Reliability Analysis of Primary and Purification Pumps in RSG-Gas Using Monte Carlo Simulation Approach

Abstract

Reliability and maintenance play an important role in ensuring successful operation of a system. Reliability analysis is often used to determine the probability whether or not a system is functioning. However, limited available data and information are causing uncertainties and inaccuracies on component parameters. The purpose of this study is to conduct component/system reliability analysis using Monte Carlo simulation-based method. This method enables us to estimate the reliability of components/systems including parameter uncertainty and imprecision. It is also useful to predict and evaluate maintenance decisions related to reliability. Monte Carlo method employs random number generation based on the probability of the distribution of processed data, of which then validated with real available data to ensure the simulation condition is relatively similar to real-life condition. The data used in this research is failure data on RSG-GAS components/systems for core configuration number of 81 to 95, accumulated from year 2013 to 2018. The results show that reliability values of components JE01/AP01-02 on TTF 233.619 is 0.579 while for components KBE01/AP-01-02 in TTF 185.38 is 0.368.The component reliability value is 60%, which implies that maintenance may be performed after 225 days and 100 days for componentsJE01/AP01-02 and KBE01/AP01-02, respectively.Keywords: Reliability, Monte Carlo, Component damage, RSG-GAS

Aging management experience to support reliability of RSG-GAS operation and future NPP in Indonesia

“…In addition to maintenance and repair program, aging identification is performed by preparing a questionary table to ensure SSC condition. This program should be based on information on data of design, periodic maintenance and testing of component or system [14][15]. Any actual incident and failure should be considered as a determining factor in the aging management program and the SSC performance is estimated based on the available service lifetime.…”

Pemodelan dan Simulasi Monte Carlo dalam Identifikasi Kebutuhan Bahan Bakar Minyak (BBM)

“…nilai probabilitas hasil simulasi untuk semua z dianggap sangat baik [6]. Bilangan acak yang di olah kemudian divalidasikan dengan data fakta atau data real untuk memastikan dengan kondisi yang sebenarnya [7]. Metode Monte Carlo adalah metode numerik yang dideskripsikan sebagai metode simulasi statistic.…”

Optimalisasi Prediksi Biaya Komisi Penjualan Mobil Menggunakan Metode Monte Carlo

“…Simulasi Monte Carlo adalah suatu solusi yang dapat menghilangkan ketidakpastian dalam kehandalan pemodelan, karena simulasi Monte Carlo mampu mensimulasi proses nyata dan prilaku dari sistem [14] menggunakan metode penyelesaian masalah yang melibatkan bilangan random dengan distribusi peluang atau kemungkinan yang bisa diketahui dan ditentukan [15]. Proses randomisasi mempunyai distribusi yang berasal dari berbagai variabel data yang dikelompokkan berdasarkan data histori [16], Metode Monte Carlo menggunakan generate probabilitas distribusi bilangan acak yang diolah, kemudian divalidasi dengan data fakta untuk memastikan kondisi simulasi relatif sama kondisi sebenarnya [17]. Monte Carlo merupakan salah satu metode numerik yang dideskripsikan sebagai metode simulasi statis [18] yang dapat diintegrasikan secara numerik oleh prosedur simulasi [19].…”