Preventive maintenance optimization of sugarcane harvester machine based on FT-Bayesian network reliability
International Journal of Quality & Reliability Management
ISSN: 0265-671X
Article publication date: 14 August 2020
Issue publication date: 15 February 2021
Abstract
Purpose
The purpose of this paper is to optimize the preventive maintenance based on fault tree (FT)–Bayesian network (BN) reliability for sugarcane harvester machine as a fundamental machine in the sugar industry that must be operated failure-free during a given period of the harvesting process.
Design/methodology/approach
To determine machine reliability using the algorithm developed based on mapping FTs into BNs, the common failures of 168 machines were carefully investigated over 12 years (2007–2019). This algorithm was then used to predict the harvester reliability, estimate delays by machine downtimes and their consequences on white sugar production losses that can be reduced by optimizing the preventive maintenance scheduling.
Findings
The optimization of preventive maintenance scheduling based on estimated reliability of sugarcane harvester machines using FT–BNs can reduce white sugar production losses, the operation-stopping breakdowns and the downtime costs as a crisis that the sugar industry is facing.
Practical implications
Machine reliability gradually decreased by 31.08% approximately, which resulted in a working time loss of 26% in the 2018–19 harvesting season. In total, the white sugar losses were estimated as 204.17 tons for burnt canes and 114.53 tons for green canes. The losses of the 2018–19 harvesting season have been 11.85 times greater than the first harvesting season. The proposed maintenance interval for critical subsystems including the hydraulic, chopper and base cutter were obtained as 1.815, 1.12 and 1.05 h, respectively.
Originality/value
In this study, a new approach was used to optimize preventive maintenance to reduce delays and their implications upon costs in time, inconvenience and white sugar losses. The FT–BNs algorithm was found a useful tool that was over-fitting of failure occurrence probabilities data for sugarcane harvester machine.
Keywords
Acknowledgements
The present study was financially supported by the Research and Technology Deputy of Agricultural Sciences and Natural Resources University of Khuzestan and also Iran National Science Foundation [(INSF), grant number 97010993].Conflict of interest: The authors declare that they have no conflict of interest.
Citation
Afsharnia, F., Marzban, A., Asoodar, M. and Abdeshahi, A. (2021), "Preventive maintenance optimization of sugarcane harvester machine based on FT-Bayesian network reliability", International Journal of Quality & Reliability Management, Vol. 38 No. 3, pp. 722-750. https://doi.org/10.1108/IJQRM-01-2020-0015
Publisher
:Emerald Publishing Limited
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