A Comprehensive Procedure to Estimate the Probability of Extreme Vibration Levels Due to Mistuning

2010 ◽  
Vol 132 (11) ◽  
Author(s):  
Y.-J. Chan ◽  
D. J. Ewins
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Distribution Function ◽  
Importance Sampling ◽  
Gradient Method ◽  
Sampling Method ◽  
Optimization Analysis ◽  
Amplification Factors ◽  
Bladed Disk ◽  
Importance Sampling Method

A new procedure is developed to find the probabilities of extremely high amplification factors in mistuned bladed disk vibration levels, typical of events which occur rarely. While a rough estimate can be made by curve-fitting the distribution function generated in a Monte Carlo simulation, the procedure presented here can determine a much more accurate upper bound and the probabilities of amplification factors near to that bound. The procedure comprises an optimization analysis based on the conjugate gradient method and a stochastic simulation using the importance sampling method. Two examples are provided to illustrate the efficiency of the procedure, which can be 2 or 3 orders of magnitude more efficient than Monte Carlo simulations.

Reliability Assessment of Power System Using Importance Sampling Technique Based on Layer Optimization Simulation

2011 ◽  
Vol 88-89 ◽  
pp. 554-558 ◽  
Author(s):  
Bin Wang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Power System ◽  
Importance Sampling ◽  
System Reliability ◽  
Sampling Method ◽  
Sampling Technique ◽  
Test System ◽  
Importance Sampling Method ◽  
Importance Sampling Technique

An improved importance sampling method with layer simulation optimization is presented in this paper. Through the solution sequence of the components’ optimum biased factors according to their importance degree to system reliability, the presented technique can further accelerate the convergence speed of the Monte-Carlo simulation. The idea is that the multivariate distribution’ optimization of components in power system is transferred to many steps’ optimization based on importance sampling method with different optimum biased factors. The practice is that the components are layered according to their importance degree to the system reliability before the Monte-Carlo simulation, the more forward, the more important, and the optimum biased factors of components in the latest layer is searched while the importance sampling is carried out until the demanded accuracy is reached. The validity of the presented is verified using the IEEE-RTS79 test system.

An Importance Sampling Approach for Time-Dependent Reliability

2011 ◽  
Author(s):  
Amandeep Singh ◽  
Zissimos P. Mourelatos ◽  
Efstratios Nikolaidis
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Random Process ◽  
Importance Sampling ◽  
Sampling Technique ◽  
Process Time ◽  
Cumulative Probability ◽  
Repairable Systems ◽  
Importance Sampling Method ◽  
True Value

Reliability is an important engineering requirement for consistently delivering acceptable product performance through time. The reliability usually degrades with time increasing the lifecycle cost due to potential warranty costs, repairs and loss of market share. Reliability is the probability that the system will perform its intended function successfully for a specified time. In this article, we consider the first-passage reliability which accounts for the first time failure of non-repairable systems. Methods are available which provide an upper bound to the true reliability which may overestimate the true value considerably. The traditional Monte-Carlo simulation is accurate but computationally expensive. A computationally efficient importance sampling technique is presented to calculate the cumulative probability of failure for random dynamic systems excited by a stationary input random process. Time series modeling is used to characterize the input random process. A detailed example demonstrates the accuracy and efficiency of the proposed importance sampling method over the traditional Monte Carlo simulation.

A Comprehensive Set of Procedures to Estimate the Probability of Extreme Vibration Levels Due to Mistuning

2009 ◽  
Author(s):  
Y.-J. Chan ◽  
D. J. Ewins
Keyword(s):  
Monte Carlo ◽  
Distribution Function ◽  
Monte Carlo Simulations ◽  
Curve Fitting ◽  
Upper Bound ◽  
Amplification Factors ◽  
Bladed Disk ◽  
New Procedures

A set of new procedures is developed to find the probabilities of extremely high amplification factors in mistuned bladed disk vibration levels, which rarely occur. While a rough estimate can be made by curve-fitting the distribution function, the method presented here can determine an accurate upper bound and the probabilities of amplification factors near to that bound. Two examples are provided to illustrate the efficiency of the method, which can be 2 or 3 orders of magnitudes more efficient than Monte Carlo simulations.

The Optimization Analysis of Equipment Maintenance Based on Monte-Carlo Simulation

2011 ◽  
Vol 189-193 ◽  
pp. 424-427 ◽  
Author(s):  
Dai Geng ◽  
Shi Min Zhang ◽  
De Guo Wang ◽  
Jian Gao ◽  
Li Sha Dai
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Distribution Function ◽  
Theoretical Basis ◽  
Economic Benefits ◽  
Maintenance Cost ◽  
Equipment Maintenance ◽  
Maintenance Period ◽  
Optimization Analysis ◽  
Maintenance Decisions

In order to improve the reliability and security in production, effectively checking and maintaining equipment must be put into practice. In this paper, the on-condition maintenance period of the equipment is optimized by Monte-Carlo for the lowest maintenance cost in unit time by expressing the maintenance interval as an exponential function parameterizing Weibull’s distribution function。Finally, the oil centrifugal pump as an example was demonstrated. The results show that our model has the obvious economic benefits. The optimization analysis of equipment maintenance based on Monte-Carlo provides a theoretical basis for optimized detection and maintenance decisions.

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