Abstract
It is a common recommendation not to attempt a reliability analysis with a small sample size. However, this is feasible after considering certain statistical methods. One such method is meta-analysis, which can be considered to assess the effectiveness of a small sample size by combining data from different studies. The method explores the presence of heterogeneity and the robustness of the fresh large sample size using sensitivity analysis. The present study describes the approach in the reliability estimation of diesel engines and the components of industrial heavy load carrier equipment used in mines for transporting ore. A meta-analysis is carried out on field-based small-sample data for the reliability of different subsystems of the engine. The level of heterogeneity is calculated for each subsystem, which is further verified by constructing a forest plot. The level of heterogeneity was 0 for four subsystems and 2.23% for the air supply subsystem, which is very low. The result of the forest plot shows that all the plotted points mostly lie either on the center line (line of no effect) or very close to it, for all five subsystems. Hence, it was found that the grouping of an extremely small number of failure data is possible. By using this grouped TBF data, reliability analysis could be very easily carried out.
Highlights
Mining and the mineral industry are often the backbone of a country’s economy.Mining of ore in India is mostly done by the open-pit mining method
Meta-analysis has not been used on industrial equipment, especially on dumper engines
The problem associated with reliability analysis using an extremely small amount of failure data has been solved in this paper
Summary
Mining and the mineral industry are often the backbone of a country’s economy. Mining of ore in India is mostly done by the open-pit mining method. G Wang et al [14] proposed a method that determined which small-sample failure data of diesel engines could be used for a reliability estimation. The methods used for reliability analyses of small-size data mostly make use of the Bayesian approach, the FMECA, and the Monte. Studies on the reliability of engine subsystems that make use of an extremely small sample size of failure data have not been reported. The present study uses the metaanalysis test, which has been used only in the medical field and not in the industrial field Using this method, the small failure data of any machine or system (in this case, the diesel engine) can be grouped and used for further reliability analysis. After the meta-analysis test, the failure data may be grouped and used for statistical analysis
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