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Statistical distributions comparison for remaining useful life prediction of components via ANN

Author

Listed:
  • Mohammad Ali Farsi

    (Ministry of Science, Research and Technology)

  • S. Masood Hosseini

    (Ministry of Science, Research and Technology)

Abstract

Remaining useful life prediction of a system is one of the most important and critical items to achieve the optimal condition-based maintenance for availability and reliability increase, and maintenance reduction. We develop an artificial neural network (ANN) based approach to increase accuracy for the prediction of a system/component remaining useful life. The ANN model takes the working time/age and some parameters produced by a condition monitoring process at the present and previous inspection points as the inputs, and the percentage of the life is produced as the output. Also, different distribution functions, such as Weibull, Birnbaum–Saunders, Gamma, Wakeby, Logistic and Log–normal function are utilized to adjust each condition monitoring data for a failure history, and the adjusted values are applied to determine the training set so as to decrease the noise factors influences unrelated to the degradation of the equipment. The ANN method is validated using the collected vibration monitoring data from a particular machine (pump bearings in the field). Finally, the performance of the distribution functions on the results of the ANN output is compared and the more effective functions are defined.

Suggested Citation

  • Mohammad Ali Farsi & S. Masood Hosseini, 2019. "Statistical distributions comparison for remaining useful life prediction of components via ANN," 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. 10(3), pages 429-436, June.
    • Handle: RePEc:spr:ijsaem:v:10:y:2019:i:3:d:10.1007_s13198-019-00813-w
    DOI: 10.1007/s13198-019-00813-w
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    References listed on IDEAS

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    1. Zhu, Shun-Peng & Huang, Hong-Zhong & Peng, Weiwen & Wang, Hai-Kun & Mahadevan, Sankaran, 2016. "Probabilistic Physics of Failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 146(C), pages 1-12.
    2. D Lin & D Banjevic & A K S Jardine, 2006. "Using principal components in a proportional hazards model with applications in condition-based maintenance," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(8), pages 910-919, August.
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    Cited by:

    1. Swapnil K. Gundewar & Prasad V. Kane, 2022. "Rolling element bearing fault diagnosis using supervised learning methods- artificial neural network and discriminant classifier," 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. 13(6), pages 2876-2894, December.
    2. Bahareh Tajiani & Jørn Vatn, 2023. "Adaptive remaining useful life prediction framework with stochastic failure threshold for experimental bearings with different lifetimes under contaminated condition," 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. 14(5), pages 1756-1777, October.
    3. Neeraj Khera & Shakeb A. Khan & Obaidur Rahman, 2020. "Valve regulated lead acid battery diagnostic system based on infrared thermal imaging and fuzzy algorithm," 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. 11(3), pages 614-624, June.
    4. Wassim R. Abou Ghaida & Ayman Baklizi, 2022. "Prediction of future failures in the log-logistic distribution based on hybrid censored data," 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. 13(4), pages 1598-1606, August.
    5. Ding, Yifei & Jia, Minping & Miao, Qiuhua & Huang, Peng, 2021. "Remaining useful life estimation using deep metric transfer learning for kernel regression," Reliability Engineering and System Safety, Elsevier, vol. 212(C).

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