Research on Maintenance Decision-making Method of Machinery Considering Life Prediction Error

doi:10.3901/JME.260304

Abstract

Abstract: Predictive maintenance can be based on the health state degradation trends of the machinery or the remaining life of the machinery and other indicators to rationally develop the maintenance plan, to select optimal time for machinery maintenance, so as to ensure the reliability of the machinery and reduce the maintenance costs. However, in engineering practice, the performance of the predictive maintenance decision-making model heavily relies on the accuracy of the life prediction methods. Both overestimated and underestimated remaining life may cause the machinery operation and maintenance to miss the optimal time for maintenance, and severely restrict the engineering application effect of the maintenance decision-making model. Therefore, how to mitigate the negative impact of prediction errors on maintenance strategies has become a critical issue that urgently needs to be addressed in the field of intelligent maintenance for machinery. Considering the significant impact of real remaining useful life on life prediction errors, this paper proposes a non-periodic maintenance strategy considering life prediction errors. Firstly, a dynamic relationship between prediction error distribution and real remaining life is established through error modeling. Subsequently, the impacts of prediction errors are categorized into three decision outcomes - timely maintenance, unexpected maintenance and premature maintenance, with corresponding maintenance costs calculated for each scenario. Finally, a non-periodic maintenance model is developed based on machinery life distribution to generate maintenance schedules, with the integrated maintenance cost rate calculated accordingly, forming a non-periodic maintenance decision-making framework. The proposed strategy is validated through a case study on machine tool milling cutter maintenance. Results demonstrate that the methodology can effectively arrange inspection plans while considering prediction errors, achieving significant maintenance cost reduction.

Key words: machinery, maintenance decision, life prediction error, non-periodic maintenance

CLC Number:

TH17

LEI Yaguo, CHEN Zexun, YANG Bin, WU Tonghai, KONG Detong. Research on Maintenance Decision-making Method of Machinery Considering Life Prediction Error[J]. Journal of Mechanical Engineering, 2026, 62(13): 155-165.

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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260304

http://www.cjmenet.com.cn/EN/Y2026/V62/I13/155

References

[1] LEI Y，LI N，LI X. Big data-driven intelligent fault diagnosis and prognosis for mechanical systems[M]. Singapore：Springer，2023.
[2] 胡昌华，樊红东，王兆强. 设备剩余寿命预测与最优维修决策[M]. 北京：国防工业出版社，2018. HU Changhua，FAN Hongdong，WANG Zhaoqiang. Residual life prediction and optimal maintenance decision for a piece of equipment[M]. Beijing：National Defense Industry Press，2018.
[3] YU W，TU W，KIM I Y，et al. A nonlinear-drift-driven Wiener process model for remaining useful life estimation considering three sources of variability[J]. Reliability Engineering & System Safety，2021，212：107631.
[4] 谢志强，周伟，余泽睿. 动态调整设备维护开始时间的综合调度算法[J]. 机械工程学报，2021，57(4)：240-246. XIE Zhiqiang，ZHOU Wei，YU Zerui. Integrated scheduling algorithm for dynamic adjustment of equipment maintenance start time[J]. Journal of Mechanical Engineering，2021，57(4)：240-246.
[5] GRALL A，DIEULLE L，BéRENGUER C，et al. Continuous-time predictive-maintenance scheduling for a deteriorating system[J]. IEEE Transactions on Reliability，2002，51(2)：141-150.
[6] ALBERTI A R，CAVALCANTE C A V，SCARF P，et al. Modelling inspection and replacement quality for a protection system[J]. Reliability Engineering & System Safety，2018，176：145-153.
[7] ZHANG Y，OUYANG L，MENG X，et al. Condition-based maintenance considering imperfect inspection for a multi-state system subject to competing and hidden failures[J]. Computers & Industrial Engineering，2024，188：109856.
[8] DRIESSEN J P C，PENG H，VAN HOUTUM G J. Maintenance optimization under non-constant probabilities of imperfect inspections[J]. Reliability Engineering & System Safety，2017，165：115-123.
[9] JAIN A K，DHADA M，HERNANDEZ M P，et al. Influence of imperfect prognostics on maintenance decisions[C]//Proceedings of the 11th International Conference on Precision，Meso，Micro & Nano Engineering (COPEN 11)，December 12-14，2019，Indore，India. Indore：Indian Institute of Technology Indore，2019.
[10] 董青，郑建飞，胡昌华，等. 基于两阶段自适应Wiener过程的剩余寿命预测方法[J]. 自动化学报，2022，48(02)：539-553. DONG Qing，ZHENG Jianfei，HU Changhua，et al. Remaining useful life prognostic method based on two-stage adaptive Wiener process[J]. Acta Automatica Sinica，2022，48(02)：539-553.
[11] ZHANG Z，SI X，HU C，et al. Degradation data analysis and remaining useful life estimation：a review on Wiener-process-based methods[J]. European Journal of Operational Research，2018，271(3)：775-796.
[12] OKUMURA S，JARDINE A K S，YAMASHINA H. An inspection policy for a deteriorating single-unit system characterized by a delay-time model[J]. International Journal of Production Research，1996，34(9)：2441-2460.
[13] VAN ZYL J P，ENGELBRECHT A P. Set-based particle swarm optimisation：a review[J]. Mathematics，2023，11(13)：2980.
[14] SAYYAD S，KUMAR S，BONGALE A，et al. Data-driven remaining useful life estimation for milling process：sensors，algorithms，datasets，and future directions[J]. IEEE Access，2021，9：110255-86.
[15] WANG Z，WANG W，HU C，et al. A prognostic-information-based order-replacement policy for a non-repairable critical system in service[J]. IEEE Transactions on Reliability，2015，64(2)：721-728.
[16] YAN T，LEI Y，WANG B，et al. Joint maintenance and spare parts inventory optimization for multi-unit systems considering imperfect maintenance actions[J]. Reliability Engineering & System Safety，2020，202：106994.
[17] HAJIPOUR Y，TAGHIPOUR S. Non-periodic inspection optimization of multi-component and k-out-of-m systems[J]. Reliability Engineering & System Safety，2016，156：228-243.