Maintenance scheduling in manufacturing systems based on predicted machine degradation

Yang, Zimin; Djurdjanović, Dragan; Ni, Jun

doi:10.1007/s10845-007-0047-3

Cited by 123 publications

(61 citation statements)

References 23 publications

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“…Other work has been focused on the optimization of maintenance activities scheduling based on predictive degradation models combined with various optimization methods. In [5] discrete event simulations are used to are used to estimate the effects of maintenace scheudles on the systems performance. Grey theory and evaluation diagnosis were used in [6] in order to construct preventive maintenace forecasting models for semicoductor manufacturing machine failures.…”

Section: Related Workmentioning

confidence: 99%

SelSus: Towards a reference architecture for diagnostics and predictive maintenance using smart manufacturing devices

Sayed

Lohse

Søndberg-Jeppesen

et al. 2015

2015 IEEE 13th International Conference on Industrial Informatics (INDIN)

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Section: Related Workmentioning

confidence: 99%

SelSus: Towards a reference architecture for diagnostics and predictive maintenance using smart manufacturing devices

Sayed

Lohse

Søndberg-Jeppesen

et al. 2015

2015 IEEE 13th International Conference on Industrial Informatics (INDIN)

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“…But PHM can promote the development of CBM [3]. It tries its best to reduce unplanned maintenance, to improve maintenance efficiency and to economize maintenance costs.…”

Section: ) Safetymentioning

confidence: 99%

Application of PHM for Complex Electromechanical Equipments

Wu¹,

Wen²,

Meng³

et al. 2013

Proceedings of the 3rd International Conference on Electric and Electronics

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Abstract-PHM (Prognostic and Health Management) which isproposed by American army in JSF plan has gone through the fault diagnosis, fault prognostic and system integration three phases, and develops fast in both electronic and mechanical field. In this paper, the technical superiority of PHM is analyzed, and the framework of PHM for complex electromechanical equipment is built, which contains state monitoring, information processing and health management. Furthermore, the PHM system for complex electromechanical equipment is designed. At last, its application for DTG (Dynamically Tuned Gyroscope) shows that the system is feasible and usable.

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“…Yang et al [71] focus on the scheduling of the maintenance events by taking the complex interaction between the production process and maintenance operations into account. Effects of any maintenance schedule were evaluated through a discrete-event simulation that utilizes predicted probabilities of machine failures, where predicted probabilities of failure are assumed to be available either from historical equipment reliability information or based on the newly available predictive algorithms ( [72]- [74]).…”

Section: Condition Based Maintenancementioning

confidence: 99%

“…Bris et al [112] employ the coupling of GA and simulation to find the optimal PM model that minimizes the maintenance cost for a system consisting of subsystems connected in series. Yang et al [71] propose a GA based optimization procedure for scheduling of maintenance operations in a manufacturing system using the continuous assessment and prediction of the level of performance degradation of manufacturing equipment. Effects of any maintenance schedule are evaluated through a discrete-event simulation that utilizes predicted probabilities of machine failures in the manufacturing system.…”

Section: Simulation Based Optimizationmentioning

confidence: 99%

Joint Maintenance and Production Operations Decision Making in Flexible Manufacturing Systems

Celen

Djurdjanović

2012

ASME 2012 International Manufacturing Science and Engineering Conference

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In highly flexible and integrated manufacturing systems such as those in semiconductor manufacturing, there exist strong dynamic interactions between the equipment condition, operations executed on the equipment and the resulting product quality. These interactions necessitate a methodology that integrates the decisions of maintenance scheduling and production operations. Currently, maintenance and production operations decision-making are two decoupled processes. In this paper we aim to devise an integrated decision making policy for maintenance scheduling and production sequencing with the objective of maximizing an adaptive profit function, while taking into account operation-dependent degradation models and a production target. In order to obtain the optimal decision policy, a metaheuristic method based on the results of discrete-event simulations of the target manufacturing system is used. The new approach is demonstrated in simulations of a generic cluster tool routinely used in semiconductor manufacturing. The results show that jointly making maintenance and production sequencing decisions consistently outperforms the current practice of making these decisions separately.

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Maintenance scheduling in manufacturing systems based on predicted machine degradation

Cited by 123 publications

References 23 publications

SelSus: Towards a reference architecture for diagnostics and predictive maintenance using smart manufacturing devices

SelSus: Towards a reference architecture for diagnostics and predictive maintenance using smart manufacturing devices

Application of PHM for Complex Electromechanical Equipments

Joint Maintenance and Production Operations Decision Making in Flexible Manufacturing Systems

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