Design Software Failure Mode and Effect Analysis using Fuzzy TOPSIS Based on Fuzzy Entropy
Subject Areas : Software Architecture
shahrzad Oveisi
1
,
Mohammad Nadjafi
2
,
Mohammad Ali Farsi
3
,
Ali moeini
4
,
Mahmood Shabankhah
5
1 - Department of Algorithms and Computation, College of Engineering Sciences, University of Tehran, Tehran, Iran
2 - Aerospace Research Institute (Ministry of Science, Research and Technology), Tehran, P.O.B. 14665-834, Iran
3 - Aerospace Research Institute (Ministry of Science, Research and Technology), Tehran, P.O.B. 14665-834, Iran
4 - Department of Algorithms and Computation, College of Engineering Sciences, University of Tehran, Tehran, Iran
5 - Department of Algorithms and Computation, College of Engineering Sciences, University of Tehran, Tehran, Iran
Keywords: Risk Assessment, Fuzzy TOPSIS, Software FMEA, Requirement analysis, Fuzzy Entropy, Expert Opinion,
Abstract :
One of the key pillars of any operating system is its proper software performance. Software failure can have dangerous effects and consequences and can lead to adverse and undesirable events in the design or use phases. The goal of this study is to identify and evaluate the most significant software risks based on the FMEA indices with respect to reduce the risk level by means of experts’ opinions. To this end, TOPSIS as one of the most applicable methods of prioritizing and ordering the significance of events has been used. Since uncertainty in the data is inevitable, the entropy principle has been applied with the help of fuzzy theory to overcome this problem to weigh the specified indices.The applicability and effectiveness of the proposed approach is validated through a real case study risk analysis of an Air/Space software system. The results show that the proposed approach is valid and can provide valuable and effective information in assisting risk management decision making of our software system that is in the early stages of software life cycle. After obtaining the events and assessing their risk using the existing method, finally, suggestions are given to reduce the risk of the event with a higher risk rating.
[1]. Farsi, M. A., & Zio, E. (2019). Industry 4.0: Some challenges and opportunities for Reliability Engineering. International Journal of Reliability, Risk and Safety: Theory and Application, 2(1), 23-34.
[2]. Zio, E. (2016). Some challenges and opportunities in reliability engineering. IEEE Transactions on Reliability, 65(4), 1769-1782.
[3]. Oveisi, S., & Ravanmehr, R. (2017). Analysis of software safety and reliability methods in cyber physical systems. International journal of critical infrastructures, 13(1), 1-15.
[4]. Oveisi, S., Farsi, M. A., Nadjafi, M., & Moeini, A., 2019. A New Approach to Promote Safety in the Software Life Cycle. Journal of Computer & Robotics, 12(1), 77-91.
[5]. Narayanagounder, S., & Gurusami, K.,2009. A new approach for prioritization of failure modes in design FMEA using ANOVA. World Academy of Science, Engineering and Technology, 49(524-31).
[6]. Stamatis, D. H. (2003). Failure mode and effect analysis: FMEA from theory to execution. Quality Press.
[7]. Patil, S. K., & Kant, R. (2014). A fuzzy AHP-TOPSIS framework for ranking the solutions of Knowledge Management adoption in Supply Chain to overcome its barriers. Expert systems with applications, 41(2), 679-693.
[8]. Liu, H. C., Liu, L., Liu, N., & Mao, L. X. (2012). Risk evaluation in failure mode and effects analysis with extended VIKOR method under fuzzy environment. Expert Systems with Applications, 39(17), 12926-12934.
[9]. Bowles, J. B., & Peláez, C. E. (1995). Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis. Reliability Engineering & System Safety, 50(2), 203-213.
[10]. Kutlu, A. C., & Ekmekçioğlu, M. (2012). Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP. Expert Systems with Applications, 39(1), 61-67.
[11]. Liu, H. C., Liu, L., & Liu, N. (2013). Risk evaluation approaches in failure mode and effects analysis: A literature review. Expert systems with applications, 40(2), 828-838..
[12]. Yazdani, M., Alidoosti, A., & Zavadskas, E. K. (2011). Risk analysis of critical infrastructures using fuzzy COPRAS. Economic research-Ekonomska istraživanja, 24(4), 27-40.
[13]. Zhang, F., & Zhang, W. (2015, August). Failure modes and effects analysis based on fuzzy TOPSIS. In 2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS) (pp. 588-593). IEEE.
[14]. CAO, W., NIU, C., & FAN, Y. (2013). A Grey Relational Projection Method for Multi-attribute Decision Making Based on Interval-valued Intuitionistic Trapezoidal Fuzzy Number. Journal of Taiyuan University of Technology, 2(026), 3467-3477..
[15]. Oveisi, S., & Farsi, M. A. (2018). Software safety analysis with UML-Based SRBD and fuzzy VIKOR-Based FMEA. International Journal of Reliability, Risk and Safety: Theory and Application, 1(1), 35-44.
[16]. Abdelgawad, M., & Fayek, A. R. (2010). Risk management in the construction industry using combined fuzzy FMEA and fuzzy AHP. Journal of Construction Engineering and Management, 136(9), 1028-1036.
[17]. Ilangkumaran, M., Shanmugam, P., Sakthivel, G., & Visagavel, K. (2014). Failure mode and effect analysis using fuzzy analytic hierarchy process. International Journal of Productivity and Quality Management, 14(3), 296-313.
[18]. Liu, H. C., You, J. X., You, X. Y., & Shan, M. M. (2015). A novel approach for failure mode and effects analysis using combination weighting and fuzzy VIKOR method. Applied soft computing, 28, 579-588.
[19]. Liu, H. C., Fan, X. J., Li, P., & Chen, Y. Z. (2014). Evaluating the risk of failure modes with extended MULTIMOORA method under fuzzy environment. Engineering Applications of Artificial Intelligence, 34, 168-177.
[20]. Liu, H. C., You, J. X., Lin, Q. L., & Li, H. (2015). Risk assessment in system FMEA combining fuzzy weighted average with fuzzy decision-making trial and evaluation laboratory. International Journal of Computer Integrated Manufacturing, 28(7), 701-714.
[21]. Braglia, M., Frosolini, M., & Montanari, R. (2003). Fuzzy TOPSIS approach for failure mode, effects and criticality analysis. Quality and reliability engineering international, 19(5), 425-443.
[22]. Yang, J., Huang, H. Z., He, L. P., Zhu, S. P., & Wen, D. (2011). Risk evaluation in failure mode and effects analysis of aircraft turbine rotor blades using Dempster–Shafer evidence theory under uncertainty. Engineering Failure Analysis, 18(8), 2084-2092.
[23]. Oveisi, S., & Ravanmehr, R. (2017). SFTA-Based Approach for Safety/Reliability Analysis of Operational Use-Cases in Cyber-Physical Systems. Journal of Computing and Information Science in Engineering, 17(3).
[24]. Chang, C. L., Wei, C. C., & Lee, Y. H. (1999). Failure mode and effects analysis using fuzzy method and grey theory. Kybernetes..
[25]. Wang, Y. M., Chin, K. S., Poon, G. K. K., & Yang, J. B. (2009). Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean. Expert systems with applications, 36(2), 1195-1207..
[26]. Chanamool, N., & Naenna, T. (2016). Fuzzy FMEA application to improve decision-making process in an emergency department. Applied Soft Computing, 43, 441-453.
[27]. Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European journal of operational research, 156(2), 445-455.
[28]. Zhu, P., Han, J., Liu, L., & Lombardi, F. (2015). A stochastic approach for the analysis of dynamic fault trees with spare gates under probabilistic common cause failures. IEEE Transactions on Reliability, 64(3), 878-892.
[29] Tian, Z. P., Wang, J. Q., & Zhang, H. Y. (2018). An integrated approach for failure mode and effects analysis based on fuzzy best-worst, relative entropy, and VIKOR methods. Applied Soft Computing, 72, 636-646.
[30] Fattahi, R., & Khalilzadeh, M. (2018). Risk evaluation using a novel hybrid method based on FMEA, extended MULTIMOORA, and AHP methods under fuzzy environment. Safety science, 102, 290-300.
[31] Deng, X., & Jiang, W. (2017). Fuzzy risk evaluation in failure mode and effects analysis using a D numbers based multi-sensor information fusion method. Sensors, 17(9), 2086.
[32] Selim, H., Yunusoglu, M. G., & Yılmaz Balaman, Ş., "A dynamic maintenance planning framework based on fuzzy TOPSIS and FMEA: application in an international food company, " Quality and Reliability Engineering International, 32(3), 2016, pp. 795-804.
[33] Barukab, O., Abdullah, S., Ashraf, S., Arif, M., & Khan, S. A. (2019). A New Approach to Fuzzy TOPSIS Method Based on Entropy Measure under Spherical Fuzzy Information. Entropy, 21(12), 1231.
[34] Mangeli, M., Shahraki, A., & Saljooghi, F. H. (2019). Improvement of risk assessment in the FMEA using nonlinear model, revised fuzzy TOPSIS, and support vector machine. International Journal of Industrial Ergonomics, 69, 209-216.
[35] Chai, K. C., Jong, C. H., Tay, K. M., & Lim, C. P. (2016). A perceptual computing-based method to prioritize failure modes in failure mode and effect analysis and its application to edible bird nest farming. Applied Soft Computing, 49, 734-747.
[36] Wang, L. E., Liu, H. C., & Quan, M. Y. (2016). Evaluating the risk of failure modes with a hybrid MCDM model under interval-valued intuitionistic fuzzy environments. Computers & Industrial Engineering, 102, 175-185.
[37] Zhao, H., You, J. X., & Liu, H. C. (2017). Failure mode and effect analysis using MULTIMOORA method with continuous weighted entropy under interval-valued intuitionistic fuzzy environment. Soft Computing, 21(18), 5355-5367.
[38] Liu, H. C., You, J. X., & Duan, C. Y. (2019). An integrated approach for failure mode and effect analysis under interval-valued intuitionistic fuzzy environment. International Journal of Production Economics, 207, 163-172.
[39] Huang, J., Li, Z. S., & Liu, H. C. (2017). New approach for failure mode and effect analysis using linguistic distribution assessments and TODIM method. Reliability Engineering & System Safety, 167, 302-309.