Data on time between complete power outages, Time between Failure (TBF) in Uyo were considered. Trend test and serial correlation test were conducted graphically for the data. The tests proved that the data were ident...Data on time between complete power outages, Time between Failure (TBF) in Uyo were considered. Trend test and serial correlation test were conducted graphically for the data. The tests proved that the data were identically and independently distributed (iid). Summary statistics of the data showed that complete power outage occurred 416 times between the year 2014 and 2018. The maximum likelihood estimation method was used to estimate the parameters of Weibull 2-parameter, Normal, Lognormal 2-parameter and exponential distributions. The values of Kolmogorov-Smirnov, Anderson Darling and Chi-Square statistics were used to determine the best fit distributions. A model</span></span><span><span><span style="font-family:""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">for the computation of reliability of electric power was then proposed</span></span></span><span style="font-family:Verdana;">.展开更多
This study explores the arrivals of water pipeline break failures. The aim is to assist the facility manager in the decision making process. Based on characteristics of the data set ranging from 2011 to 2014, two step...This study explores the arrivals of water pipeline break failures. The aim is to assist the facility manager in the decision making process. Based on characteristics of the data set ranging from 2011 to 2014, two steps of analysis were presented in the paper. This first step is the analysis of partially complete data set (2011 data). The 2-sample KS test is adopted to check the similarity between this data set and the entire data set with no underlying distribution implied. In order to conduct the reliability analysis, the Weibull distribution is adopted to evaluate the data. For annual data set, the 2-parameter Weibull distribution fits data sets pretty well. The shape parameters are a little greater than 1, indicating a slightly increasing arrival rate of such failures. For the entire data set, the 3-parameter Weibull tends to fit the data better than the 2-parameter Weibull. The shape parameter is well above 1, indicating an increasing arrival rate of the failures. To eliminate the impact of missing points for the 2011 data set, data from 2012 to 2014 were also considered as a new set, the Weibull distribution generated a decent fitting. The shape parameter is a little greater than 1. Therefore, there is a slightly increasing arrival rate of those pipeline failures. Results from this study provide decision makers valuable information in terms of whether additional efforts shall be made to enhance the system’s performance in order to reduce the failure rate.展开更多
文摘Data on time between complete power outages, Time between Failure (TBF) in Uyo were considered. Trend test and serial correlation test were conducted graphically for the data. The tests proved that the data were identically and independently distributed (iid). Summary statistics of the data showed that complete power outage occurred 416 times between the year 2014 and 2018. The maximum likelihood estimation method was used to estimate the parameters of Weibull 2-parameter, Normal, Lognormal 2-parameter and exponential distributions. The values of Kolmogorov-Smirnov, Anderson Darling and Chi-Square statistics were used to determine the best fit distributions. A model</span></span><span><span><span style="font-family:""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">for the computation of reliability of electric power was then proposed</span></span></span><span style="font-family:Verdana;">.
文摘This study explores the arrivals of water pipeline break failures. The aim is to assist the facility manager in the decision making process. Based on characteristics of the data set ranging from 2011 to 2014, two steps of analysis were presented in the paper. This first step is the analysis of partially complete data set (2011 data). The 2-sample KS test is adopted to check the similarity between this data set and the entire data set with no underlying distribution implied. In order to conduct the reliability analysis, the Weibull distribution is adopted to evaluate the data. For annual data set, the 2-parameter Weibull distribution fits data sets pretty well. The shape parameters are a little greater than 1, indicating a slightly increasing arrival rate of such failures. For the entire data set, the 3-parameter Weibull tends to fit the data better than the 2-parameter Weibull. The shape parameter is well above 1, indicating an increasing arrival rate of the failures. To eliminate the impact of missing points for the 2011 data set, data from 2012 to 2014 were also considered as a new set, the Weibull distribution generated a decent fitting. The shape parameter is a little greater than 1. Therefore, there is a slightly increasing arrival rate of those pipeline failures. Results from this study provide decision makers valuable information in terms of whether additional efforts shall be made to enhance the system’s performance in order to reduce the failure rate.
