IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v15y2024i4d10.1007_s13198-022-01843-7.html
   My bibliography  Save this article

Condition-based maintenance using machine learning and role of interpretability: a review

Author

Listed:
  • Jeetesh Sharma

    (Malaviya National Institute of Technology)

  • Murari Lal Mittal

    (Malaviya National Institute of Technology)

  • Gunjan Soni

    (Malaviya National Institute of Technology)

Abstract

This article aims to review the literature on condition-based maintenance (CBM) by analyzing various terms, applications, and challenges. CBM is a maintenance technique that monitors the existing condition of an industrial asset to determine what maintenance needs to be performed. This article enlightens the readers with research in condition-based maintenance using machine learning and artificial intelligence techniques and related literature. A bibliometric analysis is performed on the data collected from the Scopus database. The foundation of a CBM is accurate anomaly detection and diagnosis. Several machine-learning approaches have produced excellent results for anomaly detection and diagnosis. However, due to the black-box nature of the machine learning models, the level of their interpretability is limited. This article provides insight into the existing maintenance strategies, anomaly detection techniques, interpretable models, and model-agnostic methods that are being applied. It has been found that significant work has been done towards condition based-maintenance using machine learning, but explainable artificial intelligence approaches got less attention in CBM. Based on the review, we contend that explainable artificial intelligence can provide unique insights and opportunities for addressing critical difficulties in maintenance leading to more informed decision-making. The analysis put forward encouraging research directions in this area.

Suggested Citation

  • Jeetesh Sharma & Murari Lal Mittal & Gunjan Soni, 2024. "Condition-based maintenance using machine learning and role of interpretability: a review," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(4), pages 1345-1360, April.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:4:d:10.1007_s13198-022-01843-7
    DOI: 10.1007/s13198-022-01843-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-022-01843-7
    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/s13198-022-01843-7?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. Swanson, Laura, 2001. "Linking maintenance strategies to performance," International Journal of Production Economics, Elsevier, vol. 70(3), pages 237-244, April.
    2. Salahshoor, Karim & Kordestani, Mojtaba & Khoshro, Majid S., 2010. "Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers," Energy, Elsevier, vol. 35(12), pages 5472-5482.
    3. Wenna Zhang & Xiandong Ma, 2016. "Simultaneous Fault Detection and Sensor Selection for Condition Monitoring of Wind Turbines," Energies, MDPI, vol. 9(4), pages 1-15, April.
    4. Minghui Ou & Hua Wei & Yiyi Zhang & Jiancheng Tan, 2019. "A Dynamic Adam Based Deep Neural Network for Fault Diagnosis of Oil-Immersed Power Transformers," Energies, MDPI, vol. 12(6), pages 1-16, March.
    5. Daniel W. Apley & Jingyu Zhu, 2020. "Visualizing the effects of predictor variables in black box supervised learning models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(4), pages 1059-1086, September.
    6. Hsu-Hao Yang & Mei-Ling Huang & Shih-Wei Yang, 2015. "Integrating Auto-Associative Neural Networks with Hotelling T 2 Control Charts for Wind Turbine Fault Detection," Energies, MDPI, vol. 8(10), pages 1-16, October.
    7. Zhao, Yang & Liu, Peng & Wang, Zhenpo & Zhang, Lei & Hong, Jichao, 2017. "Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods," Applied Energy, Elsevier, vol. 207(C), pages 354-362.
    8. 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.
    9. Gits, C. W., 1992. "Design of maintenance concepts," International Journal of Production Economics, Elsevier, vol. 24(3), pages 217-226, March.
    10. Guifang Liu & Huaiqian Bao & Baokun Han, 2018. "A Stacked Autoencoder-Based Deep Neural Network for Achieving Gearbox Fault Diagnosis," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-10, July.
    11. Sunil Sapra, 2010. "Robust vs. classical principalcomponent analysis in the presence of outliers," Applied Economics Letters, Taylor & Francis Journals, vol. 17(6), pages 519-523.
    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. Toon Vanderschueren & Robert Boute & Tim Verdonck & Bart Baesens & Wouter Verbeke, 2022. "Prescriptive maintenance with causal machine learning," Papers 2206.01562, arXiv.org.
    2. Muchiri, Peter & Pintelon, Liliane & Gelders, Ludo & Martin, Harry, 2011. "Development of maintenance function performance measurement framework and indicators," International Journal of Production Economics, Elsevier, vol. 131(1), pages 295-302, May.
    3. Vanderschueren, Toon & Boute, Robert & Verdonck, Tim & Baesens, Bart & Verbeke, Wouter, 2023. "Optimizing the preventive maintenance frequency with causal machine learning," International Journal of Production Economics, Elsevier, vol. 258(C).
    4. Mahboob Elahi & Samuel Olaiya Afolaranmi & Wael M. Mohammed & Jose Luis Martinez Lastra, 2022. "Energy-Based Prognostics for Gradual Loss of Conveyor Belt Tension in Discrete Manufacturing Systems," Energies, MDPI, vol. 15(13), pages 1-18, June.
    5. Lu, Xuefei & Borgonovo, Emanuele, 2023. "Global sensitivity analysis in epidemiological modeling," European Journal of Operational Research, Elsevier, vol. 304(1), pages 9-24.
    6. Elianne Mora & Jenny Cifuentes & Geovanny Marulanda, 2021. "Short-Term Forecasting of Wind Energy: A Comparison of Deep Learning Frameworks," Energies, MDPI, vol. 14(23), pages 1-26, November.
    7. Swanson, Laura, 2001. "Linking maintenance strategies to performance," International Journal of Production Economics, Elsevier, vol. 70(3), pages 237-244, April.
    8. Jian Guo & Saizhuo Wang & Lionel M. Ni & Heung-Yeung Shum, 2022. "Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligence," Papers 2301.04020, arXiv.org.
    9. Zhang, Chanyuan (Abigail) & Cho, Soohyun & Vasarhelyi, Miklos, 2022. "Explainable Artificial Intelligence (XAI) in auditing," International Journal of Accounting Information Systems, Elsevier, vol. 46(C).
    10. Bastos, João A. & Matos, Sara M., 2022. "Explainable models of credit losses," European Journal of Operational Research, Elsevier, vol. 301(1), pages 386-394.
    11. Johannes Freiesleben & Nicolas Gu'erin, 2015. "Homogenization and Clustering as a Non-Statistical Methodology to Assess Multi-Parametrical Chain Problems," Papers 1505.03874, arXiv.org, revised Dec 2017.
    12. Zhao, Yang & Jiang, Ziyue & Chen, Xinyu & Liu, Peng & Peng, Tianduo & Shu, Zhan, 2023. "Toward environmental sustainability: data-driven analysis of energy use patterns and load profiles for urban electric vehicle fleets," Energy, Elsevier, vol. 285(C).
    13. Yu, Quanqing & Dai, Lei & Xiong, Rui & Chen, Zeyu & Zhang, Xin & Shen, Weixiang, 2022. "Current sensor fault diagnosis method based on an improved equivalent circuit battery model," Applied Energy, Elsevier, vol. 310(C).
    14. Feng Lu & Jipeng Jiang & Jinquan Huang & Xiaojie Qiu, 2018. "An Iterative Reduced KPCA Hidden Markov Model for Gas Turbine Performance Fault Diagnosis," Energies, MDPI, vol. 11(7), pages 1-21, July.
    15. Alsyouf, Imad, 2007. "The role of maintenance in improving companies' productivity and profitability," International Journal of Production Economics, Elsevier, vol. 105(1), pages 70-78, January.
    16. Hu, Songhua & Xiong, Chenfeng & Ji, Ya & Wu, Xin & Liu, Kailun & Schonfeld, Paul, 2024. "Understanding factors influencing user engagement in incentive-based travel demand management program," Transportation Research Part A: Policy and Practice, Elsevier, vol. 186(C).
    17. Shao, Qifan & Zhang, Wenjia & Cao, Xinyu (Jason) & Yang, Jiawen, 2023. "Built environment interventions for emission mitigation: A machine learning analysis of travel-related CO2 in a developing city," Journal of Transport Geography, Elsevier, vol. 110(C).
    18. Barbosa de Santis, Rodrigo & Silveira Gontijo, Tiago & Azevedo Costa, Marcelo, 2021. "Condition-based maintenance in hydroelectric plants: A systematic literature review," MPRA Paper 115912, University Library of Munich, Germany.
    19. Green, Maxwell H. & Davies, Philip & Ng, Irene C.L., 2017. "Two strands of servitization: A thematic analysis of traditional and customer co-created servitization and future research directions," International Journal of Production Economics, Elsevier, vol. 192(C), pages 40-53.
    20. Liu, Qiquan & Ma, Jian & Zhao, Xuan & Zhang, Kai & Meng, Dean, 2023. "Online diagnosis and prediction of power battery voltage comprehensive faults for electric vehicles based on multi-parameter characterization and improved K-means method," Energy, Elsevier, vol. 283(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:spr:ijsaem:v:15:y:2024:i:4:d:10.1007_s13198-022-01843-7. 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.