Optimization of Intelligent Maintenance System in Smart Factory Using State Space Search Algorithm DOI Creative Commons

Nuttawan Thongtam,

Sukree Sinthupinyo, Achara Chandrachai

и другие.

Applied Sciences, Год журнала: 2024, Номер 14(24), С. 11973 - 11973

Опубликована: Дек. 20, 2024

With the continuous growth of Industry 4.0 (I4.0), industrial sector has transformed into smart factories, enhancing business competitiveness while aiming for sustainable development organizations. Machinery is a critical component and key to success production in factory. Minimizing unplanned downtime (UPDT) poses significant challenge designing an effective maintenance system. In era 4.0, most widely adopted frameworks are intelligent systems (IMSs), which integrate predictive with computerized systems. IMSs tools designed efficiently plan cycles each machine This research presents application search algorithm named state space (SSS) conjunction newly IMS, aimed at optimizing routines by identifying optimal timing cycles. The design began new IMS concept that incorporates three elements: automation pyramid standard, Industrial Internet Things (IIoT) sensors, management system (CMMS). CMMS collects data from database, real-time parameters gathered via IIoT sensors supervisory control acquisition (SCADA) provides summary total cost remaining lifetime equipment. By integrating SSS algorithms, optimized cycle solutions manager, focusing on minimizing costs maximizing Moreover, algorithms take account risks associated routines, following factory standards such as failure mode effects analysis (FMEA). approach well suited factories helps reduce UPDT.

Язык: Английский

Advanced hybrid frameworks for water quality index prediction DOI

Mohammad Ehteram,

Somayeh Soltani-Gerdefaramarzi

Ain Shams Engineering Journal, Год журнала: 2025, Номер 16(8), С. 103478 - 103478

Опубликована: Май 13, 2025

Язык: Английский

Процитировано

0
An Epsilon constraint-based evolutionary algorithm and multi-objective quality metrics for combined economic emission dispatch problem DOI Creative Commons
Kit Yan Chan, Ka Fai Cedric Yiu, Dowon Kim

и другие.

Neural Computing and Applications, Год журнала: 2025, Номер unknown

Опубликована: Июнь 1, 2025

Язык: Английский

Процитировано

0
Recent Progress in Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes DOI Creative Commons
Sheng Du, Zixin Huang, Li Jin

и другие.

Algorithms, Год журнала: 2024, Номер 17(12), С. 569 - 569

Опубликована: Дек. 12, 2024

This editorial discusses recent progress in data-driven intelligent modeling and optimization algorithms for industrial processes. With the advent of Industry 4.0, amalgamation sophisticated data analytics, machine learning, artificial intelligence has become pivotal, unlocking new horizons production efficiency, sustainability, quality assurance. Contributions to this Special Issue highlight innovative research advancements work-sampling analysis, process choreography discovery, ship scheduling maritime rescue, variability monitoring, hybrid economic emission dispatches, controlled oscillations smart structures. These studies collectively contribute body knowledge on optimization, offering practical solutions theoretical frameworks address complex challenges.

Язык: Английский

Процитировано

1
Optimization of Intelligent Maintenance System in Smart Factory Using State Space Search Algorithm DOI Creative Commons

Nuttawan Thongtam,

Sukree Sinthupinyo, Achara Chandrachai

и другие.

Applied Sciences, Год журнала: 2024, Номер 14(24), С. 11973 - 11973

Опубликована: Дек. 20, 2024

With the continuous growth of Industry 4.0 (I4.0), industrial sector has transformed into smart factories, enhancing business competitiveness while aiming for sustainable development organizations. Machinery is a critical component and key to success production in factory. Minimizing unplanned downtime (UPDT) poses significant challenge designing an effective maintenance system. In era 4.0, most widely adopted frameworks are intelligent systems (IMSs), which integrate predictive with computerized systems. IMSs tools designed efficiently plan cycles each machine This research presents application search algorithm named state space (SSS) conjunction newly IMS, aimed at optimizing routines by identifying optimal timing cycles. The design began new IMS concept that incorporates three elements: automation pyramid standard, Industrial Internet Things (IIoT) sensors, management system (CMMS). CMMS collects data from database, real-time parameters gathered via IIoT sensors supervisory control acquisition (SCADA) provides summary total cost remaining lifetime equipment. By integrating SSS algorithms, optimized cycle solutions manager, focusing on minimizing costs maximizing Moreover, algorithms take account risks associated routines, following factory standards such as failure mode effects analysis (FMEA). approach well suited factories helps reduce UPDT.

Язык: Английский

Процитировано

0