Advances in Manufacturing ›› 2022, Vol. 10 ›› Issue (2): 205-219.doi: 10.1007/s40436-021-00380-z

• ARTICLES • 上一篇    下一篇

A maintenance driven scheduling cockpit for integrated production and maintenance operation schedule

Mario Arena, Valentina Di Pasquale, Raffaele Iannone, Salvatore Miranda, Stefano Riemma   

  1. Department of Industrial Engineering, University of Salerno, Fisciano, SA, Italy
  • 收稿日期:2021-03-17 修回日期:2021-05-27 发布日期:2022-06-11
  • 通讯作者: Valentina Di Pasquale E-mail:vdipasquale@unisa.it

A maintenance driven scheduling cockpit for integrated production and maintenance operation schedule

Mario Arena, Valentina Di Pasquale, Raffaele Iannone, Salvatore Miranda, Stefano Riemma   

  1. Department of Industrial Engineering, University of Salerno, Fisciano, SA, Italy
  • Received:2021-03-17 Revised:2021-05-27 Published:2022-06-11
  • Contact: Valentina Di Pasquale E-mail:vdipasquale@unisa.it

摘要: The production and maintenance functions have objectives that are often in contrast and it is essential for management to ensure that their activities are carried out synergistically, to ensure the maximum efficiency of the production plant as well as the minimization of management costs. The current evolution of ICT technologies and maintenance strategies in the industrial field is making possible a greater integration between production and maintenance. This work addresses this challenge by combining the knowledge of the data collected from physical assets for predictive maintenance management with the possibility of dynamic simulate the future behaviour of the manufacturing system through a digital twin for optimal management of maintenance interventions. The paper, indeed, presents a supporting digital cockpit for production and maintenance integrated scheduling. The tool proposes an innovative approach to manage health data from machines being in any production system and provides support to compare the information about their remaining useful life (RUL) with the respective production schedule. The maintenance driven scheduling cockpit (MDSC) offers, indeed, a supporting decision tool for the maintenance strategy to be implemented that can help production and maintenance managers in the optimal scheduling of preventive maintenance interventions based on RUL estimation. The simulation is performed by varying the production schedule with the maintenance tasks involvement; opportune decisions are taken evaluating the total costs related to the simulated strategy and the impact on the production schedule.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-021-00380-z

关键词: Industry 4.0, Maintenance task, Production schedule, Predictive maintenance, Integrated plan

Abstract: The production and maintenance functions have objectives that are often in contrast and it is essential for management to ensure that their activities are carried out synergistically, to ensure the maximum efficiency of the production plant as well as the minimization of management costs. The current evolution of ICT technologies and maintenance strategies in the industrial field is making possible a greater integration between production and maintenance. This work addresses this challenge by combining the knowledge of the data collected from physical assets for predictive maintenance management with the possibility of dynamic simulate the future behaviour of the manufacturing system through a digital twin for optimal management of maintenance interventions. The paper, indeed, presents a supporting digital cockpit for production and maintenance integrated scheduling. The tool proposes an innovative approach to manage health data from machines being in any production system and provides support to compare the information about their remaining useful life (RUL) with the respective production schedule. The maintenance driven scheduling cockpit (MDSC) offers, indeed, a supporting decision tool for the maintenance strategy to be implemented that can help production and maintenance managers in the optimal scheduling of preventive maintenance interventions based on RUL estimation. The simulation is performed by varying the production schedule with the maintenance tasks involvement; opportune decisions are taken evaluating the total costs related to the simulated strategy and the impact on the production schedule.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-021-00380-z

Key words: Industry 4.0, Maintenance task, Production schedule, Predictive maintenance, Integrated plan