The Experts below are selected from a list of 1896 Experts worldwide ranked by ideXlab platform

Mohd Zain - One of the best experts on this subject based on the ideXlab platform.

  • Analysis Of Preventive Maintenance For Processing Plant (Gassification Process Unit)
    2015
    Co-Authors: Abdul Paizal, Mohd Zain
    Abstract:

    Preventive maintenance defined as a philosophy, a skill of management series in how to manage the plant operation to be more efficient in terms of cost and times. The purposes of this research is to find the value of preventive maintenance performance that consists of a several activities, according to standard operation of a particular components. The method that will be use is Weibull method, which by this method, the Shape Parameter and characteristic life can be evaluate to find the failure and reliability distribution in a period of specific time and economic analysis. The scope of this research are only for terms in preventive maintenance; types of preventive activities, number of labors, inventory / spare parts, failures and any related terms for preventive maintenance. The terms of cost in labors and inventory for economic analysis will have two Parameters as to be comparison, the first one is maintenance cost without optimization and the other one is maintenance cost with maintenance optimization. The result of these two Parameters are depends on the characteristic life factors ( ɳ ) and mean time between failure ( MTBF ). The result of this research will shows all the evaluation for Beta Shape Parameter, s and eta characteristic life, ɳ for each of the components in the particular plant. Then both of the Parameters will determine the factors needed in terms to find the optimized time line for preventive maintenance and leads to economic analysis after the optimization. Lastly, the conclusion will discuss the overall results and the relationship between all of the comparison, the Shape factors, characteristic life and optimized maintenance cost.

Abdul Paizal - One of the best experts on this subject based on the ideXlab platform.

  • Analysis Of Preventive Maintenance For Processing Plant (Gassification Process Unit)
    2015
    Co-Authors: Abdul Paizal, Mohd Zain
    Abstract:

    Preventive maintenance defined as a philosophy, a skill of management series in how to manage the plant operation to be more efficient in terms of cost and times. The purposes of this research is to find the value of preventive maintenance performance that consists of a several activities, according to standard operation of a particular components. The method that will be use is Weibull method, which by this method, the Shape Parameter and characteristic life can be evaluate to find the failure and reliability distribution in a period of specific time and economic analysis. The scope of this research are only for terms in preventive maintenance; types of preventive activities, number of labors, inventory / spare parts, failures and any related terms for preventive maintenance. The terms of cost in labors and inventory for economic analysis will have two Parameters as to be comparison, the first one is maintenance cost without optimization and the other one is maintenance cost with maintenance optimization. The result of these two Parameters are depends on the characteristic life factors ( ɳ ) and mean time between failure ( MTBF ). The result of this research will shows all the evaluation for Beta Shape Parameter, s and eta characteristic life, ɳ for each of the components in the particular plant. Then both of the Parameters will determine the factors needed in terms to find the optimized time line for preventive maintenance and leads to economic analysis after the optimization. Lastly, the conclusion will discuss the overall results and the relationship between all of the comparison, the Shape factors, characteristic life and optimized maintenance cost.

Derya Dispinar - One of the best experts on this subject based on the ideXlab platform.

  • Effect of Ranking Selection on the Weibull Modulus Estimation
    gazi university journal of science, 2012
    Co-Authors: S. Kirtay, Derya Dispinar
    Abstract:

    Determination of mechanical properties of materials requires repeatable and consistent results. Therefore, the Weibull statistical distribution is widely used to verify the confidence levels of the test results where probability of failure is predicted. The reliability of results is characterised by Weibull distribution where Weibull Parameters are determined and compared with the benchmarks. Recent studies showed that the size of the population and the method chosen to estimate the Weibull modulus play an important role. Therefore, in this study, the effect of ranking selection over the Weibull Parameters ( alpha- characteristic life, Beta- Shape Parameter, R 2 and survival probability) was investigated. The data from authors’ recent research were re-evaluated. In addition, randomly generated data with population from 5 to 50 sample sizes were studied. The results showed that the mean rank regression to estimate the failure probability had the highest R 2 and the lowest Shape Parameter. It was also found that the results were independent of sample size. Keywords: Estimators, mechanical test, prediction limit, Weibull distribution, Weibull modulus

M. Zanin - One of the best experts on this subject based on the ideXlab platform.

  • Influence of additives on the dielectric strength of high-density polyethylene
    IEEE Transactions on Dielectrics and Electrical Insulation, 1999
    Co-Authors: M.m. Ueki, M. Zanin
    Abstract:

    In this study, we present the results of the influence of chemical additives (antioxidant and UV stabilizer) and pigments (titanium dioxide and carbon black) on the short-term dielectric breakdown test of high-density polyethylene (HDPE). These additives and pigments are commonly added to polyolefins, which are used as insulating material for medium voltage cables. The incorporation was performed in a single screw extruder and thin films specimens were obtained by hot compression from extruded materials. For the dielectric breakdown test, an automated system has been used. A voltage ramp of 500 V/s was applied to specimens immersed in a silicon oil bath at room temperature. The degree of crystallinity and chemical modification of the formulations were evaluated by X-ray diffraction and Fourier transform infrared (FTIR), respectively. The dielectric breakdown results have been analyzed by a Weibull distribution. The Shape and scale Parameters of this distribution have been obtained by a graphic and maximum likelihood method. These results showed that the carbon black is the component that affects the dielectric strength, that the /spl Beta/ Shape Parameter from the graphic method can be used to evaluate additive mixing conditions, and that the weakest point for formation of the rupture channel is on the carbon black agglomerate.

S. Kirtay - One of the best experts on this subject based on the ideXlab platform.

  • Effect of Ranking Selection on the Weibull Modulus Estimation
    gazi university journal of science, 2012
    Co-Authors: S. Kirtay, Derya Dispinar
    Abstract:

    Determination of mechanical properties of materials requires repeatable and consistent results. Therefore, the Weibull statistical distribution is widely used to verify the confidence levels of the test results where probability of failure is predicted. The reliability of results is characterised by Weibull distribution where Weibull Parameters are determined and compared with the benchmarks. Recent studies showed that the size of the population and the method chosen to estimate the Weibull modulus play an important role. Therefore, in this study, the effect of ranking selection over the Weibull Parameters ( alpha- characteristic life, Beta- Shape Parameter, R 2 and survival probability) was investigated. The data from authors’ recent research were re-evaluated. In addition, randomly generated data with population from 5 to 50 sample sizes were studied. The results showed that the mean rank regression to estimate the failure probability had the highest R 2 and the lowest Shape Parameter. It was also found that the results were independent of sample size. Keywords: Estimators, mechanical test, prediction limit, Weibull distribution, Weibull modulus