Meta-Analysis and Forest Plots for Sustainability of Heavy Load Carrier Equipment Used in the Industrial Mining Environment

Somnath Chattopadhyaya, Brajeshkumar Kishorilal Dinkar, Alok Kumar Mukhopadhyay, Shubham Sharma and José Machado
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Somnath Chattopadhyaya: Department of Mechanical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, India
Brajeshkumar Kishorilal Dinkar: Department of Mining Machinery Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, India
Alok Kumar Mukhopadhyay: Department of Mining Machinery Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, India
Shubham Sharma: Department of Mechanical Engineering, Main Campus, IK Gujral Punjab Technical University, Kapurthala 144603, India
José Machado: MEtRICs Research Center, Campus of Azurém, University of Minho, 4800-058 Guimarães, Portugal

Sustainability, 2021, vol. 13, issue 15, 1-15

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.

Keywords: reliability; Time between Failures (TBF); meta-analysis test; level of heterogeneity; sensitivity analysis; forest plot (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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