22, March.
    70. Compare, M. & Baraldi, P. & Bani, I. & Zio, E. & Mc Donnell, D., 2017. "Development of a Bayesian multi-state degradation model for up-to-date reliability estimations of working industrial components," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 25-40.
    71. Yang, Zhe & Baraldi, Piero & Zio, Enrico, 2020. "A novel method for maintenance record clustering and its application to a case study of maintenance optimization," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    72. Mancuso, A. & Compare, M. & Salo, A. & Zio, E., 2021. "Optimal Prognostics and Health Management-driven inspection and maintenance strategies for industrial systems," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    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. Lee, Jun S. & Yeo, In-Ho & Bae, Younghoon, 2024. "A stochastic track maintenance scheduling model based on deep reinforcement learning approaches," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    2. Pinciroli, Luca & Baraldi, Piero & Compare, Michele & Zio, Enrico, 2023. "Optimal operation and maintenance of energy storage systems in grid-connected microgrids by deep reinforcement learning," Applied Energy, Elsevier, vol. 352(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. de Jonge, Bram & Scarf, Philip A., 2020. "A review on maintenance optimization," European Journal of Operational Research, Elsevier, vol. 285(3), pages 805-824.
    2. Radim Briš & Nuong Thi Thuy Tran, 2023. "Discrete Model for a Multi-Objective Maintenance Optimization Problem of Safety Systems," Mathematics, MDPI, vol. 11(2), pages 1-18, January.
    3. Compare, Michele & Antonello, Federico & Pinciroli, Luca & Zio, Enrico, 2022. "A general model for life-cycle cost analysis of Condition-Based Maintenance enabled by PHM capabilities," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    4. He, Rui & Tian, Zhigang & Wang, Yifei & Zuo, Mingjian & Guo, Ziwei, 2023. "Condition-based maintenance optimization for multi-component systems considering prognostic information and degraded working efficiency," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    5. Pinciroli, Luca & Baraldi, Piero & Ballabio, Guido & Compare, Michele & Zio, Enrico, 2022. "Optimization of the Operation and Maintenance of renewable energy systems by Deep Reinforcement Learning," Renewable Energy, Elsevier, vol. 183(C), pages 752-763.
    6. Barlow, E. & Bedford, T. & Revie, M. & Tan, J. & Walls, L., 2021. "A performance-centred approach to optimising maintenance of complex systems," European Journal of Operational Research, Elsevier, vol. 292(2), pages 579-595.
    7. Oakley, Jordan L. & Wilson, Kevin J. & Philipson, Pete, 2022. "A condition-based maintenance policy for continuously monitored multi-component systems with economic and stochastic dependence," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    8. Olde Keizer, Minou C.A. & Teunter, Ruud H. & Veldman, Jasper, 2016. "Clustering condition-based maintenance for systems with redundancy and economic dependencies," European Journal of Operational Research, Elsevier, vol. 251(2), pages 531-540.
    9. Retsef Levi & Thomas Magnanti & Yaron Shaposhnik, 2019. "Scheduling with Testing," Management Science, INFORMS, vol. 65(2), pages 776-793, February.
    10. Hu, Yang & Miao, Xuewen & Si, Yong & Pan, Ershun & Zio, Enrico, 2022. "Prognostics and health management: A review from the perspectives of design, development and decision," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    11. David T. Abdul‐Malak & Jeffrey P. Kharoufeh & Lisa M. Maillart, 2019. "Maintaining systems with heterogeneous spare parts," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(6), pages 485-501, September.
    12. Liu, Gehui & Chen, Shaokuan & Ho, Tinkin & Ran, Xinchen & Mao, Baohua & Lan, Zhen, 2022. "Optimum opportunistic maintenance schedule over variable horizons considering multi-stage degradation and dynamic strategy," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    13. 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.
    14. Meng, Huixing & Liu, Xuan & Xing, Jinduo & Zio, Enrico, 2022. "A method for economic evaluation of predictive maintenance technologies by integrating system dynamics and evolutionary game modelling," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    15. Hao, Zhaojun & Di Maio, Francesco & Zio, Enrico, 2023. "A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    16. Wang, Wenbin, 2012. "An overview of the recent advances in delay-time-based maintenance modelling," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 165-178.
    17. Adedipe, Tosin & Shafiee, Mahmood & Zio, Enrico, 2020. "Bayesian Network Modelling for the Wind Energy Industry: An Overview," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    18. Zou, Xinyu & Tao, Laifa & Sun, Lulu & Wang, Chao & Ma, Jian & Lu, Chen, 2023. "A case-learning-based paradigm for quantitative recommendation of fault diagnosis algorithms: A case study of gearbox," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    19. Carpitella, Silvia & Mzougui, Ilyas & Benítez, Julio & Carpitella, Fortunato & Certa, Antonella & Izquierdo, Joaquín & La Cascia, Marco, 2021. "A risk evaluation framework for the best maintenance strategy: The case of a marine salt manufacture firm," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    20. Wu, Tianyi & Yang, Li & Ma, Xiaobing & Zhang, Zihan & Zhao, Yu, 2020. "Dynamic maintenance strategy with iteratively updated group information," Reliability Engineering and System Safety, Elsevier, vol. 197(C).

    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:eee:reensy:v:234:y:2023:i:c:s0951832023001199. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.