1. Zhai S, Gehring B, Reinhart G (2021) Enabling predictive maintenance integrated production scheduling by operation-specific health prognostics with generative deep learning. J Manuf Syst 61:830–855 2. Pech M, Vrchota J, Bednar J (2021) Predictive maintenance and intelligent sensors in smart factory: review. Sensors (Basel) 21:1470. https://doi.org/10.3390/s21041470 3. Mohan TR, Roselyn JP, Uthra RA et al (2021) Intelligent machine learning based total productive maintenance approach for achieving zero downtime in industrial machinery. Comput Ind Eng 157:107267. https://doi.org/10.1016/j.cie.2021.107267 4. Huynh KT (2021) An adaptive predictive maintenance model for repairable deteriorating systems using inverse Gaussian degradation process. Reliab Eng Syst Saf 213:107695. https://doi.org/10.1016/j.ress.2021.107695 5. She D, Jia M (2021) A BiGRU method for remaining useful life prediction of machinery. Measurement 167:108277. https://doi.org/10.1016/j.measurement.2020.108277 6. Jamil N, Hassan MF, Lim SK et al (2021) Predictive maintenance for rotating machinery by using vibration analysis. J Mech Eng Sci 15:8289–8299 7. Shi Y, Lu ZZ, Huang HZ et al (2022) A new preventive maintenance strategy optimization model considering lifecycle safety. Reliab Eng Syst Saf 221:108325. https://doi.org/10.1016/j.ress.2022.108325 8. Xue B, Xu F, Huang X et al (2022) Improved similarity based prognostics method for turbine engine degradation with degradation consistency test. Appl Intell 52:10181–10201 9. Serradilla O, Zugasti E, Rodriguez J et al (2022) Deep learning models for predictive maintenance: a survey, comparison, challenges and prospects. Appl Intell 52:10934–10964 10. Serradilla O, Zugasti E, de Okariz JR et al (2022) Methodology for data-driven predictive maintenance models design, development and implementation on manufacturing guided by domain knowledge. Int J Comput Integr Manuf 35:1310–1334 11. Popescu TD, Aiordachioaie D, Culea-Florescu A (2022) Basic tools for vibration analysis with applications to predictive maintenance of rotating machines: an overview. Int J Adv Manuf Technol 118:2883–2899 12. Ouadah A, Zemmouchi-Ghomari L, Salhi N (2022) Selecting an appropriate supervised machine learning algorithm for predictive maintenance. Int J Adv Manuf Technol 119:4277–4301 13. Meng HX, Liu X, Xing JD et al (2022) A method for economic evaluation of predictive maintenance technologies by integrating system dynamics and evolutionary game modelling. Reliab Eng Syst Saf 222:108424. https://doi.org/10.1016/j.ress.2022.108424 14. Al Hanbali A, Saleh H, Ullah N (2022) Two-threshold control limit policy in condition-based maintenance. Qual Reliab Eng Int 38:2170–2187 15. Olde Keizer MCA, Flapper SDP, Teunter RH (2017) Conditionbased maintenance policies for systems with multiple dependent components: a review. Eur J Oper Res 261:405–420 16. Ruschel E, Santos EAP, Loures EdFR (2017) Industrial maintenance decision-making: a systematic literature review. J Manuf Syst 45:180–194 17. Jerbi A, Hachicha W, Aljuaid AM et al (2022) Multi-objective design optimization of flexible manufacturing systems using design of simulation experiments: a comparative study. Machines 10:247. https://doi.org/10.3390/machines10040247 18. Nabi HZ, Aized T, Riaz F (2022) Modeling, analysis and optimization of carousel-based flexible manufacturing system. J Ind Prod Eng 39:479–493 19. Zhang S, Li S, Wang H et al (2022) An intelligent manufacturing cell based on human–robot collaboration of frequent task learning for flexible manufacturing. Int J Adv Manuf Technol 120:5725–5740 20. Maganha I, Silva C, Ferreira LMDF (2018) Understanding reconfigurability of manufacturing systems: an empirical analysis. J Manuf Syst 48:120–130 21. Rösiö C, Aslam T, Srikanth KB et al (2019) Towards an assessment criterion of reconfigurable manufacturing systems within the automotive industry. Procedia Manuf 28:76–82 22. Bortolini M, Galizia FG, Mora C (2018) Reconfigurable manufacturing systems: literature review and research trend. J Manuf Syst 49:93–106 23. Dahmani A, Benyoucef L, Mercantini JM (2022) Toward sustainable reconfigurable manufacturing systems (SRMS): past, present, and future. Procedia Comput Sci 200:1605–1614 24. Zhang WY, Gan J, Hou QY (2022) Joint decision of condition-based maintenance and production scheduling for multicomponent systems. Proc Inst Mech Eng Part B J Eng Manuf 236:726–740 25. Zhai SM, Kandemir MG, Reinhart G (2022) Predictive maintenance integrated production scheduling by applying deep generative prognostics models: approach, formulation and solution. Prod Eng Res Dev 16:65–88 26. Ghaleb M, Taghipour S, Zolfagharinia H (2021) Real-time integrated production-scheduling and maintenance-planning in a flexible job shop with machine deterioration and conditionbased maintenance. J Manuf Syst 61:423–449 27. Rokhforoz P, Fink O (2022) Maintenance scheduling of manufacturing systems based on optimal price of the network. Reliab Eng Syst Saf 217:108088. https://doi.org/10.1016/j.ress.2021.108088 28. Ladj A, Tayeb FBS, Varnier C (2021) Hybrid of metaheuristic approaches and fuzzy logic for the integrated flowshop scheduling with predictive maintenance problem under uncertainties. Eur J Ind Eng 15:675–710 29. Dong Y, Xia T, Fang X et al (2019) Prognostic and health management for adaptive manufacturing systems with online sensors and flexible structures. Comput Ind Eng 133:57–68 30. Wang YK, Li XP, Chen JY et al (2022) A condition-based maintenance policy for multi-component systems subject to stochastic and economic dependencies. Reliab Eng Syst Saf 219:108174. https://doi.org/10.1016/j.ress.2021.108174 31. Oakley JL, Wilson KJ, Philipson P (2022) A condition-based maintenance policy for continuously monitored multi-component systems with economic and stochastic dependence. Reliab Eng Syst Saf 222:108321. https://doi.org/10.1016/j.ress.2022.108321 32. Dinh DH, Do P, Iung B (2022) Multi-level opportunistic predictive maintenance for multi-component systems with economic dependence and assembly/disassembly impacts. Reliab Eng Syst Saf 217:108055. https://doi.org/10.1016/j.ress.2021.108055 33. Xu J, Liang ZL, Li YF et al (2021) Generalized condition-based maintenance optimization for multi-component systems considering stochastic dependency and imperfect maintenance. Reliab Eng Syst Saf 211:107592. https://doi.org/10.1016/j.ress.2021.107592 34. Han X, Wang ZL, Xie M et al (2021) Remaining useful life prediction and predictive maintenance strategies for multi-state manufacturing systems considering functional dependence. Reliab Eng Syst Saf 210:107560. https://doi.org/10.1016/j.ress.2021.107560 35. Pater I, Mitici M (2021) Predictive maintenance for multi-component systems of repairables with remaining-useful-life prognostics and a limited stock of spare components. Reliab Eng Syst Saf 214:107761. https://doi.org/10.1016/j.ress.2021.107761 36. Geng SJ, Wang XL (2022) Predictive maintenance scheduling for multiple power equipment based on data-driven fault prediction. Comput Ind Eng 164:107898. https://doi.org/10.1016/j.cie.2021.107898 37. Özgür-Ünlüakın D, Türkali B (2021) Evaluation of proactive maintenance policies on a stochastically dependent hidden multicomponent system using DBNs. Reliab Eng Syst Saf 211:107559. https://doi.org/10.1016/j.ress.2021.107559 38. Cui PH, Wang JQ, Li Y (2021) Data-driven modelling, analysis and improvement of multistage production systems with predictive maintenance and product quality. Int J Prod Res 60:6848–6865 39. Lu B, Zhou X (2019) Quality and reliability oriented maintenance for multistage manufacturing systems subject to condition monitoring. J Manuf Syst 52:76–85 40. Wang YM, Liu PD, Yao YY (2022) BMW-TOPSIS: a generalized TOPSIS model based on three-way decision. Inform Sci 607:799–818 41. Xie S (2022) PdM-database. https://github.com/shulian00/PdMdatabase. Accessed 11 Feb 2023 |