• Journal of the Korean Data and Information Science Society
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• 제13권1호
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• pp.55-65
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• 2002

In this paper we present a Bayesian approach for determining an optimal maintenance policy following the expiration of warranty for a repairable system. We consider two types of warranty policies : non-renewing free replacement warranty (NFRW) and non-renewing pro-rata warranty (NPRW). The mathematical formula of the expected cost rate per unit time is obtained for NFRW and NPRW, respectively. When the failure time is Weibull distribution with uncertain parameters, a Bayesian approach is established to formally express and update the uncertain parameters for determining an optimal maintenance policy. We illustrate the use of our approach with simulated data.

• Smart Structures and Systems
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• 제12권6호
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• pp.641-659
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• 2013

In this paper is studied the influence of the uncertain mass distribution over the floors on the choice of the optimal parameters of a hybrid control system for tall buildings subjected to wind load. In particular, an optimization procedure is developed for the robust design of a hybrid control system that is based on an enhanced Monte Carlo simulation technique and the genetic algorithm. The large computational effort inherent in the use of a MC-based procedure is reduced by the employment of the Latin Hypercube Sampling. With reference to a tall building modeled as a multi degrees of freedom system, several numerical analyses are carried out varying the parameters influencing the floors' masses, like the coefficient of variation of the distribution and the correlation between the floors' masses. The procedure allows to obtain optimal designs of the control system that are robust with respect to the uncertainties on the distribution of the dead and live loads.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.110-110
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• 2001

By the state transformation including independent outputs functions, a nonlinear process model can be decomposed into two subsystems; the one(design model) is described in output variables as new states and used for control system synthesis and the other(disturbance model) is described in the original unavailable states and its couplings with the design model are treated as uncertain time-varying parameters in the design model. Its existence with respect to the design model is ignored. So, the design model is and uncertain time-variant system. Control synthesis based on a reduced design model is a combined form of a time-variant input-output linearization with parameter estimation. The parameter estimation is also based on the design model and it gives the parameter estimates such that the estimated outputs follow the actual outputs in a specified way. The disturbances form disturbance model and as well all the other uncertainties affecting the outputs will be reflected into the estimated parameters used in the linearizing control law.

• 소음진동
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• 제6권6호
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• pp.791-800
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• 1996

Linear LQG controller has been investigated to control flexible link manipulators. The performance and complexity of these depend largely on the model upon which the controller is designed. In this study, the flexible modes of the link manipulator are considered to have uncertain parameters, which can be represented by random variable and these parameters are reflected on the weighting of performance. In this method, the exact modelling for the flexible modes is not necessary. The order of the resulting controller is much lower than the one based on a full model. Through numerical study, it is shown that the performance and the stability-robustness of the proposed controller reaches reasonably the one based on the full model.

• 대한전기학회논문지:전력기술부문A
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• 제48권2호
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• pp.136-144
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• 1999

This paper deals with the fault detection problem in uncertain linear multivariable systems and its application. A robust fault detection method presented by Kim et a. (1998) for MIMO (Multi Input/Multi Output) systems has been adopted and applied to the twin rotor MIMO experimental setup using industrial DSP. The system identification problem is formulated for the twin rotor MIMO system and its parameters are estimated using experimental data. Based on the estimated parameters, some fault detection simulations are performed using the robust fault detection method, which shows that the preformance is satisfied.

• Communications for Statistical Applications and Methods
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• 제9권1호
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• pp.21-31
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• 2002

This paper considers a Bayesian approach to determine an optimal replacement policy for a repairable system with warranty period. The mathematical formula of the expected cost rate per unit time is obtained for two cases : RFRW(renewing free-replacement warranty) and RPRW(renewing pro-rata warranty). When the failure time is Weibull distribution with uncertain parameters, a Bayesian approach is established to formally express and update the uncertain parameters for determining an optimal replacement policy. Some numerical examples are presented for illustrative purpose.

• Journal of the Korean Data and Information Science Society
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• 제17권3호
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• pp.743-752
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• 2006

This paper considers a Bayesian approach to replacement policy model with minimal repair. We use the criterion based on the expected cost and the expected downtime to determine the optimal replacement period. To do so, we obtain the expected cost rate per unit time and the expected downtime per unit time, respectively. When the failure time is Weibull distribution with uncertain parameters, a Bayesian approach is established to formally express and update the uncertain parameters for determining an optimal maintenance policy. Especially, the overall value function suggested by Jiagn and Ji(2002) is applied to obtain the optimal replacement period. The numerical examples are presented for illustrative purpose.

• Geomechanics and Engineering
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• 제14권6호
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• pp.589-600
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• 2018

For foundations in permafrost regions, the displacement characteristics are uncertain because of the randomness of temperature characteristics and mechanical parameters, which make the structural system have an unexpected deviation and unpredictability. It will affect the safety of design and construction. In this paper, we consider the randomness of temperature characteristics and mechanical parameters. A stochastic analysis model for the uncertain displacement characteristic of foundations is presented, and the stochastic coupling program is compiled by Matrix Laboratory (MATLAB) software. The stochastic displacement fields of an embankment in a permafrost region are obtained and analyzed by Neumann stochastic finite element method (NSFEM). The results provide a new way to predict the deformation characteristics of foundations in permafrost regions, and it shows that the stochastic temperature has a different influence on the stochastic lateral displacement and vertical displacement. Construction disturbance and climate warming lead to three different stages for the mean settlement of characteristic points. For the stochastic settlement characteristic, the standard deviation increases with time, which imply that the results of conventional deterministic analysis may be far from the true value. These results can improve our understanding of the stochastic deformation fields of embankments and provide a theoretical basis for engineering reliability analysis and design in permafrost regions.

• Geomechanics and Engineering
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• 제20권1호
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• pp.19-28
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• 2020

The spatial variability of geotechnical properties can lead to the uncertainty of settlement for frozen soil foundation around the oil pipeline, and it can affect the stability of permafrost foundation. In this paper, the elastic modulus, cohesion, angle of internal friction and poisson ratio are taken as four independent random fields. A stochastic analysis model for the uncertain settlement characteristic of frozen soil foundation around an oil pipeline is presented. The accuracy of the stochastic analysis model is verified by measured data. Considering the different combinations for the coefficient of variation and scale of fluctuation, the influences of spatial variability of geotechnical properties on uncertain settlement are estimated. The results show that the stochastic effects between elastic modulus, cohesion, angle of internal friction and poisson ratio are obviously different. The deformation parameters have a greater influence on stochastic settlement than the strength parameters. The overall variability of settlement reduces with the increase of horizontal scale of fluctuation and vertical scale of fluctuation. These results can improve our understanding of the influences of spatial variability of geotechnical properties on uncertain settlement and provide a theoretical basis for the reliability analysis of pipeline engineering in permafrost regions.

• Journal of Mechanical Science and Technology
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• 제18권11호
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• pp.1916-1922
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• 2004

A robust position control with the bound function of neural network structure is proposed for uncertain robot manipulators. The uncertain factors come from imperfect knowledge of system parameters, payload change, friction, external disturbance, and etc. Therefore, uncertainties are often nonlinear and time-varying. The neural network structure presents the bound function and does not need the concave property of the bound function. The robust approach is to solve this problem as uncertainties are included in a model and the controller can achieve the desired properties in spite of the imperfect modeling. Simulation is performed to validate this law for four-axis SCARA type robot manipulator.