The Experts below are selected from a list of 5475 Experts worldwide ranked by ideXlab platform

Fan C. Meng - One of the best experts on this subject based on the ideXlab platform.

  • Comparing the MTBF of four systems with standby components
    Microelectronics Reliability, 1995
    Co-Authors: Fan C. Meng
    Abstract:

    Abstract The mean time between failures (MTBF) of four systems arising in standby redundancy enhancement are compared. A general ordering relationship between their MTBF is obtained. In deriving this result the usual assumption of exponential life distribution of components is removed, and the components can assume arbitrary life distributions.

  • On comparison of MTBF between four redundant systems
    Microelectronics Reliability, 1993
    Co-Authors: Fan C. Meng
    Abstract:

    Abstract In this study we compare the mean time between failures (MTBF) of four series-parallel and parallel-series redundant systems composed of 2 n independent components. General ordering relations between the four systems in terms of their MTBF are obtained. These results substantially improve previous ones obtained by Yamashiro etc.

Jinping Chen - One of the best experts on this subject based on the ideXlab platform.

  • Analysis of MTBF/MTTR for Logistics Service System
    ICTE 2013, 2013
    Co-Authors: Jinping Chen
    Abstract:

    Mean Time Between Failures/Mean Time to Repair (MTBF/MTTR) are important parameters for measuring the reliability of systems. Shorter MTBF/MTTR normally means high service quality. However, a reasonable level of reliability is usually not defined. In this paper, the calculation method of MTBF/MTTR is presented. This paper also provided the simulation model and simulation experiment oriented service systems for the quantitative analysis of the relation between MTBF/MTTR and performance metrics of service systems. In the paper, the technique is developed for defining the rational level of reliability, and a practical case is used to demonstrate the aforementioned approaches.

  • analysis of MTBF mttr for logistics service system
    Fourth International Conference on Transportation EngineeringAmerican Society of Civil EngineersSouthwest Jiaotong UniversityChina Communications and , 2013
    Co-Authors: Jinping Chen
    Abstract:

    Mean Time Between Failures/Mean Time to Repair (MTBF/MTTR) are important parameters for measuring the reliability of systems. Shorter MTBF/MTTR normally means high service quality. However, a reasonable level of reliability is usually not defined. In this paper, the calculation method of MTBF/MTTR is presented. This paper also provided the simulation model and simulation experiment oriented service systems for the quantitative analysis of the relation between MTBF/MTTR and performance metrics of service systems. In the paper, the technique is developed for defining the rational level of reliability, and a practical case is used to demonstrate the aforementioned approaches.

Milena Krasich - One of the best experts on this subject based on the ideXlab platform.

John Crocker - One of the best experts on this subject based on the ideXlab platform.

  • Maintenance free operating period – an alternative measure to MTBF and failure rate for specifying reliability?
    Reliability Engineering & System Safety, 1999
    Co-Authors: U. Dinesh Kumar, Jezdimir Knezevic, John Crocker
    Abstract:

    Abstract The paper analyses the concept of maintenance free operating period (MFOP), the reliability requirement driven by the Ministry of Defence (UK) for the next generation of future aircraft to be included in the fleet. Since the traditional reliability requirement MTBF (mean operating time between failure) has several drawbacks, the immediate reaction would be to analyse the credibility of the new measure MFOP against MTBF. The paper discusses various issues associated with MFOP. Two mathematical models are developed to predict the maintenance free operating period survivability (MFOPS), one using mission reliability approach and the other using alternating renewal theory. The paper also analyses cost implications of MFOP to the customer and to the producer.

  • maintenance free operating period an alternative measure to MTBF and failure rate for specifying reliability
    Reliability Engineering & System Safety, 1999
    Co-Authors: Dinesh U Kumar, Jezdimir Knezevic, John Crocker
    Abstract:

    Abstract The paper analyses the concept of maintenance free operating period (MFOP), the reliability requirement driven by the Ministry of Defence (UK) for the next generation of future aircraft to be included in the fleet. Since the traditional reliability requirement MTBF (mean operating time between failure) has several drawbacks, the immediate reaction would be to analyse the credibility of the new measure MFOP against MTBF. The paper discusses various issues associated with MFOP. Two mathematical models are developed to predict the maintenance free operating period survivability (MFOPS), one using mission reliability approach and the other using alternating renewal theory. The paper also analyses cost implications of MFOP to the customer and to the producer.

Hongzhou Li - One of the best experts on this subject based on the ideXlab platform.

  • Comparison between Bayesian Method and LSE in Estimating MTBF of NC Machine Tools
    2015 International Conference on Computer Science and Mechanical Automation (CSMA), 2015
    Co-Authors: Lihui Wang, Binbin Xu, Zhaojun Yang, Hongzhou Li
    Abstract:

    Aiming at the large bias of LSE (Least Squares Estimation) in estimating MTBF (mean time between failures) under a small sample of data, a Bayesian MTBF estimating method is proposed for NC (numerical control) machine tools. To solve difficulty in directly presenting the prior distributions of Weibull parameters, an expert-judgment method which incorporates prior information is developed to indirectly obtain Weibull parameters' prior distributions. Aiming at the problem that analytic solutions to Weibull parameters' posterior distributions and estimators are impossible to obtain, a Metropolis algorithm is developed. The iteration procedure of the algorithm is presented, the posterior distribution of each parameter is simulated, and the parameter estimators and MTBF are obtained. Given the actual MTBF as standard value, the proposed method and LSE are applied to the same real case respectively. The results indicate that when sample size n≤10, relative errors of the proposed method lie between 4.43% and 7.19%, which are smaller than those of LSE. The proposed Bayesian MTBF estimating method is better than LSE and suitable for NC machine tools under small samples.

  • Comparison between Bayesian Method and LSE in Estimating MTBF of NC Machine Tools
    2015 International Conference on Computer Science and Mechanical Automation (CSMA), 2015
    Co-Authors: Lihui Wang, Binbin Xu, Zhaojun Yang, Hongzhou Li
    Abstract:

    Aiming at the large bias of LSE (Least Squares Estimation) in estimating MTBF (mean time between failures) under a small sample of data, a Bayesian MTBF estimating method is proposed for NC (numerical control) machine tools. To solve difficulty in directly presenting the prior distributions of Weibull parameters, an expert-judgment method which incorporates prior information is developed to indirectly obtain Weibull parameters' prior distributions. Aiming at the problem that analytic solutions to Weibull parameters' posterior distributions and estimators are impossible to obtain, a Metropolis algorithm is developed. The iteration procedure of the algorithm is presented, the posterior distribution of each parameter is simulated, and the parameter estimators and MTBF are obtained. Given the actual MTBF as standard value, the proposed method and LSE are applied to the same real case respectively. The results indicate that when sample size n≤10, relative errors of the proposed method lie between 4.43% and 7.19%, which are smaller than those of LSE. The proposed Bayesian MTBF estimating method is better than LSE and suitable for NC machine tools under small samples.