Tracking gearbox degradation based on stable distribution parameters: A case study

Author(s):  
Jianshe Kang ◽  
Xinghui Zhang ◽  
Tongdan Jin
2020 ◽  
Vol 143 (1) ◽  
Author(s):  
André L. R. Alves ◽  
T. A. Netto

Abstract This work presents a methodology for evaluating the uncontrolled external leakage probability of a subsea well during the production phase. Based on a barrier diagram, an algorithm for possible leak path identification is proposed, considering different operation modes: gas lift operation, free-flowing, or well closed at the subsea Christmas tree. Considering the equivalency between these paths and the minimum cut sets from a fault tree modeling, the uncontrolled external leakage probability is calculated using the upper bound approximation. The effect of common cause failures is considered for the failure mode fail-to-close-valve. The instantaneous availability function of each component is considered. Non-repairable, repairable, and periodically tested items are used. Probability distribution parameters are estimated in order to make a case study. The failure rate functions determined are used as input for the proposed model, regarding the following failure modes: fail-to-close, external-leakage, and internal-leakage at the closed position. Finally, failure probability results and sensitivity analysis are demonstrated for a base case study. Parameters like time between tests, inspections, and component reliability are varied in order to identify the impact on the uncontrolled external leakage probability. The main objective of the proposed methodology is to support decision-making on the well integrity management system during the production phase of a subsea well. To this end, actual and reliable input data should be considered.


2014 ◽  
Vol 638-640 ◽  
pp. 1822-1827
Author(s):  
Hao Li

In this paper, the probabilistic model is used to quantify the uncertainty of structural resistance, and the convex model is used to quantify the seismic uncertainty. The distribution parameters for the probabilistic model, together with the interval range for the convex model, are obtained through pushover analysis. Two-level function equation method is employed to calculate the seismic reliability of the structure. Case study shows that compared with the classical probability method, the proposed method is more simple and reasonable for seismic reliability analysis.


2018 ◽  
Vol 35 (8) ◽  
pp. 1639-1652 ◽  
Author(s):  
Jawad Hassan ◽  
Tariq Aldowaisan ◽  
Mustapha Nourelfath

Purpose The purpose of this paper is to study the relationship between reported sigma levels and actual failure rates (FRs) of gamma-distributed processes. The added complexity of the non-normality behavior of the gamma distribution is analyzed for the case of the cycle time (CT) of a real procurement process from the oil and gas industry. Then, recommendations and guidelines for the application of Six Sigma methodology for the case study are proposed. Design/methodology/approach Sensitivity analysis is conducted to study the relationship between gamma distribution parameters and FRs considering different quality levels. Then, adjustments for implementing Six Sigma programs for gamma processes are proposed. These adjustments consist of first determining the appropriate probability distribution, the standard CT and the due date, followed by setting performance zones and improvement strategies on target gamma parameters that yield the minimal FR. Findings For gamma-distributed processes, simply reporting the sigma level is not sufficient to capture the main characteristics of the process. These characteristics include process FR, mean setting, shape, spread and amount of variation reduction (i.e. improvement effort) required. That is why caution must be exercised when dealing with one-sided non-normal quality characteristics such as CT. Originality/value To the authors’ knowledge, this is the first time that the Six Sigma performance has been evaluated for gamma processes to analyze the link between Six Sigma FRs and gamma distribution parameters leading to the development of a modified Six Sigma methodology for non-normal processes.


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