Predicting Failures for Repairable System Subjected to Imperfect Maintenance - Nurul Akma Binti Brahim, Nurul Akma (Universiti Teknologi Petronas, 2009)
The purpose of this project is to develop a reliability model which results from reliability analysis conducted on repairable system subjected to imperfect maintenance. Hence, in order to perform the reliability analysis, field data from actual equipment failure were gathered and analyzed. In this project, the equipment selected was the centrifugal pump used in one of the petrochemical plants. Various stages had been conducted in order to achieve the objectives of the project. This includes data screening and analysis, determination of failure distribution as well as the maintenance effectiveness which denoted by q. All of these phases were performed by using the reliability software, Weibull ++7. The data analysis showed that the failure data displayed Weibull distribution while q value indicated the Generalized Renewal Process (GRP) is the most applicable probabilistic models that characterized the failure data. Thus, the reliability model was developed by using GRP model of Type I and Type II. The comparison between both models was conducted to select the suitable model to be used in developing the reliability model. Based on the likelihood value (LV), GRP model Type I was selected as it possessed higher LV and this model was used to predict the future failures of the system. Evaluation phase was conducted to verify that GRP model Type I was the most suitable model which fits best the failure data. In this phase, the reliability model was developed by using other probabilistic models such as Renewal Process (RP) and Non-Homogeneous Poisson Process (NHPP). The LV were compared which resulted in GRP model Type I produced the highest LV. Finally, the model was validated by using reliability models developed based on the different duration of operation days which were 1500 and 2000 operation days, respectively. The expected cumulative numbers of failures calculated by both models were then compared with the actual cumulative number of failures obtained from the model developed using 3000 operation days. Based on the comparison, both models produced similar values with the actual failure data. Hence, the developed reliability model could be used to predict the next failure of the system. It is hoped that this project and report could be used as a reference for further research and study.