IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v30y2019i1d10.1007_s10845-016-1249-3.html
   My bibliography  Save this article

Two improvements of similarity-based residual life prediction methods

Author

Listed:
  • Mengyao Gu

    (Chongqing University)

  • Youling Chen

    (Chongqing University)

Abstract

The similarity-based residual life prediction (SbRLP) approach is an emerging technique and occupies a significant place in remaining useful life (RUL) prediction. Researches on (a) considering different operating conditions; and (b) considering maintenance are rare. But aforesaid factors have great influence on effective utilization of the SbRLP method. In this article, improvements are implemented from two above perspectives and thus a novel weight function and a fresh similarity measurement are advanced. Afterwards, a case study of the gyroscope’ RUL estimation demonstrates the reasonability and effectiveness of the proposed weight function and similarity measurement through comparisons with the classical SbRLP method. Meanwhile, the investigation results reveal that the performance of the SbRLP method with the recommended weight function improves fast with the increment of available reference systems, which have different operating conditions with the operating systems. And with the increase of maintenance frequency, the difference between the local performance of the SbRLP method with the introduced similarity measurement and that of the classical SbRLP method decreases gradually, which is just the opposite of the difference between their overall performances.

Suggested Citation

  • Mengyao Gu & Youling Chen, 2019. "Two improvements of similarity-based residual life prediction methods," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 303-315, January.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:1:d:10.1007_s10845-016-1249-3
    DOI: 10.1007/s10845-016-1249-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-016-1249-3
    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/s10845-016-1249-3?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. Wang, Wenbin & Hussin, B. & Jefferis, Tim, 2012. "A case study of condition based maintenance modelling based upon the oil analysis data of marine diesel engines using stochastic filtering," International Journal of Production Economics, Elsevier, vol. 136(1), pages 84-92.
    2. Ju-Liang Jin & Yi-Ming Wei & Le-Le Zou & Li Liu & Juan Fu, 2012. "Risk evaluation of China’s natural disaster systems: an approach based on triangular fuzzy numbers and stochastic simulation," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 62(1), pages 129-139, May.
    3. 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.
    4. You, Ming-Yi & Li, Hongguang & Meng, Guang, 2011. "Control-limit preventive maintenance policies for components subject to imperfect preventive maintenance and variable operational conditions," Reliability Engineering and System Safety, Elsevier, vol. 96(5), pages 590-598.
    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. 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.
    2. Yue Zhao & Zaiwu Gong & Wenhao Wang & Kai Luo, 2014. "The comprehensive risk evaluation on rainstorm and flood disaster losses in China mainland from 2004 to 2009: based on the triangular gray correlation theory," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 71(2), pages 1001-1016, March.
    3. Coria, V.H. & Maximov, S. & Rivas-Dávalos, F. & Melchor, C.L. & Guardado, J.L., 2015. "Analytical method for optimization of maintenance policy based on available system failure data," Reliability Engineering and System Safety, Elsevier, vol. 135(C), pages 55-63.
    4. 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.
    5. Braglia, Marcello & Carmignani, Gionata & Frosolini, Marco & Zammori, Francesco, 2012. "Data classification and MTBF prediction with a multivariate analysis approach," Reliability Engineering and System Safety, Elsevier, vol. 97(1), pages 27-35.
    6. García Nieto, P.J. & García-Gonzalo, E. & Sánchez Lasheras, F. & de Cos Juez, F.J., 2015. "Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 219-231.
    7. Yuanyuan He & Zaiwu Gong, 2014. "China’s regional rainstorm floods disaster evaluation based on grey incidence multiple-attribute decision model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 71(2), pages 1125-1144, March.
    8. Jun He & Xiao-Hua Yang & Jian-Qiang Li & Ju-Liang Jin & Yi-Ming Wei & Xiao-Juan Chen, 2015. "Spatiotemporal variation of meteorological droughts based on the daily comprehensive drought index in the Haihe River basin, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(2), pages 199-217, February.
    9. 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).
    10. 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.
    11. Yuanshu Jing & Jian Li & Yongyuan Weng & Jing Wang, 2014. "The assessment of drought relief by typhoon Saomai based on MODIS remote sensing data in Shanghai, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 71(2), pages 1215-1225, March.
    12. Gössinger, Ralf & Helmke, Hanna & Kaluzny, Michael, 2017. "Condition-based release of maintenance jobs in a decentralised production-maintenance system – An analysis of alternative stochastic approaches," International Journal of Production Economics, Elsevier, vol. 193(C), pages 528-537.
    13. Xiaojia Wang & Ting Huang & Keyu Zhu & Xibin Zhao, 2022. "LSTM-Based Broad Learning System for Remaining Useful Life Prediction," Mathematics, MDPI, vol. 10(12), pages 1-13, June.
    14. Naiming Xie & Jianghui Xin & Sifeng Liu, 2014. "China’s regional meteorological disaster loss analysis and evaluation based on grey cluster model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 71(2), pages 1067-1089, March.
    15. Vega, Manuel A. & Hu, Zhen & Todd, Michael D., 2020. "Optimal maintenance decisions for deteriorating quoin blocks in miter gates subject to uncertainty in the condition rating protocol," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    16. M. López-Campos & F. Kristjanpoller & P. Viveros & R. Pascual, 2018. "Reliability Assessment Methodology for Massive Manufacturing Using Multi-Function Equipment," Complexity, Hindawi, vol. 2018, pages 1-8, February.
    17. Bellaera, R. & Bonifetto, R. & Di Maio, F. & Pedroni, N. & Savoldi, L. & Zanino, R. & Zio, E., 2020. "Integrated deterministic and probabilistic safety assessment of a superconducting magnet cryogenic cooling circuit for nuclear fusion applications," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    18. Zhu, Qiushi & Peng, Hao & Timmermans, Bas & van Houtum, Geert-Jan, 2017. "A condition-based maintenance model for a single component in a system with scheduled and unscheduled downs," International Journal of Production Economics, Elsevier, vol. 193(C), pages 365-380.
    19. Jiawen Hu & Zuhua Jiang & Hong Wang, 2016. "Preventive maintenance for a single-machine system under variable operational conditions," Journal of Risk and Reliability, , vol. 230(4), pages 391-404, August.
    20. Tamilselvan, Prasanna & Wang, Pingfeng, 2013. "Failure diagnosis using deep belief learning based health state classification," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 124-135.

    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_s10845-016-1249-3. 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.