• Title/Summary/Keyword: Markov process model

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Parametric Sensitivity Analysis of Markov Process Based RAM Model (Markov Process 기반 RAM 모델에 대한 파라미터 민감도 분석)

  • Kim, Yeong Seok;Hur, Jang Wook
    • Journal of the Korean Society of Systems Engineering
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    • v.14 no.1
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    • pp.44-51
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    • 2018
  • The purpose of RAM analysis in weapon systems is to reduce life cycle costs, along with improving combat readiness by meeting RAM target value. We analyzed the sensitivity of the RAM analysis parameters to the use of the operating system by using the Markov Process based model (MPS, Markov Process Simulation) developed for RAM analysis. A Markov process-based RAM analysis model was developed to analyze the sensitivity of parameters (MTBF, MTTR and ALDT) to the utility of the 81mm mortar. The time required for the application to reach the steady state is about 15,000H, which is about 2 years, and the sensitivity of the parameter is highest for ALDT. In order to improve combat readiness, there is a need for continuous improvement in ALDT.

A Markov Chain Representation of Statistical Process Monitoring Procedure under an ARIMA(0,1,1) Model (ARIMA(0,1,1)모형에서 통계적 공정탐색절차의 MARKOV연쇄 표현)

  • 박창순
    • The Korean Journal of Applied Statistics
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    • v.16 no.1
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    • pp.71-85
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    • 2003
  • In the economic design of the process control procedure, where quality is measured at certain time intervals, its properties are difficult to derive due to the discreteness of the measurement intervals. In this paper a Markov chain representation of the process monitoring procedure is developed and used to derive its properties when the process follows an ARIMA(0,1,1) model, which is designed to describe the effect of the noise and the special cause in the process cycle. The properties of the Markov chain depend on the transition matrix, which is determined by the control procedure and the process distribution. The derived representation of the Markov chain can be adapted to most different types of control procedures and different kinds of process distributions by obtaining the corresponding transition matrix.

A Study on the Fatigue Reliability of Structures by Markov Chain Model (Markov Chain Model을 이용한 구조물의 피로 신뢰성 해석에 관한 연구)

  • Y.S. Yang;J.H. Yoon
    • Journal of the Society of Naval Architects of Korea
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    • v.28 no.2
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    • pp.228-240
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    • 1991
  • Many experimental data of fatigue crack propagation show that the fatigue crack propagation process is stochastic. Therefore, the study on the crack propagation must be based on the probabilistic approach. In the present paper, fatigue crack propagation process is assumed to be a discrete Markov process and the method is developed, which can evaluate the reliability of the structural component by using Markov chain model(Unit step B-model) suggested by Bogdanoff. In this method, leak failure, plastic collapse and brittle fracture of the critical component are taken as failure modes, and the effects of initial crack distribution, periodic and non-periodic inspection on the probability of failure are considered. In this method, an equivalent load value for random loading such as wave load is used to facilitate the analysis. Finally some calculations are carried out in order to show the usefulness and the applicability of this method. And then some remarks on this method are mentioned.

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Application Markov State Model for the RCM of Combustion Turbine Generating Unit (Markov State Model을 이용한 복합화력 발전설비의 최적의 유지보수계획 수립)

  • Lee, Seung-Hyuk;Shin, Jun-Seok;Kim, Jin-O
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.56 no.2
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    • pp.248-253
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    • 2007
  • Traditional time based preventive maintenance is used to constant maintenance interval for equipment life. In order to consider economic aspect for time based preventive maintenance, preventive maintenance is scheduled by RCM(Reliability-Centered Maintenance) evaluation. So, Markov state model is utilized considering stochastic state in RCM. In this paper, a Markov state model which can be used for scheduling and optimization of maintenance is presented. The deterioration process of system condition is modeled by a Markov model. In case study, simulation results about RCM are used to the real historical data of combustion turbine generating units in Korean power systems.

Application of Hidden Markov Model Using AR Coefficients to Machine Diagnosis (AR계수를 이용한 Hidden Markov Model의 기계상태진단 적용)

  • 이종민;황요하;김승종;송창섭
    • Transactions of the Korean Society for Noise and Vibration Engineering
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    • v.13 no.1
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    • pp.48-55
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    • 2003
  • Hidden Markov Model(HMM) has a doubly embedded stochastic process with an underlying stochastic process that can be observed through another set of stochastic processes. This structure of HMM is useful for modeling vector sequence that doesn't look like a stochastic process but has a hidden stochastic process. So, HMM approach has become popular in various areas in last decade. The increasing popularity of HMM is based on two facts : rich mathematical structure and proven accuracy on critical application. In this paper, we applied continuous HMM (CHMM) approach with AR coefficient to detect and predict the chatter of lathe bite and to diagnose the wear of oil Journal bearing using rotor shaft displacement. Our examples show that CHMM approach is very efficient method for machine health monitoring and prediction.

ATM Traffic Modeling with Markov Renewal Process and Performance Analysis (마코프 재생과정을 이용한 ATM 트랙픽 모델링 및 성능분석)

  • Jeong, Seok-Yun;Hur, Sun
    • Journal of the Korean Operations Research and Management Science Society
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    • v.24 no.3
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    • pp.83-91
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    • 1999
  • In order to build and manage an ATM network effectively under several types of control methods, it is necessary to estimate the performance of the equipments in various viewpoints, especially of ATM multiplexer. As for the method to model the input stream into the ATM multiplexer, many researches have been done to characterize it by, such as, fluid flow, MMPP(Markov Modulated Poisson Process), or MMDP (Markov Modulated Deterministic Process). We introduce an MRP(Markov Renewal Process) to model the input stream which has proper structure to represent the burst traffic with high correlation. In this paper, we build a model for aggregated heterogeneous ON-OFF sources of ATM traffic by MRP. We make discrete time MR/D/1/B queueing system, whose input process is the superposed MRP and present a performance analysis by finding CLP(Cell Loss Probability). A simulation is done to validate our algorithm.

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Balanced mobility pattern generation using Random Mean Degree modification in Gauss Markov model for Mobile network (이동 네트워크를 위한 가우스 마코프 모델에서 평균 이동각도 조절을 통한 균형잡힌 이동 패턴 생성)

  • 노재환;이병직;류정필;하남구;한기준
    • Proceedings of the Korean Information Science Society Conference
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    • 2004.04a
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    • pp.502-504
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    • 2004
  • 이동성이 중요시되는 네트워크에서 특정 프로토콜의 성능 평가를 위해서는 노드의 이동패턴을 정확하게 표현할 수 있는 Mobility Model이 필요하다. 노드의 연속적인 이동패턴을 필요로 하는 Mobile Ad-hoc 네트워크를 위해선 Markov process 기반의 Gauss-Markov Mobility Model이 적절하다. 그러나 맵의 엣지 부근에서 노드 이동의 부적절한 처리로 인해, 기존의 Gauss-Markov Model은 편중된 이동 패턴을 야기한다. 본 논문은 엣지 부근의 평균 이동각도를 랜덤하게 조정함으로써 기존의 모델이 가진 문제를 해결하고, 시뮬레이션을 통해서 이를 검증한다.

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Reliability Analysis of Multi-Component System Considering Preventive Maintenance: Application of Markov Chain Model (예방정비를 고려한 복수 부품 시스템의 신뢰성 분석: 마코프 체인 모형의 응용)

  • Kim, Hun Gil;Kim, Woo-Sung
    • Journal of Applied Reliability
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    • v.16 no.4
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    • pp.313-322
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    • 2016
  • Purpose: We introduce ways to employ Markov chain model to evaluate the effect of preventive maintenance process. While the preventive maintenance process decreases the failure rate of each subsystems, it increases the downtime of the system because the system can not work during the maintenance process. The goal of this paper is to introduce ways to analyze this trade-off. Methods: Markov chain models are employed. We derive the availability of the system consisting of N repairable subsystems by the methods under various maintenance policies. Results: To validate our methods, we apply our models to the real maintenance data reports of military truck. The error between the model and the data was about 1%. Conclusion: The models developed in this paper fit real data well. These techniques can be applied to calculate the availability under various preventive maintenance policies.