A Risk and Reliability-Based Scheduling Method for Troubleshooting Regulators in Gas Pressure Stations: A Case Study of Isfahan Gas Company

Document Type : Research Paper

Authors

1 Assistant prof., Department of Management and Innovation, Faculty of Administrative Sciences & Economics, Shahid Ashrafi Esfahani University, Isfahan, Iran.

2 Assistant prof., Department of Industrial Engineering, Faculty of Engineering, Shahid Ashrafi Esfahani University, Isfahan, Iran.

3 M.Sc., Faculty of management and innovation, Shahid Ashrafi Esfahani University, Isfahan, Iran.

10.22059/imj.2025.387414.1008211

Abstract

Objective: This research presents a new method for scheduling troubleshooting operations of station regulators in natural gas distribution stations, focusing on the importance index of equipment, reliability, and risk management.
Methods: Using reliability-based maintenance principles and the expertise of professionals from Isfahan Gas Company, we selected 166 regulators from 112 pressure reduction stations in Isfahan. We assessed the importance index of each station and evaluated the potential consequences of its failure risks, followed by calculating its reliability metrics. The results were grouped using the K-means clustering method. Ultimately, we identified the optimal time frame for conducting troubleshooting operations. 
Results: In this study, 166 regulators were grouped into three clusters. The average time required to perform troubleshooting activities varied among the clusters. For the first cluster, the average time was determined to be 48 hours. The second cluster had an average troubleshooting time of 544 hours, while the third cluster had an average of 829 hours. Currently, the average time for troubleshooting regulators is 720 hours.
Conclusion: This paper presents the following contributions: 1. Identification of the station importance index based on the gas supply mission to subscribers and end consumers. 2. Localization of the method for estimating risks and consequences arising from station equipment failures. 3. Assessment of equipment reliability. 4. Clustering of key regulatory equipment in the case study.

Keywords

References

Attia, A. M. (2025). Integrated risk management and maintenance planning in Oil and Gas Supply Chain operations under market uncertainty. Computers & Chemical Engineering, 192, 108879. doi:https://doi.org/10.1016/j.compchemeng.2024.108879
Fan, A., Huang, Z., Zheng, Q., & Luo, X. (2024). A two-stage stochastic programming approach for generation and transmission maintenance scheduling with risk management. Computational Optimization and Applications, 1-23. doi:https://doi.org/10.1007/s10589-024-00624-1
Gandhare, S., Kumar, P., Madankar, T., Singh, D., & Bhamu, J. (2025). Development and validation of an FMEA-based medical equipment maintenance framework using Industry 4.0. International Journal of Quality & Reliability Management. doi:https://doi.org/10.1108/IJQRM-07-2024-0246
Ge, J., Sigsgaard, K. V., Andersen, B. S., Mortensen, N. H., Agergaard, J. K., & Hansen, K. B. (2024). An adaptable end-to-end maintenance performance diagnostic framework. International Journal of Quality & Reliability Management, 41(2), 732-753. doi:https://doi.org/10.1108/IJQRM-02-2022-0071
Geisbush, J., & Ariaratnam, S. T. (2023). Reliability centered maintenance (RCM): literature review of current industry state of practice. Journal of Quality in Maintenance Engineering, 29(2), 313–337. doi:https://doi.org/10.1108/JQME-02-2021-0018
Goni, K. M., Mohammed, A., Sundararajan, S., & Kassim, S. I. (2024). Proactive Risk Management in Smart Manufacturing: A Comprehensive Approach to Risk Assessment and Mitigation Artificial Intelligence Solutions for Cyber-Physical Systems (pp. 139-164): Auerbach Publications.
Gupta, G., Mishra, R., & Singhvi, P. (2016). An application of reliability centered maintenance using RPN mean and range on conventional lathe machine. International Journal of Reliability, Quality and Safety Engineering, 23(06), 1640010. doi:https://doi.org/10.1142/S0218539316400106
Hokmabadi, R., Zarei, E., & Karimi, A. (2022). Identifying, Assessing, and Prioritizing Pressure Reduction Station Risks Using FMEA Based on SWARA-VIKOR Multi-criteria Decision-making Methods [In Persian]. Journal of Health and Safety at Work, 12(3), 632–651. doi:http://doi.org/20.1001.1.2251807.1401.12.3.12.6
Jasiulewicz-Kaczmarek, M., Legutko, S., & Kluk, P. (2020). Maintenance 4.0 technologies–new opportunities for sustainability driven maintenance. Management and production engineering review, 11. doi:http://doi.org/10.24425/mper.2020.133730
Khedry, H., Jamali, G., & Ghorbanpour, A. (2020). A Mixed Approach for Evaluating Preventive Maintenance Performance Based on Anti-Fragility Factors [In Persian]. Research in Production and Operations Management, 11(3), 73–94. doi: 10.22108/jpom.2021.124605.1287
Kıvanç, E., Tuzkaya, G., & Vayvay, Ö. (2025). Safety management system and risk-based approach in aviation maintenance: A systematic literature review. Safety science, 184, 106755. doi:https://doi.org/10.1016/j.ssci.2024.106755
Kumar, M. P., Raju, N., Kumar, M. S., & Gupta, G. (2024). Risk assessment and prioritization using fuzzy FMECA: A case study of dumper breakdowns. International Journal of System Assurance Engineering and Management, 1–15. doi:https://doi.org/10.1007/s13198-024-02506-5
Mohtadi, M. M., & sheikh, r. m. (2021). Maintenance optimal strategy selection based on the risk of equipment failure in the Fourth South Pars refinery [In Persian]. ANDISHEH AMAD, 20(77), 81–109.
Olutimehin, A. T., Olaniyi, O. O., Popoola, A. D., Ogunmolu, A. M., & Kolo, F. H. O. (2025). AI and IoT Integration for Predictive Maintenance and Risk Management in Smart Manufacturing. Asian Journal of Research in Computer Science, 18(7), 120-142. doi: https://doi.org/10.9734/ajrcos/2025/v18i7724
Paltrinieri, N., Landucci, G., & Salvo Rossi, P. (2017). Real-time data for risk assessment in the offshore oil and gas industry. Paper presented at the International Conference on Offshore Mechanics and Arctic Engineering.
Pilanawithana, N. M., Feng, Y., London, K., & Zhang, P. (2022). Developing resilience for safety management systems in building repair and maintenance: A conceptual model. Safety science, 152, 105768. doi:https://doi.org/10.1016/j.ssci.2022.105768
Preußker, M., Büttgen, R., Noé, M., & Heufer, K. (2024). Finding a common ground for RCM experiments. Part A: On the influences of facility effects regarding the reliability of experimental validations. Combustion and Flame, 262, 113323. doi:https://doi.org/10.1016/j.combustflame.2024.113323
Rodriguez, P. C., Marti-Puig, P., Caiafa, C. F., Serra-Serra, M., Cusidó, J., & Solé-Casals, J. (2023). Exploratory analysis of SCADA data from wind turbines using the K-means clustering algorithm for predictive maintenance purposes. Machines, 11(2), 270. doi:https://doi.org/10.3390/machines11020270
Salehian, Z., & Jahan, A. (2022). Establishing a Reliability-based Maintenance methodology in a Gas Pressure Reduction System [In Persian]. Journal of System Engineering and Productivity, 1(1), 121–136. doi:http://doi.org/10.22034/sep.2022.243401
Shahin, A., Ghofrani Isfahani, N., & Nilipour Tabatabaei, S. A. (2013). Determining an appropriate maintenance strategy based on decision-making grid, Sigma level, and process capability index–with a case study in a steel company. International Journal of Applied Management Science, 5(3), 265–280. doi:https://doi.org/10.1504/IJAMS.2013.055442
Shan, X., Wang, H., Di Wang, W. Y., Wen, K., Gong, J., Zheng, H., . . . Wei, S. (2024). A Methodology to Determine the Maximum Allowable Repair Time for Critical Units of Natural Gas Pipeline Systems Using Gas Supply Reliability Theory. doi:https://www.energy-proceedings.org/wp-content/uploads/icae2023/1704123789
Siahaan, J. P., Yaqin, R. I., Priharanto, Y. E., Abrori, M. Z. L., & Siswantoro, N. (2024). Risk-based maintenance strategies on fishing vessel refrigeration systems using fuzzy-FMEA. Journal of Failure Analysis and Prevention, 24(2), 855-876. doi:https://doi.org/10.1007/s11668-024-01878-x
Soltanali, H., Rohani, A., Abbaspour-Fard, M. H., Parida, A., & Farinha, J. T. (2020). Development of a risk-based maintenance decision-making approach for the automotive production line. International Journal of System Assurance Engineering and Management, 11(1), 236–251. doi:https://doi.org/10.1007/s13198-019-00927-1
Song, M., Zhang, X., & Lind, M. (2023). Automatic identification of maintenance significant items in reliability centered maintenance analysis by using functional modeling and reasoning. Computers & Industrial Engineering, 182, 109409. doi:https://doi.org/10.1016/j.cie.2023.109409
Sulistiyono, R. T., Juniani, A. I., & Setyana, I. (2008). Implementation of RCM II (Reliability Centered Maintenance) and RPN (Risk Priority Number) in Risk Assessment and Scheduling Maintenance Task at HPB (High Pressure Boiler) Base On JSA (Job Safety Analysis)(Case study at PT. SMART Tbk. Surabaya). Performa: Media Ilmiah Teknik Industri, 7(2). doi:https://doi.org/10.20961/performa.7.2.13794
Wang, K., Dong, P., Chen, W., Ma, R., & Cui, L. (2024). Research on risk management of ship maintenance projects based on multi agent swarm model simulation method. Heliyon, 10(19). doi:https://doi.org/10.1016/j.heliyon.2024.e38785
Xu, A., Zhang, Z., Zhang, H., Zhang, M., Wang, H., Ma, Y., . . . Zheng, G. (2018). Real-time online risk monitoring and management method for maintenance optimization in nuclear power plant. Paper presented at the International Conference on Nuclear Engineering.
Industrial Management Journal
Volume 17, Issue 4
2025
Pages 56-75

Files

Share

How to cite

Statistics