Automated guided vehicles (AGVs) are be-
ing extensively used for intelligent transportation and distribution of materials in warehouses and autoproduction lines due to their attributes of high efficiency and low costs. Such vehicles travel along a predefined route to deliver desired tasks without the supervision of an operator. Much effort in this area has focused primarily on route optimisation and traffic management of
these AGVs. However, the health management of these vehicles and their optimal mission configuration have received little attention. To assure their added value, taking a typical AGV transport system as an example, the capability to evaluate reliability issues in AGVs are investigated in this paper. Following a Failure Modes Effects and Criticality Analysis (FMECA), the reliability of the AGV system is analysed via Fault Tree
Analysis (FTA) and the vehicles mission reliability is evaluated using the Petri net (PN) method. By performing the analysis the acceptability of failure of the mission can be analysed, and hence the service capability and potential profit of the AGV system can be
reviewed and the mission altered where performance is unacceptable. The PN method could easily be extended to have the capability to deal with eet AGV mission
reliability assessment.
Funding
The work reported in this paper aligns
to the working being researched as part of the EPSRC grant EP/K014137/1.
History
School
Aeronautical, Automotive, Chemical and Materials Engineering
Department
Aeronautical and Automotive Engineering
Published in
The International Journal of Advanced Manufacturing Technology
Volume
92
Issue
5-8
Pages
1825 - 1837
Citation
YAN, R., JACKSON, L.M. and DUNNETT, S.J., 2017. Automated guided vehicle mission reliability modelling using a combined fault tree and Petri net approach. The International Journal of Advanced Manufacturing Technology, 92(5-8), pp.1825-1837.
Publisher
Springer Verlag (Germany)
Version
VoR (Version of Record)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution(CC BY 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/
Acceptance date
2017-02-16
Publication date
2017-03-21
Notes
This is an open access article published by Springer and distributed under the terms of the Creative Commons Attribution Licence, https://creativecommons.org/licenses/by/4.0/