{"title":"Reliability assessment on natural gas pressure reduction stations using Monte Carlo simulation (MCS)","authors":"A. Karimi, E. Zarei, R. Hokmabadi","doi":"10.30699/ijrrs.5.1.4","DOIUrl":"https://doi.org/10.30699/ijrrs.5.1.4","url":null,"abstract":"Gas pressure reduction stations play a key role in the timely and safe supply of natural gas (NG) to residential, commercial, and industrial customers. Accordingly, system reliability analysis should be performed to prevent potential failures and establish resilient operations. This research proposed a reliability assessment approach to natural gas pressure-reducing stations using historical data, statistical analysis, and Monte Carlo simulation (MCS). Historical data are employed to establish the probability distributions of the system and subsystems in gas stations. Then the Kolmogorov-Smirnov test is conducted to assess the goodness-of-fit for the developed distributions. Bayesian network (BN) is utilized to develop a system failure causality model. Finally, we performed MCS to precisely predict the failure rate and reliability of stations and all subsystems, such as the regulator, separator and dry gas filters, shut-off valves, and regulator. This research provided numerical findings on the reliability indicators of pressure reduction stations which can be used to improve system performance and, subsequently, the resilience of NG pipelines.","PeriodicalId":395350,"journal":{"name":"International Journal of Reliability, Risk and Safety: Theory and Application","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130020057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Reliability Improvement in Distribution Systems Via Game Theory","authors":"Mohammad Rahim Mohammadi, H. Rajabi Mashhadi","doi":"10.30699/ijrrs.5.1.12","DOIUrl":"https://doi.org/10.30699/ijrrs.5.1.12","url":null,"abstract":"This paper presents a new competitive approach to provide reliability for distribution system customers. The model is based on the Cournot game and utilizes the Nash equilibrium concept to find the output of the problem. Reliability in the proposed framework is an ancillary service and the customers who participated in the program must pay for reliability provision. The proposed model also considers regulatory concerns of reliability insuring the average reliability of the system is not incurred. Based on the proposed model, customers will compete for their reliability enhancement considering all the constraints related to the network, regulator and each customer. The expected outage time for each customer is considered the reliability index in this paper. The model is investigated in a sample case study and the results show how a customer would behave if they participated in the reliability improvement program of distribution systems. Our results also show that there would exist a high motivation for both parties (utility and customers) to implement the proposed model for the reliability enhancement of the distribution system.","PeriodicalId":395350,"journal":{"name":"International Journal of Reliability, Risk and Safety: Theory and Application","volume":"771 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117018948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Stress-Strength Weibull Analysis Applied to Estimate Reliability Index in Industry 4.0","authors":"Manuel Baro Tijerina, Manuel Román Pina Monarrez","doi":"10.30699/ijrrs.4.2.8","DOIUrl":"https://doi.org/10.30699/ijrrs.4.2.8","url":null,"abstract":"With technological advances, companies are allowed to integrate digital data, physical supplies, and human resources, and all this integration capability can be done thanks to Industry 4.0. This concept, also called the fourth industrial revolution, refers to smart companies that work with intelligent cyber-physical systems. Industry 4.0enables automation, data interchange, and big data processing, among others. Then, the process decision-making, efficiency, and productivity improvement for companies will become faster and more accurate, thanks to real-time data processes and all supply chain integration allowed by Industry 4.0. However, the implementation of Industry 4.0 carries several challenges for companies to have success in the transformation of a normal industry into an Industry 4.0, like the necessity of adding new hardware, software, and other technologic devices. Because of this, the implementation and control of Industry 4.0 come with new issues to handle and new failure modes for both hardware and electronic devices. These problems can be faced using reliability engineering tools. Then the object of this research is the use of reliability engineering methodology stress-strength Weibull analysis, highlighting that the behavior of frequency emitted by electronics devices follows a Weibull distribution most of the time. Also, a stress-strength Weibull with a different shape parameter close solution is presented to increase the efficiency and productivity in Industry 4.0 electronic devices.","PeriodicalId":395350,"journal":{"name":"International Journal of Reliability, Risk and Safety: Theory and Application","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132343896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"On Possibility of Extending the Optimal Replacement time of Series and Parallel Systems","authors":"Tijjani A. Waziri, M. Isa","doi":"10.30699/ijrrs.4.2.4","DOIUrl":"https://doi.org/10.30699/ijrrs.4.2.4","url":null,"abstract":"Among all systems, the series system has the lowest optimal replacement time, while the parallel system has the highest optimal replacement time. This paper is comparing the standard age replacement strategy (SARS) with some proposed replacement strategies (strategy A and strategy B) for two multi-unit systems. Two numerical examples are provided for a simple illustration of the proposed replacement cost models under SARS, strategies A and B. The results obtained showed that strategy B can extend the optimal replacement time of a series system","PeriodicalId":395350,"journal":{"name":"International Journal of Reliability, Risk and Safety: Theory and Application","volume":"392 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123201129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Thermal Sensitivity Analysis of a Telemetry Antenna in Sub-Orbital Spaceflights","authors":"Mahmoud Talafi Noghani, M. Nadjafi","doi":"10.30699/ijrrs.4.2.2","DOIUrl":"https://doi.org/10.30699/ijrrs.4.2.2","url":null,"abstract":"A thermal sensitivity analysis is performed for an Inverted-F antenna (IFA) at the worst-case thermal condition (reentry phase) of a typical sub-orbital sounding-rocket spaceflight. Electromagnetic simulations show that a 300 Celsius change in the IFA temperature, mainly caused by the aerodynamic heating, increases the reflected power by 1% and decreases the antenna gain by 2.5%. The results show that the degradation of antenna performance and the telemetry link quality is negligible. Therefore, the IFA is a good antenna choice for similar sounding rockets from the thermal reliability point of view","PeriodicalId":395350,"journal":{"name":"International Journal of Reliability, Risk and Safety: Theory and Application","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125266252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zahra Dehghani Ghobadi, F. Haghighi, Abdollah Safari
{"title":"An overview of reinforcement learning and deep reinforcement learning for condition-based maintenance","authors":"Zahra Dehghani Ghobadi, F. Haghighi, Abdollah Safari","doi":"10.30699/ijrrs.4.2.9","DOIUrl":"https://doi.org/10.30699/ijrrs.4.2.9","url":null,"abstract":"Condition-based maintenance (CBM) involves making decisions on maintenance based on the actual deterioration conditions of the components. It consists of a chain of states representing various stages of deterioration and a set of maintenance actions. Therefore, condition-based maintenance is a sequential decision-making problem. Reinforcement Learning(RL) is a subfield of Machine Learning proposed for automated decision-making. This article provides an overview of reinforcement learning and deep reinforcement learning methods that have been used so far in condition-based maintenance optimization","PeriodicalId":395350,"journal":{"name":"International Journal of Reliability, Risk and Safety: Theory and Application","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124548359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Analyzing Reliability of CGS Station by Continuous Time Markov Chains (CTMC)","authors":"A. Karimi, E. Zarei, R. Hokmabadi","doi":"10.30699/ijrrs.4.2.10","DOIUrl":"https://doi.org/10.30699/ijrrs.4.2.10","url":null,"abstract":"Improving the system's reliability is one way to achieve a secure system. City Gas Station (CGS) has a key role in the timely and safe supply of Natural gas (NG) to residential, commercial, and industrial customers. With complexities inherent in systems, having a proper and all-embracing model of the entirety of a system is not readily possible. The continuous-time Markov chain (CTMC) model is regarded as a great help in communicating, comparing, and integrating partial system models. In this study, we have exploited CTMC for reliability analysis in CGS stations. The CTMC model can solve both time-dependent and stationary state probabilities. Therefore, it can potentially develop the state enumeration method into a sequential one. Implementing this procedure leads to identifying critical components and failure probability, eventually enhancing the station's reliability. Additionally, some suggestions are presented for optimizing the performance of the station.","PeriodicalId":395350,"journal":{"name":"International Journal of Reliability, Risk and Safety: Theory and Application","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114419600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"LSTM Encoder-Decoder Dropout Model in Software Reliability Prediction","authors":"S. Oveisi, A. Moeini, S. Mirzaei","doi":"10.30699/ijrrs.4.2.1","DOIUrl":"https://doi.org/10.30699/ijrrs.4.2.1","url":null,"abstract":"Numerous methods have been introduced to predict the reliability of software. In general, these methods can be divided into two main categories, namely parametric (e.g. software reliability growth models) and non-parametric (e.g. neural networks). Both approaches have been successfully implemented in software testing applications over the past four decades. Since most software reliability prediction data are available in the form of time series, deep recurrent network models (e.g. RNN, LSTM, NARX, and LSTM Encoder-Decoder networks) are considered as powerful tools to be employed in reliability-related problems. However, the problem of overfitting is a major concern when using deep neural networks for software reliability applications. To address this issue, we propose the use of dropout; therefore, this study utilizes a deep learning model based on LSTM Encoder-Decoder Dropout to predict the number of faults in software and assess software reliability. Experimental results show that the proposed model has better prediction performance compared with other RNN-based models.","PeriodicalId":395350,"journal":{"name":"International Journal of Reliability, Risk and Safety: Theory and Application","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124823588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Markov Modeling and Reliability analysis of solar photovoltaic system Using Gumbel Hougaard Family Copula","authors":"A. Maihulla, I. Yusuf","doi":"10.30699/ijrrs.4.2.6","DOIUrl":"https://doi.org/10.30699/ijrrs.4.2.6","url":null,"abstract":"The present work illustrated the reliability analysis of solar photovoltaic systems and the efficiency of medium grid-connected photovoltaic (PV) power systems with 1-out of- 2 PV panels, one out of one charge controller, 1- out of 3 batteries, 1- out of 2 inverters and one out one Distributor. The units that comprise the solar were studied. Gumbel Hougaard Family Copula method was used to evaluate the performances of solar photovoltaics. Other reliability metrics were investigated, including availability, mean time to failure, and sensitivity analysis. The numerical result was generated using the Maple 13 software. The numerical results were presented in tables, with graphs to go along with them. Failure rates and their effects on various solar photovoltaic subsystems were investigated. Numerical examples are provided to demonstrate the obtained results and to assess the influence of various system characteristics. The current research could aid companies, and their repairers overcome some issues that specific manufacturing and industrial systems repairers face.","PeriodicalId":395350,"journal":{"name":"International Journal of Reliability, Risk and Safety: Theory and Application","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125873674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Prediction of Cavitation Failure in Crankpin Bearings","authors":"H. Karimaei","doi":"10.30699/ijrrs.4.2.3","DOIUrl":"https://doi.org/10.30699/ijrrs.4.2.3","url":null,"abstract":"Cavitation failure is a common failure in bearing shells. Due to the generation and immediate collapse of small gas bubbles, causing high-pressure pulses, the bearing surface is being locally damaged. Cavitation failure is also observed in IC engines due to highly dynamic loading, oscillation of pins, the turbulence of oil flow, and other factors. In this paper, cavitation failure in the crankpin bearing of an IC engine is studied. In order to calculate the bearing lubrication characteristic such as oil fill ratio and maximum oil film pressure, the Elasto-Hydrodynamic Lubrication (EHL) method to consider the effect of stiffness of the bearing shell housing in the model is utilized that incorporates mass conserving algorithms. In order to investigate the effect of some design parameters, such as clearance height between shaft and bearing shell, oil supply temperature and pressure, and oil bore position, on the cavitation failure, a parametric study was also done. The results showed that the cavitation failure in crankpin bearing is not critical and it is slight.","PeriodicalId":395350,"journal":{"name":"International Journal of Reliability, Risk and Safety: Theory and Application","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123951281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}