Sequential Testing for Comparison of the Mean Time Between Failures for Two Systems

2007 ◽  
Vol 56 (2) ◽  
pp. 321-331 ◽  
Author(s):  
Yefim H. Michlin ◽  
Genady Ya. Grabarnik
Keyword(s):  
Sequential Testing ◽  
Mean Time Between Failures ◽  
Time Between Failures ◽  
Two Systems ◽  
The Mean ◽  
Mean Time

Comparison of the Mean Time Between Failures for Two Systems Under Short Tests

2009 ◽  
Vol 58 (4) ◽  
pp. 589-596 ◽  
Author(s):  
Y.H. Michlin ◽  
G.Y. Grabarnik ◽  
E. Leshchenko
Keyword(s):  
Mean Time Between Failures ◽  
Time Between Failures ◽  
Two Systems ◽  
The Mean ◽  
Mean Time

scholarly journals Life Cycle Cost versus Life Cycle Investment – A new Approach

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Life Cycle ◽  
Life Cycle Cost ◽  
Economic Life ◽  
Maintenance Policy ◽  
Mean Time Between Failures ◽  
Time Between Failures ◽  
The Mean ◽  
Physical Assets ◽  
Physical Asset ◽  
Mean Time

The paper proposes a model for the life cycle of physical assets that includes the maintenance policy, because it has direct implications on the equipment’s Return On Investment (ROI) and Life Cycle Cost; the developed model can be applied to any type of physical asset. The model is called Life Cycle Investment (LCI) instead of the traditional Life Cycle Cost (LCC). The paper proposes a new methodology based on the modified economic life cycle and lifespan methods by including the maintenance policy using maintenance Key Performance Indicators (KPI), namely Availability, based on the Mean Time Between Failures (MTBF) and the Mean Time To Repair (MTTR). The benefits (profits) that result from the asset’s Availability must be balanced with the initial investment and the variable maintenance investment along its life, which has relation with the maintenance policy and the ROI.

Predicting Gas Turbine Reliability to Assess Risks for Purchased Power Agreements

1996 ◽  
Author(s):  
Wolfram Krockow
Keyword(s):  
Gas Turbine ◽  
Heavy Duty ◽  
Bottom Line ◽  
Mean Time Between Failures ◽  
Time Between Failures ◽  
The Monte Carlo Method ◽  
Parameter Variations ◽  
The Mean ◽  
Heavy Duty Gas Turbine ◽  
Mean Time

Availability and Reliability have become a major concern for operating companies; especially for those where unscheduled outages due to purchased power agreements may be highly penalized. On the other hand, high revenues by the sale of electricity can be achieved if the power generating set performs according to its demand. Economic calculations demonstrate that the bottom line between profit and loss lies within the high 90th% of availability. This paper summarizes the results of an analysis based on the regulations in France with its so-called EJP days (Effacement Jour Pointe). Any loss of highly penalized EJP days is quantified based on known RAM (Reliability, Availability, Maintainability) values of the equipment, as these are the Mean Time Between Failures MTBF’s and the Mean Times To Repair MTTR’s of the overall equipment as well as its subassemblies. In formulating the demand, the past 12 years of assigned EJP days by the Electricité de France, EdF, was analyzed to derive probability ratings of seasonal distributions, weekly distributions and day block distributions. The mathematics of this simulation model are based on well proven statistical procedures (i.e. the Monte Carlo Method). By performing parameter variations, the model can also quantitatively predict how much the Mean Time Between Failures of a heavy duty gas turbine must usually be better for this application when compared to an aeroderivative gas turbine. This is because it normally takes longer to repair or replace a heavy duty gas turbine versus an aeroderivative unit in case of a major unscheduled or forced outage.

The Application of RAM Analysis in a CNC System

2012 ◽  
Vol 591-593 ◽  
pp. 1701-1709
Author(s):  
Tien Long Nguyen ◽  
Yi Qiang Wang ◽  
Yan Hui Zhao
Keyword(s):  
Steady State Solution ◽  
Numerical Control ◽  
System Behavior ◽  
Cnc System ◽  
Mean Time Between Failures ◽  
Time Between Failures ◽  
Equipment Availability ◽  
The Mean ◽  
Ram Analysis ◽  
Mean Time

The reliability, availability and maintainability (RAM) analysis of a computerized numerical control (CNC) system is helpful for performing design modifications that are required to achieve minimum failures or to increase the mean time between failures (MTBF), to plan maintainability requirements, to optimize reliability and to maximize equipment availability. To demonstrate these benefits, this paper presents the application of RAM analysis in a CNC system. The Weibull approach is used to model the CNC system behavior. After determining the steady state solution for the CNC system behavior, the corresponding values of reliability and maintainability are estimated at different mission times. The computed results proved to be helpful for analyzing the CNC system behavior, which enabled a considerable improvement of the system performance by implementing suitable maintenance strategies.

scholarly journals Mathematical Modeling and Examination of the Effects of Structural Redundancy in а Class of Computer-Based Fault Tolerant Systems

2016 ◽  
Vol 16 (2) ◽  
pp. 35-45
Author(s):  
Mariya Hristova
Keyword(s):  
Fault Tolerant ◽  
Structural Redundancy ◽  
Mean Time Between Failures ◽  
Reliability Indicators ◽  
Time Between Failures ◽  
Computer Based ◽  
The Mean ◽  
Modular Redundancy ◽  
The Impact ◽  
Mean Time

Abstract The present article models and examines k˅n systems, in particular Triple modular redundancy (2˅3) and 3˅5. The aim of the study is to derive mathematical models, which are used for determining the impact of structural redundancy (the number of channels n and the threshold of the quorum function k) on the reliability of the system. The probability of failure-free operation p and the Mean Time Between Failures (MTBF) are used as reliability indicators.

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