Integrated Risk Assessment on Argon Purification Unit Based on FMECA and Fuzzy-AHP

Rengga Sanditya, Anda Iviana Juniani, Haidar Natsir Amrullah, Wiediartini Wiediartini

Abstract


Argon Purification Unit is a processing unit to purify the crude argon using hydrogen gas through an automatic machinery process. Based on the hazardous material and its automatic machinery process, the argon purification unit needs to be assessed for risk control consideration and business performance. This research proposed risk assessment of argon purification unit based on the failure modes using Failure Modes, Effects and Criticality Analysis (FMECA) with Fuzzy Analytical Hierarchy Process (Fuzzy-AHP) approach to minimize the risks and losses. In this research, FMECA is used to identify the potential failure modes, failure mechanism (causes), potential failure effects for each unit component and evaluate the risk by determining risk priority number (RPN). The RPN is the product of severity, occurrence, and detection variables. Then, Fuzzy-AHP is used to determine the weight of each variable based on its hierarchy. The fuzzy-AHP approach aims to increase validity and decrease expert judgment subjectivity in the risk assessment process for each failure mode by considering variables’ weight. The result of RPN is gained by multiplying each failure mode’s variables by considering the importance of variables. This research results weight of severity is 0.43, which is the highest of all variables. The highest RPN is 8.76, shown by the leaked joint of the argon compressor. This research indicates that the application of the fuzzy-AHP approach in FMECA can identify and evaluate the potential risk of the Argon Purification Unit validly and objectively, which provides the different weight of RPN variables.

Keywords


Argon Purification Unit; FMECA; Fuzzy-AHP; Risk Assessment; Risk Priority Number

Full Text:

Full Text

References


Gerbec M, Kontić B. Safety related key performance indicators for securing long-term business development – A case study. Safety Science 2017;98:77–88.

Stamatis DH. Failure Mode and Effect Analysis - FMEA from Theory to Execution (2nd Edition Revised and Expanded); 2013.

Department of the US Army. Failure modes, effects and Criticality Analysis (FMECA) for command, control, computer, intelligence, surveillance and reconnaissance (C4ISR) Facilities; 2006.

Chin KS, Chan A, Yang JB. Development of a fuzzy FMEA based product design system. International Journal of Advanced Manufacturing Technology 2008;36(7-8).

Pillay A, Wang J. Modified failure mode and effects analysis using approximate reasoning. Reliability Engineering and System Safety 2003;79(1):69–85.

George JJ, Renjith VR, George P, George AS. Application of fuzzy failure mode effect and criticality analysis on unloading facility of LNG terminal. Journal of Loss Prevention in the Process Industries 2019;61:104–113.

Sartor M, Cescon E. Failure mode and effect analysis (FMEA). In: Quality Management: Tools, Methods and Standards. Emerald Group Publishing Ltd.; 2019.p. 117–127.

Ciani L, Guidi G, Patrizi G. A Critical Comparison of Alternative Risk Priority Numbers in Failure Modes, Effects, and Criticality Analysis. IEEE Access 2019;7:92398–92409.

Braglia M, Bevilacqua M. Fuzzy modelling and analytical hierarchy processing as a means of quantifying risk levels associated with failure modes in production systems. Technology, Law and Insurance 2000 sep;5(3-4):125–134. https://www.tandfonline.com/doi/abs/10.1080/135993700750364341.

Braglia M, Frosolini M, Montanari R. Fuzzy TOPSIS Approach for Failure Mode, Effects and Criticality Analysis. Quality and Reliability Engineering International 2003;19(5):425–443.

Chang CL, Liu PH, Wei CC. Failure mode and effects analysis using grey theory. Integrated Manufacturing Systems 2001;12(3):211–216.

Gilchrist W. Modelling failure modes and effects analysis. International Journal of Quality & Reliability Management 1993 May;10(5):16–23.

Sankar NR, Prabhu BS. Modified approach for prioritization of failures in a system failure mode and effects analysis. International Journal of Quality and Reliability Management 2001;18(3):324–336.

Wang YM, Chin KS, Poon GKK, Yang JB. Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean. Expert Systems with Applications 2009;36(1):1195–1207.

Chanamool N, Naenna T. Fuzzy FMEA application to improve decision-making process in an emergency department. Applied Soft Computing Journal 2016;43:441–453.

You-Peng Z, Zheng-Jie X, Hong-Sheng S. Risk assessment on railway signal system based on Fuzzy-FMECA method. Sensors and Transducers 2013;156(9):203–210.

Giardina M, Morale M. Safety study of an LNG regasification plant using an FMECA and HAZOP integrated methodology. Journal of Loss Prevention in the Process Industries 2015;35:35–45.

Yunita D, Ayshwarya B, Ridhawati E, Huda M, Hashim A, Teh KSM, et al. Application of analytical hierarchy process method in laptop selection. International Journal of Recent Technology and Engineering 2019;8(2S3):1603–1607.

Durmuşoğlu ZDU. Assessment of techno-entrepreneurship projects by using Analytical Hierarchy Process (AHP). Technology in Society 2018;54:41–46.

Ghimire LP, Kim Y. An analysis on barriers to renewable energy development in the context of Nepal using AHP. Renewable Energy 2018;129(A):446–456.

Sossa JWZ, Halal W, Zarta RH. Delphi method: analysis of rounds, stakeholder and statistical indicators. Foresight 2019;21(5):525–544.

Seçme NY, Bayrakdaroglu A, Kahraman C. Fuzzy performance evaluation in Turkish Banking Sector using Analytic Hierarchy Process and TOPSIS. Expert Systems with Applications 2009 nov;36(9):11699–11709.

Kahraman C, Cebeci U, Ulukan Z. Multi-criteria supplier selection using fuzzy AHP. Logistics Information Management 2003 dec;16(6):382–394.

Kwiesielewicz M. A note on the fuzzy extension of Saaty’s priority theory. Fuzzy Sets and Systems 1998;95(2):161–172.

Aggarwal R, Singh S. AHP and Extent Fuzzy AHP Approach for Prioritization of Performance Measurement Attributes. Engineering and Technology 2013;7(1):6–11.

Hsu YL, Lee CH, Kreng VB. The application of Fuzzy Delphi Method and Fuzzy AHP in lubricant regenerative technology selection. Expert Systems with Applications 2010;37(1):419–425.

An M, Chen Y, Baker CJ. A fuzzy reasoning and fuzzy-analytical hierarchy process based approach to the process of railway risk information: A railway risk management system. Information Sciences 2011;181(18):3946–3966.




DOI: http://dx.doi.org/10.12962/j20882033.v31i3.6345

Refbacks

  • There are currently no refbacks.


Creative Commons License

IPTEK Journal of Science and Technology by Lembaga Penelitian dan Pengabdian kepada Masyarakat, ITS is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at https://iptek.its.ac.id/index.php/jts.