loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: E. Garcia 1 ; N. Montes 2 and M. Alacreu 2

Affiliations: 1 Ford Spain, Polígono Industrial Ford S/N, CP 46440, Almussafes, Valencia and Spain ; 2 Department of Mathematics, Physics and Technological Sciences, University CEU Cardenal Herrera, C/ San Bartolom, 55, Alfara del Patriarca, Valencia and Spain

Keyword(s): Knowledge-driven Support System, Maintenance, Prognosis, Mini-term, Change Point.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Business Analytics ; Cardiovascular Technologies ; Computing and Telecommunications in Cardiology ; Data Engineering ; Decision Support Systems ; Decision Support Systems, Remote Data Analysis ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Knowledge-Based Systems ; Knowledge-Based Systems Applications ; Symbolic Systems

Abstract: This paper presents how to design a Knowledge-driven Maintenance Support System (MSS) to prognostic breakdowns in production lines and how it affects to the production rate. The system is based on the sub-cycle time monitorization and how the cycle time variability of machine parts can be used as a deterioration indicator that could describe the dynamic of the failure for the machine parts. For this proposal, a novel model based on mini-terms and micro-terms introduced in our previous work as a machine subdivision is used. A mini-term subdivision can be selected by the expert team for several reasons, the replacement of a machine part or simply to analyze the machine more adequately. (A micro-term is a component from a mini-term and it can be as small as the user wishes. Without loss of generality, the paper focuses its attention on a welding line at Ford Motor Company located at Almusafes (Valencia) where a welding unit was isolated and tested for some particular pathologies. The cy cle time of each mini-term is measured by changing the deteriorated components in the cycle time. The deterioration of the parts (a proportional valve, a cylinder, an electrical transformer, the robot speed and the loss of pressure) are tested within the range of normal production, which is the range that cannot be detected by alarms or maintenance workers but when the change point is occured. The statistical analysis of the data obtained in the experiments allows us to define the rules that govern the decisions for the real-time Knowledge-driven MSS. This analysis and the welding line simulation also allows us to know the loss of productivity when the change point occurs. In the worst case, the welding line reduces their production rate almost 40%. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 34.235.150.151

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Garcia, E.; Montes, N. and Alacreu, M. (2018). Towards a Knowledge-driven Maintenance Support System for Manufacturing Lines. In Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-321-6; ISSN 2184-2809, SciTePress, pages 43-54. DOI: 10.5220/0006834800430054

@conference{icinco18,
author={E. Garcia. and N. Montes. and M. Alacreu.},
title={Towards a Knowledge-driven Maintenance Support System for Manufacturing Lines},
booktitle={Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2018},
pages={43-54},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006834800430054},
isbn={978-989-758-321-6},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Towards a Knowledge-driven Maintenance Support System for Manufacturing Lines
SN - 978-989-758-321-6
IS - 2184-2809
AU - Garcia, E.
AU - Montes, N.
AU - Alacreu, M.
PY - 2018
SP - 43
EP - 54
DO - 10.5220/0006834800430054
PB - SciTePress