Repairable Systems Reliability: Modelling, Inference, Misconceptions and their Causes.

Littlewood, Bev; Ascher, Harold; Feingold, Harry

doi:10.2307/2981951

Cited by 13 publications

(2 citation statements)

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“…The frequency of breakdown causing the failure of diesel engines is adequate. A vital concern in the engine system's performance under certain operational conditions is to guarantee the satisfactory uninterrupted operation of the equipment [1][2][3][4][5][6][7]. However, failure of components is unescapable and takes place due to the ongoing wear and tear process in working parts of the system.…”

Section: Introductionmentioning

confidence: 99%

Statistical Reliability Assessment for Small Sample of Failure Data of Dumper Diesel Engines Based on Power Law Process and Maximum Likelihood Estimation

et al. 2021

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Dumpers or dump trucks are used all over the world to move overburden from many opencast mines. Diesel engines are the main driving force behind the trucks. The frequency of damage due to the failure of diesel engines is enormous. Therefore, efforts are necessary to analyze failure to reduce the downtime periods. A detailed analysis of engine failure at the subsystem level needs to be done. Reliability analysis and maintenance planning remain the norm in this regard. The obstacle faced while analysing the reliability of dumpers was the availability of a large number of data failures. In this paper, this issue is addressed by using Common Beta Hypothesis test and Meta-analysis test. The engine is divided into five subsystems. The result shows that all five subsystems pass the CBH test and Meta-analysis test. Accordingly, the failure data is grouped. The trend test of grouped failure data shows that the Failure data of two subsystems follows the independent and identically distributed characteristics while the remaining three do not follow it. The reliability is estimated for all five subsystems. Finally, fuel supply subsystems show the highest reliability while the lowest value is seen for self-starting subsystems.

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Section: Introductionmentioning

confidence: 99%

Statistical Reliability Assessment for Small Sample of Failure Data of Dumper Diesel Engines Based on Power Law Process and Maximum Likelihood Estimation

et al. 2021

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“…Most of the open-pit mines use dumpers or dump trucks as the equipment for transporting ore from the mine. Dumpers are powered by diesel engines [1][2][3][4][5]. The engine consists of different subsystems and is the most significant functioning unit of a dumper.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Reliability control for mission-critical items

Berg¹

1987

Naval Research Logistics

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Reliability control for mission‐critical items is studied. The purpose of the control is to ensure the attainment during mission time of a required performance level which is based on suitably defined measures. This is achieved by replacing the item during normal operation time as soon as the reliability requirement is violated. We first consider the case of a known life distribution, but the main focus of the work is on the case where the life distribution is not completely specified. A Bayesian approach is taken and a replacement procedure is constructed on the basis of the overall uncertainty we face, namely, the stochastic failure process and our (continuously revised) probabilistic assessment of the underlying life distribution. The resulting replacement policy is investigated and detailed results are obtained for some common life distributions.

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Repairable Systems Reliability: Modelling, Inference, Misconceptions and their Causes.

Abstract: 3. Repairable Systems Reliability: Modelling, Inference, Misconceptions and their Causes. By H. Ascher and H. Feingold. New York, Marcel Dekker, 1984. 223 p. SFr 119. (Lecture Notes in Statistics, Vol. 7)

Cited by 13 publications

References 0 publications

Statistical Reliability Assessment for Small Sample of Failure Data of Dumper Diesel Engines Based on Power Law Process and Maximum Likelihood Estimation

Statistical Reliability Assessment for Small Sample of Failure Data of Dumper Diesel Engines Based on Power Law Process and Maximum Likelihood Estimation

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

Reliability control for mission-critical items

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