Iraj Mohammadfam, Abbas Shafikhani, Ali Akbar Shafikhani, Fakhreldin Ghasemi,
Volume 7, Issue 4 (12-2017)
Abstract
Introduction: Choosing maintenance strategy is one of the most complex and essential processes that can affect the safety and cost of equipment. The main aim of this study was to determine a risk-based maintenance policy for improvement of the safety and maintenance indices.
Material and Method: According to literature reviews and constraints associated with the studied industry, a number of safety and maintenance indices were selected and their values were measured. Next, in order to promote the selected indices, the best policy was implemented on nine critical machines of the company based on criteria such as cost, risk and availability in the framework of the fuzzy network analysis process. Finally, after six months period, the indices were re-measured. The Wilcoxon test was used to assess the changes in the indices.
Result: In the implementation of the model, condition based maintenance was more effective than other strategies. Following the intervention, the improvement of safety and maintenance indices was statistically significant. The statistical analyses demonstrated that indices like reliability, availability, mean time between failures, and the number of dangerous failures all were improved significantly (P<0.05).
Conclusion: The results showed that the simultaneous use of three criteria, i.e. cost, risk and availability in maintenance planning could reduce equipment-related accidents. Finally, the recommended model can improve the efficiency and competitiveness of organizations by increasing availability and reducing equipment costs
Alireza Askarian, Mahnaz Mirza Ebrahim Tehrani, Seyed Mohammad Taghi Sadatipour, Seyed Ali Jozi, Reza Marandi,
Volume 12, Issue 4 (12-2022)
Abstract
Introduction: Unit risk management is a critical component of gas refining management, as risks that are not well-managed may lead to trip production failures. The present study aimed to provide a structural model for investigating the role and effect of different variables on stopping the gas production process in the gas refinery.
Material and Methods: This study was a retrospective cross-sectional and systematic analysis, which was carried out on key risks in the trip gas sweetening unit in a gas refinery industry located in Asaluyeh, Iran. The systems analysis was applied by using Fishbone Diagram, and then data modeling was prepared by Structural Equation Modeling (SEM) for an incident that occurred during gas sweetening production. Tools for the data analysis included the SPSS 24 and Smart PLS 2 software.
Results: Results of this research indicate that “Environment Risk” with a path coefficient of 0.943 and T- Value of 103.791; “Cost Risk” with a path coefficient of 0.937 and T- Value of 95.168; “Implementation of management system Risk” with a path coefficient of 0.847 and T- Value of 35.23; “Accident Risk” with path coefficient of 0.577 and T- Value of 25.410; “Time Risk” with path coefficient of 0.758 and T- Value of 15.121; “Human Error Risk” with path coefficient of 0.712 and T- Value of 11.215 had the most important coefficients of the paths respectively, that are effective in stopping production concerning other risks. Also, by comparing the path coefficients of the risks we can see that the impact of each of the risks on stopping production is different.
Conclusion: The findings of the present study revealed that a combination of variables can affect stopping production in the gas industry. Therefore, the role of these risks in losses in the refinery system should be investigated.
Masoumeh Khoshkerdar, Reza Saeedi, Amin Bagheri, Mohammad Hajartabar, Mohammad Darvishi, Reza Gholamnia,
Volume 14, Issue 1 (3-2024)
Abstract
Introduction: The goal of this study is to investigate how the development of technology has affected the industry (especially the mining industry). For this purpose, this paper examines the impact of intelligent mining machinery systems, including tire pressure monitoring systems (TPMS), dispatching systems, and vehicle health monitoring systems (VHMS), on health, safety, and environmental parameters and preventative maintenance.
Material and Methods: This study is descriptive-analytical research that was conducted between time intervals before and after employing the intelligent mining machinery systems. Initially, parameters were identified using the Delphi method. These parameters include human accidents, equipment accidents, environmental incidents, warnings and fines in the domains of health, safety, and the environment, tire usage parameters, the shelf life of the tire, oil overfill, fuel consumption, failure rate, mean time between failures, and preventive maintenance compliance schedules in the domain of preventative maintenance. The effectiveness of using these systems was then assessed by comparing the state of the specified parameters before and after the introduction of the intelligent mining machinery systems.
Results: The findings of this research indicate that using intelligent mining machinery systems will decrease equipment accidents by 33.3%, extend the useful life of tires by 7.1%, reduce fuel consumption by 14.6%, cut the mean time required to repair by 25.5%, and enhance preventive maintenance compliance schedules by 5.7%.
The findings showed the effectiveness of the use of intelligent systems of mining machines was obtained as follows: reduction of equipment accidents by 33.3%, increasing the useful life of tires by 7.1%, reducing fuel consumption by 14.6%, reducing the average downtime of the car for repair by 25.5% and increasing compliance with the maintenance program by 5.7%.
Conclusion: Utilizing intelligent mining machinery systems might have a positive impact on the safety of machines, reduce negative environmental effects like fuel consumption, and improve the maintenance of heavy machinery, which would lead to better mining conditions and lower costs.
