Content area
Full Text
Received Dec 7, 2017; Revised Jan 22, 2018; Accepted Feb 14, 2018
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1. Introduction
The arrival of the sugarcane culture in Brazil has had a significant impact on the national economy, which led the country to become the largest producer in the world [1]. Its subproducts are used in the food and chemical industries, as well as in electricity generation and fuel production. Mechanized harvesting is one of the most important stages in the sugar and ethanol mills, since it provides the raw material with quality, time, and competitive costs for later processing. Among the used machines in the mechanized harvest, the harvesters stand out for having a large number of corrective stops, given the functionality in such extreme environmental conditions. In addition, its operation is in a regime of 24 hours on the workdays, having impact on fatigue and wear of their parts. During operation, the harvester processes an average of 20 tons of sugarcane per hour and its malfunction may lead to major losses; therefore, an effective maintenance approach is of keen interest [2].
Reliability-centered maintenance consists of determining the most effective maintenance approach [3, 4]. This process was firstly developed in the aviation industry for deciding what maintenance work is needed to keep aircraft airborne, driven by the need to improve reliability, while reducing the cost of maintenance [5]. Reliability analysis can be used to estimate time-related parameters to the next machine stop [6], providing information to manage and control the preventive maintenance of harvesters which could result in increased production and has potential for cost savings.
In reliability, common procedures are usually based on the assumption that the data follows a Weibull distribution. Introduced by Weibull [7], this distribution has convenient mathematical properties and its physiological failure process arises in many areas (see Manton and Yashin [8]). Additionally, McCool [9] provided an extensive discussion about its use in reliability. However, this distribution cannot be used to describe data with nonmonotone hazard function (bathtub, upside-down bathtub, to list a few). To overcome this problem, many generalizations of the standard Weibull distribution have...