A predictive maintenance model for optimizing production schedule using deep neural networks-Reference-Cited by-同舟云学术

A predictive maintenance model for optimizing production schedule using deep neural networks

Published:2022-01 Issue: Volume:62 Page:450-462
ISSN:0278-6125
Container-title:Journal of Manufacturing Systems
language:en
Short-container-title:Journal of Manufacturing Systems

Author:

Zonta Tiago,da Costa Cristiano André,Zeiser Felipe A.,de Oliveira Ramos Gabriel,Kunst Rafael,da Rosa Righi Rodrigo

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Hardware and Architecture,Software,Control and Systems Engineering

Reference49 articles.

1. On a versatile scheduling concept of maintenance activities for increased availability of production resources;Nikolakis;Proc CIRP,2018

2. Odds-based decision-making tool for opportunistic production-maintenance synchronization;Levrat;Int J Prod Res,2008

3. A decision-making approach for job shop scheduling with job depending degradation and predictive maintenance;Fitouri;IFAC-PapersOnLine,2016

4. An integrated prognostic based hybrid genetic-immune algorithm for scheduling jobs and predictive maintenance;Ladj;2016 IEEE congress on evolutionary computation, CEC, July 2016,2016

5. Integrated and adaptive AR maintenance and shop-floor rescheduling;Mourtzis;Comput Ind,2021

Cited by 46 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Smart scheduling for next generation manufacturing systems: a systematic literature review;Journal of Intelligent Manufacturing;2024-09-09

2. Prediction maintenance based on vibration analysis and deep learning — A case study of a drying press supported on a Hidden Markov Model;Applied Soft Computing;2024-09

3. A hybrid deep learning approach to integrate predictive maintenance and production planning for multi-state systems;Journal of Manufacturing Systems;2024-06

4. Comparison study of dispatching rules and heuristics for online scheduling of single machine scheduling problem with predicted release time jobs;Expert Systems with Applications;2024-06

5. Digital twin-enabled robust production scheduling for equipment in degraded state;Journal of Manufacturing Systems;2024-06