IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v256y2025ics0951832024007804.html
   My bibliography  Save this article

Methodology proposal for the development of failure prediction models applied to conveyor belts of mining material using machine learning

Author

Listed:
  • Gunckel, Pablo Viveros
  • Lobos, Giovanni
  • Rodríguez, Fredy Kristjanpoller
  • Bustos, Rodrigo Mena
  • Godoy, David

Abstract

The widespread adoption of areas such as Machine Learning, the establishment of Industry 4.0, and the various techniques and information available to companies today foster the need to incorporate advanced control and monitoring tools, such as predictive failure systems, into asset management. While there are various documented cases of trained ML models yielding good results, there is still a lack of clarity on how to address all the stages that an analysis like this requires in a general manner, considering that it must be valid across different areas and different data characteristics. This article presents and describes a workflow that encompasses this methodological proposal for the development of failure forecasting systems, which was then applied to the case of a mining conveyor belt in Chile. The study and its application case result in a successful integration between data from a Distributed Control System (DCS), a Digital Twin, and an operational logbook, as well as precision and recall values exceeding 0.83 in the best cases of the various trained algorithms with data transformed into new variables and the application of principal component analysis (PCA). This is done both for failure prediction in general and for fault type-oriented forecasting Based on this, the paper presents a transferable methodological proposal that is adaptable to various data sources without relying on specific assets or physical process information. Its main strength lies in reducing dependence on maintenance data for anomaly detection. However, this approach lacks validation and raises clarity issues, diverging from the Functional and Informational Requirements outlined by other authors. Despite these challenges, the model shows acceptable results, and the potential to integrate operational data allows for further development. Future iterations may focus on improving calculation times and addressing the challenge of identifying the origins or causes of predicted events.

Suggested Citation

  • Gunckel, Pablo Viveros & Lobos, Giovanni & Rodríguez, Fredy Kristjanpoller & Bustos, Rodrigo Mena & Godoy, David, 2025. "Methodology proposal for the development of failure prediction models applied to conveyor belts of mining material using machine learning," Reliability Engineering and System Safety, Elsevier, vol. 256(C).
    • Handle: RePEc:eee:reensy:v:256:y:2025:i:c:s0951832024007804
    DOI: 10.1016/j.ress.2024.110709
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832024007804
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2024.110709?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. Quintanilha, Igor M. & Elias, Vitor R.M. & da Silva, Felipe B. & Fonini, Pedro A.M. & da Silva, Eduardo A.B. & Netto, Sergio L. & Apolinário, José A. & de Campos, Marcello L.R. & Martins, Wallace A., 2021. "A fault detector/classifier for closed-ring power generators using machine learning," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    2. Nguyen, Khanh T.P. & Medjaher, Kamal, 2019. "A new dynamic predictive maintenance framework using deep learning for failure prognostics," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 251-262.
    3. Kristjanpoller, Fredy & Cárdenas-Pantoja, Nicolás & Viveros, Pablo & Pascual, Rodrigo, 2023. "Wind farm life cycle cost modelling based on oversizing capacity under load sharing configuration," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    4. Ahmed, Umair & Carpitella, Silvia & Certa, Antonella, 2021. "An integrated methodological approach for optimising complex systems subjected to predictive maintenance," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    5. Zhou, Kai-Li & Cheng, De-Jun & Zhang, Han-Bing & Hu, Zhong-tai & Zhang, Chun-Yan, 2023. "Deep learning-based intelligent multilevel predictive maintenance framework considering comprehensive cost," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    6. Pablo Viveros & Marco Espinoza & Rodrigo Mena & Fredy Kristjanpoller & Yu Zhou, 2023. "Extended Framework for Preventive Maintenance Planning: Risk and Behaviour Analysis of a Proposed Optimization Model," Complexity, Hindawi, vol. 2023, pages 1-22, February.
    7. Zhang, Zhiwei & Li, Songling & Wang, Huajie & Qian, Hongliang & Gong, Changqing & Wu, Qiongyao & Fan, Feng, 2025. "A study of neural network-based evaluation methods for pipelines with multiple corrosive regions," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    8. He, Xinxin & Wang, Zhijian & Li, Yanfeng & Khazhina, Svetlana & Du, Wenhua & Wang, Junyuan & Wang, Wenzhao, 2022. "Joint decision-making of parallel machine scheduling restricted in job-machine release time and preventive maintenance with remaining useful life constraints," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    9. Zhuang, Liangliang & Xu, Ancha & Wang, Xiao-Lin, 2023. "A prognostic driven predictive maintenance framework based on Bayesian deep learning," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    10. Pablo Viveros & Rodrigo Mena & Enrico Zio & Leonardo Miqueles & Fredy Kristjanpoller, 2023. "Integrated planning framework for preventive maintenance grouping: A case study for a conveyor system in the Chilean mining industry," Journal of Risk and Reliability, , vol. 237(5), pages 1011-1028, October.
    11. Xu, Yadong & Yan, Xiaoan & Sun, Beibei & Liu, Zheng, 2022. "Global contextual residual convolutional neural networks for motor fault diagnosis under variable-speed conditions," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    12. Rachman, Andika & Ratnayake, R.M. Chandima, 2019. "Machine learning approach for risk-based inspection screening assessment," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 518-532.
    13. Dayo-Olupona, Oluwatobi & Genc, Bekir & Celik, Turgay & Bada, Samson, 2023. "Adoptable approaches to predictive maintenance in mining industry: An overview," Resources Policy, Elsevier, vol. 86(PA).
    14. de Jonge, Bram & Scarf, Philip A., 2020. "A review on maintenance optimization," European Journal of Operational Research, Elsevier, vol. 285(3), pages 805-824.
    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. Zhang, Huixian & Wei, Xiukun & Liu, Zhiqiang & Ding, Yaning & Guan, Qingluan, 2025. "Condition-based maintenance for multi-state systems with prognostic and deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 255(C).
    2. Cai, Yue & de Jonge, Bram & Teunter, Ruud H., 2025. "Data-driven condition-based maintenance optimization given limited data," European Journal of Operational Research, Elsevier, vol. 324(1), pages 324-334.
    3. Zaitseva, Elena & Levashenko, Vitaly & Rabcan, Jan, 2023. "A new method for analysis of Multi-State systems based on Multi-valued decision diagram under epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    4. Dehghan Shoorkand, Hassan & Nourelfath, Mustapha & Hajji, Adnène, 2024. "A hybrid CNN-LSTM model for joint optimization of production and imperfect predictive maintenance planning," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    5. Mandelli, Diego & Wang, Congjian & Agarwal, Vivek & Lin, Linyu & Manjunatha, Koushik A., 2024. "Reliability modeling in a predictive maintenance context: A margin-based approach," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    6. Cai, Yue & Teunter, Ruud H. & de Jonge, Bram, 2023. "A data-driven approach for condition-based maintenance optimization," European Journal of Operational Research, Elsevier, vol. 311(2), pages 730-738.
    7. 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).
    8. Zhou, Kai-Li & Cheng, De-Jun & Zhang, Han-Bing & Hu, Zhong-tai & Zhang, Chun-Yan, 2023. "Deep learning-based intelligent multilevel predictive maintenance framework considering comprehensive cost," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    9. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    10. Kamariotis, Antonios & Tatsis, Konstantinos & Chatzi, Eleni & Goebel, Kai & Straub, Daniel, 2024. "A metric for assessing and optimizing data-driven prognostic algorithms for predictive maintenance," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    11. Shi, Guannan & Zhang, Xiaohong & Zeng, Jianchao & Liao, Haitao & Shi, Hui & Niu, Huifang & Wang, Jinhe, 2024. "A chance-constrained net revenue model for online dynamic predictive maintenance decision-making," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    12. Xu, Yadong & Yan, Xiaoan & Feng, Ke & Zhang, Yongchao & Zhao, Xiaoli & Sun, Beibei & Liu, Zheng, 2023. "Global contextual multiscale fusion networks for machine health state identification under noisy and imbalanced conditions," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    13. 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).
    14. Basora, Luis & Viens, Arthur & Chao, Manuel Arias & Olive, Xavier, 2025. "A benchmark on uncertainty quantification for deep learning prognostics," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    15. Quintanilha, Igor M. & Elias, Vitor R.M. & da Silva, Felipe B. & Fonini, Pedro A.M. & da Silva, Eduardo A.B. & Netto, Sergio L. & Apolinário, José A. & de Campos, Marcello L.R. & Martins, Wallace A., 2021. "A fault detector/classifier for closed-ring power generators using machine learning," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    16. Torrado, Nuria, 2022. "Optimal component-type allocation and replacement time policies for parallel systems having multi-types dependent components," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    17. Dong, Jie & Li, Daye & Cong, Zhiyu & Peng, Kaixiang, 2025. "A new fault detection method based on an updatable hybrid model for hard-to-detect faults in nonstationary processes," Reliability Engineering and System Safety, Elsevier, vol. 259(C).
    18. Campari, Alessandro & Ustolin, Federico & Alvaro, Antonio & Paltrinieri, Nicola, 2024. "Inspection of hydrogen transport equipment: A data-driven approach to predict fatigue degradation," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    19. Dui, Hongyan & Lu, Yaohui & Chen, Liwei, 2024. "Importance-based system cost management and failure risk analysis for different phases in life cycle," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    20. 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).

    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:256:y:2025:i:c:s0951832024007804. 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.