| Full text | |
| Author(s): |
Diego Carvalho do Nascimento
[1]
;
Pedro Luiz Ramos
[2]
;
André Ennes
[3]
;
Camila Cocolo
[4]
;
Márcio José Nicola
[5]
;
Carlos Alonso
[6]
;
Luiz Gustavo Ribeiro
[7]
;
Francisco Louzada
[8]
Total Authors: 8
|
| Affiliation: | [1] Universidade de São Paulo. Instituto de Ciências Matemáticas e de Computação - Brasil
[2] Universidade de São Paulo. Instituto de Ciências Matemáticas e de Computação - Brasil
[3] Universidade de São Paulo. Instituto de Ciências Matemáticas e de Computação - Brasil
[4] Universidade de São Paulo. Instituto de Ciências Matemáticas e de Computação - Brasil
[5] Universidade de São Paulo. Instituto de Ciências Matemáticas e de Computação - Brasil
[6] Universidade de São Paulo. Instituto de Ciências Matemáticas e de Computação - Brasil
[7] Universidade de São Paulo. Instituto de Ciências Matemáticas e de Computação - Brasil
[8] Universidade de São Paulo. Instituto de Ciências Matemáticas e de Computação - Brasil
Total Affiliations: 8
|
| Document type: | Journal article |
| Source: | Gestão & Produção; v. 27, n. 4 2020-07-27. |
| Abstract | |
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. (AU) | |
| FAPESP's process: | 17/25971-0 - Statistical inference of complex systems |
| Grantee: | Pedro Luiz Ramos |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |