Modeling the Failure Data of a Sugarcane Harvester
DOI:
https://doi.org/10.5752/P.2316-9451.2022v10n1p3-17Keywords:
Reliability. Sugarcane harvester. Failure behavior. Lifetime. Weibull.Abstract
Reliability analysis is a methodology used to describe the behavior of equipment failures. The present work aims to model the life span of harvesters in the harvesting operations of a sugar and alcohol industry. The research procedure used is the case study. Tests were performed on an equipment that failed. The graphical method and the Chi-Square (x2) and Kolmogorov-Smirnov (K-S) adhesion tests were used to verify the distribution that best model the data sample. ProConf and R software were used to perform the analyzes. The parameters of the modeling distribution were determined. The graphs of the confidence functions R(t) and risk h(t) were obtained. The average failure time of the harvesters is 6.34 minutes. Half of the equipment failed around 6.40 minutes. The Gamma, Lognormal, Normal and Weibull distributions are considered efficient in the analysis. However, we concluded that Weibull is the distribution that best models the data. The gamma parameter of the Weibull distribution is 4.8275, indicating that the equipment is in the aging phase. In conclusion, proper maintenance for it should be a corrective one.
Downloads
Downloads
Published
How to Cite
Issue
Section
License
I (we) submit the present work, an original and unpublished manuscript, from my (our) authorship, to Abakós - Magazine of Interdisciplinary Studies on Science and Informatics, and I (we) agree that the copyright related to this work will become property of PUC Minas Publisher. No partial or full reproduction is allowed, by any means (printed or electronic), dissociated from Abakós. Any reproduction requires prior written authorization granted by the Editor.
I (we) declare there is no type of interest conflict among the subject theme, author(s), organization(s), institution(s) and person(s).
I (we) recognize that Abakós is licensed under CREATIVE COMMONS:
Licença Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0).