EVALUATION ON MECHANICAL FRACTURE OF PWR PRESSURE VESSEL AND MODELING BASED ON NEURAL NETWORK. The important component of the PWR is a pressure vessel. The material resistance in the pressure vessel needs to be evaluated. One way of evaluation is by the mechanical fracture analysis. The modeling needs to know the phenomena of the analysis result in general. A number of researches have been completed on the calculation of mechanical fracture in the pressure vessel with an internal load. The mechanical fracture was modeled using a neural network approach. In relation to the material resistance of the pressure vessel, which is used in PWR AP1000, the material must be evaluated because of the effect of the load. The modeling is needed to predict the effect of the load. The aim of this study is to evaluate the material resistance through mechanical fracture analysis because of the influence load on the pressure vessel on PWR AP1000. The material, which was observed, is SA 508. This analysis consists of the calculation of stress intensity factor and J-integral with some load at the crack propagation position. The fracture mechanic was analyzed by finite element simulation. The result of Stress Intensity factor and J-Integral was compared with fracture toughness to know the durability of the material. The modeling of  J-Integral and Stress Intensity Factor were obtained for some load based on neural network approach. Keywords: Material resistance, mechanical fracture, neural network, PWR, pressure vessel, crack propagation.   ABSTRAK EVALUASI FRAKTUR MEKANIK PADA BEJANA TEKAN PWR DAN PEMODELAN BERBASIS NEURAL NETWORK. Komponen penting dari PWR adalah  bejana tekan. Ketahanan bahan di bejana tekan perlu dievaluasi. Salah satu cara adalah dengan analisis fraktur mekanik. Pemodelan diperlukan untuk mengetahui fenomena hasil analisis pada umumnya. Terdapat penelitian untuk perhitungan fraktur mekanik dalam bejana tekan dengan beban internal. Penelitian lain adalah hasil dari fraktur mekanik dimodelkan menggunakan pendekatan jaringan syaraf. Sehubungan dengan ketahanan material dari bejana tekan yang digunakan dalam PWR AP1000, bahan harus dievaluasi karena efek dari beban. Pemodelan diperlukan untuk memprediksi pengaruh beban pada bahan dalam bejana tekan. Tujuan dari penelitian ini adalah untuk mengevaluasi ketahanan material melalui analisis fraktur mekanik karena pengaruh beban pada bejana tekan. Bahan yang diamati, adalah SA 508. Analisis ini terdiri dari perhitungan faktor intensitas tegangan dan J-integral dengan beberapa beban pada posisi perambatan retak. Fraktur mekanik dianalisis dengan metode elemen hingga. Hasil faktor intensitas tegangan dan J-Integral dibandingkan dengan ketangguhan patah untuk mengetahui daya tahan material. Pemodelan J-Integral dan faktor intensitas stres diperoleh untuk beberapa beban berdasarkan  jaringan saraf. Kata kunci: Ketahanan bahan, teknik patahan,  jaringan syaraf,  PWR,  bejana tekan, perambatan retak.

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

Reliability management is an activity to ensure no failure of all equipment when operated. Reliability management can be optimized to minimize costs or eliminate failures and causes. Critical equipment is the condition of a potentially damaging component affecting the operational reliability of the system. The criticality level of each equipment determines its impact on the operating system and the direction of maintenance improvement. The research was conducted on the main system/component of the operating system and performed at the level of reliability improvement. The purpose of this research is to prioritize the reliability of systems and equipment for safety systems using System Equipment Reliability Prioritization (SERP). Determination of component criticality level on reliability management based on category rankings of frequency data and duration of interference with certain criteria as well as system aspects, safety, quality and cost. From the evaluation results it can be concluded that the MPI of the RSG-GAS system/ component for the top 5 if sorted are KBE01 AP-01-02, PA01-02 / CR001, KBE02 AA-01/ AA-02, JE-01 (AP01-02 ) and JNA10 / 20/30 BC001 with  MPI values 143,101, 95, 90 and 60.Keywords: Maintenance, priority, index, safety system, RSG-GAS PENENTUAN MAINTENANCE PRIORITY INDEX (MPI) UNTUK KOMPONEN PADA SISTEM KESELAMATAN RSG-GAS. Manajemen keandalan  merupakan suatu kegiatan untuk menjamin tidak terjadinya suatu kegagalan pada seluruh komponen saat dioperasikan. Dengan manajemen keandalan dapat dilakukan optimasi untuk meminimumkan biaya atau menghilangkan kegagalan dan penyebabnya. komponen kritis merupakan kondisi suatu komponen yang berpotensi mengalami kerusakan yang berpengaruh pada keandalan operasional sistem. Tingkat kekritisan dari setiap komponen menentukan dampaknya terhadap sistem operasi dan arah penyempurnaan pemeliharaan. Penelitian dilakukan pada sistem/komponen yang utama dari sistem operasi dan dilakukan pada level peningkatan keandalan. Tujuan dari penelitian ini adalah menentukan indeks prioritas pemeliharaan (MPI) untuk peringkat keandalan sitem/komponen pada system keselamatan menggunakan metode System Equipment Reliability Prioritization (SERP). Penentuan tingkat kekritisan komponen pada manajemen keandalan berdasarkan peringkat kategori dari data durasi dan frekuensi gangguan  dengan kriteria tertentu serta aspek sistem, keselamatan, kualitas dan biaya. Dari hasil evaluasi dapat disimpulkan bahwa MPI dari sistem/komponen RSG-GAS untuk 5 teratas jika diurutkan adalah: KBE01 AP-01-02, PA01-02 / CR001, KBE02 AA-01 / AA-02, JE-01 (AP01-02) dan JNA10 / 20/30 BC001 dengan nilai MPI berturut turut 143,101, 95, 90 dan 60.Kata kunci:         Pemeliharaan, prioritas, indeks, sistem keselamatan, RSG-GAS

IMPLEMENTATION OF MISSING VALUES HANDLING METHOD FOR EVALUATING THE SYSTEM/COMPONENT MAINTENANCE HISTORICAL DATA.Missing values are problems in data evaluation. Missing values analysis can resolve the problem of incomplete data that is not stored properly. The missing data can reduce the precision of calculation, since the amount of information is incomplete. The purpose of this study is to implement missing values handling method for systems/components maintenance historical data evaluation in RSG GAS. Statistical methods, such as listwise deletion and mean substitution, and machine learning (KNNI), were used to determine the missing data that correspond to the systems/components maintenance historical data. Mean substitution and KNNI methods were chosen since those methods do not require the formation of predictive models for each item which is experiencing missing data. Implementation of missing data analysis on systems/components maintenance data using KNNI method results in the smallest RMSE value. The result shows that KNNI method is the best method to handle missing value compared with listwise deletion or mean substitution.

FRACTURE MECHANICS UNCERTAINTY ANALYSIS IN THE RELIABILITY ASSESSMENT OF THE REACTOR PRESSURE VESSEL: (2D) SUBJECTED TO INTERNAL PRESSURE.The reactor pressure vessel (RPV) is a pressure boundary in the PWR type reactor which serves to confine radioactive material during chain reaction process. The integrity of the RPV must be guaranteed either in a normal operation or accident conditions. In analyzing the integrity of RPV, especially related to the crack behavior which can introduce break to the reactor pressure vessel, a fracture mechanic approach should be taken for this assessment. The uncertainty of input used in the assessment, such as mechanical properties and physical environment, becomes a reason that the assessment is not sufficient if it is perfomed only by deterministic approach. Therefore, the uncertainty approach should be applied. The aim of this study is to analize the uncertainty of fracture mechanics calculations in evaluating the reliability of PWR`s reactor pressure vessel. Random character of input quantity was generated using probabilistic principles and theories. Fracture mechanics analysis is solved by Finite Element Method (FEM) with MSC MARC software, while uncertainty input analysis is done based on probability density function with Latin Hypercube Sampling (LHS) using python script. The output of MSC MARC is a J-integral value, which is converted into stress intensity factor for evaluating the reliability of RPV's 2D. From the result of the calculation, it can be concluded that the SIF from probabilistic method, reached the limit value of fracture toughness earlier than SIF from deterministic method. The SIF generated by the probabilistic method is 105.240 MPa m 0.5 . Meanwhile, the SIF generated by deterministic method is 100.