Gestão & Produção
Gestão & Produção
Artigo Original

A reliability engineering case study of sugarcane harvesters

Diego Carvalho do Nascimento; Pedro Luiz Ramos; André Ennes; Camila Cocolo; Márcio José Nicola; Carlos Alonso; Luiz Gustavo Ribeiro; Francisco Louzada

Downloads: 0
Views: 17


Abstract: The present study aimed to analyze factors associated with the equipment failures of the sugarcane harvester, whose machineries has high importance in the harvest process and cost involved. Part of the data was originally provided by a company located in the countryside of Sao Paulo State, from two machines, collected from January 2015 to August 2017, corresponding to 2.5 crops. The overall dataset was obtained from three different sources: a stop-tracking system, which provides the track of a preventive and corrective maintenance historical of the analyzed equipment; telemetry data of the equipment, captured through embedded computer systems, installed in the machine’ type under study, which provide information on its operation; and meteorological data from the Brazilian National Institute of Meteorology. Multivariate analyzes were used such as principal components and multiple regression models, therefore creating a model for prediction considering the next equipment’ break, then pointing to causes of process failures. Thus, the results point to some improvements concerned with individualized reliability scheme in order to reduce the number of corrective stops given the equipment.


Reliability, Multivariate analysis, Optimization in maintenance planning


Almeida E. F., Bomtempo J. V., Silva C. M. D. S. The performance of Brazilian biofuels. 2007.

Bendel R. B., Afifi A. A. Comparison of stopping rules in forward\stepwise” regression. Journal of the American Statistical Association. 1977;72(357):46-53.

Draper N. R., Smith H. Applied regression analysis. 2014.

Faraway J. J. Practical regression and anova using R. 2002.

Furtado C. Formação econômica do Brasil. 1986.

Gasques J. G. Wachstumstreiber der brasilianischen landwirtschaft: total efaktor produktivit¨at. EuroChoices. 2017:24-5.

Goes T., Marra R., Araújo M., Alves E., Souza M. O. Viabilidade econômica do biodiesel em Mato Grosso. Revista de Política Agrícola. 2011;20(1):39-51.

Lawless J. F. Statistical models and methods for lifetime data. 2011;362.

McCullagh P. Generalized linear models. European Journal of Operational Research. 1984;16(3):285-92.

Morrison D. Multivariate statistical methods. 1976.

Ripoli M. L. C., Ripoli T. C. C. Sistemas de colheita. 2008:671-93.

Sant’Anna A. C., Shanoyan A., Bergtold J. S., Caldas M. M., Granco G. Ethanol and sugarcane expansion in brazil: what is fueling the ethanol industry?. The International Food and Agribusiness Management Review. 2016;19(4):163-82.

Silva J., Alves M., Costa M. Planejamento de turnos de trabalho: uma abordagem no setor sucroalcooleiro com uso de simulação discreta. Gestão & Produção. 2011;18(1):73-90.

Tableman M., Kim J. S. Survival analysis using S: analysis of time-to event data. 2016.

PIB do agronegócio 2017 (janeiro a dezembro de 2017). 2017.

World trade statistical review 2016. 2016.

5ff6f6500e8825ba5c5aeabc gp Articles

Gest. Prod.

Share this page
Page Sections