Volume 13, Issue 2 (6-2023)
J Health Saf Work 2023, 13(2): 252-268 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Hokmabadi R, Zarei E, Karimi A. Assessing the Reliability of the City Gate Station Using Monte Carlo Simulation. J Health Saf Work 2023; 13 (2) :252-268
URL: http://jhsw.tums.ac.ir/article-1-6841-en.html
URL: http://jhsw.tums.ac.ir/article-1-6841-en.html
1- epartment of Occupational Health, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran | Department of Occupational Health Engineering, School of Public Health, North Khorasan University of Medical Sciences, Bojnurd, Iran
2- Department of Safety Science, College of Aviation, Embry-Riddle Aeronautical University, Prescott, AZ, 86301, USA, | Robertson Safety Institute (RSI), Embry-Riddle Aeronautical University, Prescott, AZ, 86301, USA
3- Department of Occupational Health, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran ,a_karimi@sina.tums.ac.ir
2- Department of Safety Science, College of Aviation, Embry-Riddle Aeronautical University, Prescott, AZ, 86301, USA, | Robertson Safety Institute (RSI), Embry-Riddle Aeronautical University, Prescott, AZ, 86301, USA
3- Department of Occupational Health, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran ,
Abstract: (725 Views)
Introduction: Reliability is always of particular importance in system design and planning; thus, improving reliability is among the approaches for achieving a safe system. Simulation methods are widely used in system reliability assessment. Therefore, this study aims to assess the reliability of the City Gate Gas Station (CGS) using Monte Carlo Simulation (MCS).
Material and Methods: This descriptive and analytical study was conducted in one of the CGSs of North Khorasan Province in 2021. The CGS process was carefully examined and its block diagram was plotted. Then, failure time data of CGS equipment were collected over 11 years and time between failures of subsystems was calculated. The failure probability distribution function of subsystems was determined using Easy Fit software and Kolmogorov-Smirnov test. Moreover, subsystems’ reliability was estimated by MCS. Finally, station reliability was calculated considering the series-parallel structure of the CGS.
Results: The results revealed that the failure probability density distribution function of CGS subsystems was based on gamma and normal functions. The reliabilities of filtration, heater, pressure reduction system, and odorize were calculated as 0.97, 0.987, 0.98, and 0.992 respectively, and their failure rates were 0.000003477, 0.0000014937, 0.0000023062, and 0.0000009169 failures per hour respectively. The station reliability was calculated as 0.93.
Conclusion: The failure probability distribution function and reliability assessment of subsystems were determined by data modeling and MCS respectively. Filtration and pressure reduction systems had the highest failure rate and required a proper maintenance program.
Material and Methods: This descriptive and analytical study was conducted in one of the CGSs of North Khorasan Province in 2021. The CGS process was carefully examined and its block diagram was plotted. Then, failure time data of CGS equipment were collected over 11 years and time between failures of subsystems was calculated. The failure probability distribution function of subsystems was determined using Easy Fit software and Kolmogorov-Smirnov test. Moreover, subsystems’ reliability was estimated by MCS. Finally, station reliability was calculated considering the series-parallel structure of the CGS.
Results: The results revealed that the failure probability density distribution function of CGS subsystems was based on gamma and normal functions. The reliabilities of filtration, heater, pressure reduction system, and odorize were calculated as 0.97, 0.987, 0.98, and 0.992 respectively, and their failure rates were 0.000003477, 0.0000014937, 0.0000023062, and 0.0000009169 failures per hour respectively. The station reliability was calculated as 0.93.
Conclusion: The failure probability distribution function and reliability assessment of subsystems were determined by data modeling and MCS respectively. Filtration and pressure reduction systems had the highest failure rate and required a proper maintenance program.
Type of Study: Research |
Received: 2023/06/29 | Accepted: 2023/06/22 | Published: 2023/06/22
Received: 2023/06/29 | Accepted: 2023/06/22 | Published: 2023/06/22
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
