Failure Mode and Effect Analysis using Soft Set Theory and COPRAS Method

Failure mode and effect analysis (FMEA) is a risk management technique frequently applied to enhance the system performance and safety. In recent years, many researchers have shown an intense interest in improving FMEA due to inherent weaknesses associated with the classical risk priority number (RPN) method. In this study, we develop a new risk ranking model for FMEA based on soft set theory and COPRAS method, which can deal with the limitations and enhance the performance of the conventional FMEA. First, trapezoidal fuzzy soft set is adopted to manage FMEA team members’ linguistic assessments on failure modes. Then, a modified COPRAS method is utilized for determining the ranking order of the failure modes recognized in FMEA. Especially, we treat the risk factors as interdependent and employ the Choquet integral to obtain the aggregate risk of failures in the new FMEA approach. Finally, a practical FMEA problem is analyzed via the proposed approach to demonstrate its applicability and effectiveness. The result shows that the FMEA model developed in this study outperforms the traditional RPN method and provides a more reasonable risk assessment of failure modes.

[1]  Jin Wang,et al.  Modified failure mode and effects analysis using approximate reasoning , 2003, Reliab. Eng. Syst. Saf..

[2]  Hu-Chen Liu,et al.  Failure mode and effects analysis using D numbers and grey relational projection method , 2014, Expert Syst. Appl..

[3]  Hui Li,et al.  Risk assessment in system FMEA combining fuzzy weighted average with fuzzy decision-making trial and evaluation laboratory , 2015, Int. J. Comput. Integr. Manuf..

[4]  Goutam Kumar Bose,et al.  Multi criteria FMECA for coal-fired thermal power plants using COPRAS-G , 2014 .

[5]  Hossein Safari,et al.  Identifying and evaluating enterprise architecture risks using FMEA and fuzzy VIKOR , 2014, Journal of Intelligent Manufacturing.

[6]  Hu-Chen Liu,et al.  Fuzzy Failure Mode and Effects Analysis Using Fuzzy Evidential Reasoning and Belief Rule-Based Methodology , 2013, IEEE Transactions on Reliability.

[7]  Hu-Chen Liu,et al.  Improving risk evaluation in FMEA with a hybrid multiple criteria decision making method , 2015 .

[8]  Alireza Sotoudeh-Anvari,et al.  A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems , 2017 .

[9]  Hu-Chen Liu,et al.  Failure Mode and Effect Analysis Under Uncertainty: An Integrated Multiple Criteria Decision Making Approach , 2016, IEEE Transactions on Reliability.

[10]  Jian-Bo Yang,et al.  Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean , 2009, Expert Syst. Appl..

[11]  S. Meysam Mousavi,et al.  Soft computing-based new interval-valued hesitant fuzzy multi-criteria group assessment method with last aggregation to industrial decision problems , 2017, Soft Comput..

[12]  Xin Guo Ming,et al.  A rough TOPSIS Approach for Failure Mode and Effects Analysis in Uncertain Environments , 2014, Qual. Reliab. Eng. Int..

[13]  Jian-Bo Yang,et al.  Failure mode and effects analysis by data envelopment analysis , 2009, Decis. Support Syst..

[14]  M. Grabisch.  Fuzzy integral in multicriteria decision making , 1995 .

[15]  Metin Celik,et al.  Application of failure modes and effects analysis to main engine crankcase explosion failure on-board ship , 2013 .

[16]  Jian-Xin You,et al.  Failure mode and effects analysis using intuitionistic fuzzy hybrid TOPSIS approach , 2015, Soft Comput..

[17]  Marcello Braglia,et al.  MAFMA: multi‐attribute failure mode analysis , 2000 .

[18]  Ping Chow Teoh,et al.  An evaluation of failure modes and effects analysis generation method for conceptual design , 2005, Int. J. Comput. Integr. Manuf..

[19]  Hu-Chen Liu,et al.  An integrated failure mode and effect analysis approach for accurate risk assessment under uncertainty , 2016 .

[20]  Hu-Chen Liu,et al.  Evaluating the risk of failure modes with extended MULTIMOORA method under fuzzy environment , 2014, Eng. Appl. Artif. Intell..

[21]  Prasenjit Chatterjee,et al.  Integrated QFD-MCDM framework for green supplier selection , 2017 .

[22]  Nan Liu,et al.  Risk evaluation approaches in failure mode and effects analysis: A literature review , 2013, Expert Syst. Appl..

[23]  Hu-Chen Liu,et al.  Evaluating the risk of failure modes with a hybrid MCDM model under interval-valued intuitionistic fuzzy environments , 2016, Comput. Ind. Eng..

[24]  Zeshui Xu,et al.  An overview of methods for determining OWA weights , 2005, Int. J. Intell. Syst..

[25]  Sanjay Kumar Chaturvedi,et al.  Criticality Assessment Models for Failure Mode Effects and Criticality Analysis Using Fuzzy Logic , 2011, IEEE Transactions on Reliability.

[26]  Zhi Xiao,et al.  The trapezoidal fuzzy soft set and its application in MCDM , 2012 .

[27]  Theodoro A. Netto,et al.  Prioritization of failure risk in subsea flexible pipes via data envelopment analysis , 2013 .

[28]  Dong‐Shang Chang,et al.  Applying DEA to enhance assessment capability of FMEA , 2009 .

[29]  Mesut Kumru,et al.  Fuzzy FMEA application to improve purchasing process in a public hospital , 2013, Appl. Soft Comput..

[30]  Nan Liu,et al.  Risk evaluation in failure mode and effects analysis with extended VIKOR method under fuzzy environment , 2012, Expert Syst. Appl..

[31]  Hu-Chen Liu,et al.  A novel approach for failure mode and effects analysis using combination weighting and fuzzy VIKOR method , 2015, Appl. Soft Comput..

[32]  Michio Sugeno,et al.  A Model of Human Evaluation Process Using Fuzzy Measure , 1985, Int. J. Man Mach. Stud..

[33]  H. Schneider.  Failure mode and effect analysis : FMEA from theory to execution , 1996 .

[34]  Hu-Chen Liu,et al.  A Novel Approach for FMEA: Combination of Interval 2‐Tuple Linguistic Variables and Gray Relational Analysis , 2015, Qual. Reliab. Eng. Int..

[35]  Chee Peng Lim,et al.  Application of the fuzzy Failure Mode and Effect Analysis methodology to edible bird nest processing , 2013 .

[36]  Xinguo Ming,et al.  Failure modes and effects analysis using integrated weight-based fuzzy TOPSIS , 2013, Int. J. Comput. Integr. Manuf..

[37]  Jose L. Salmeron,et al.  Fuzzy Grey Cognitive Maps in reliability engineering , 2012, Appl. Soft Comput..

[38]  Chee Peng Lim,et al.  A clustering-based failure mode and effect analysis model and its application to the edible bird nest industry , 2015, Neural Computing and Applications.

[39]  Edmundas Kazimieras Zavadskas,et al.  Evaluating construction projects of hotels based on environmental sustainability with MCDM framework , 2017 .

[40]  Valentinas Podvezko,et al.  Sustainable Assessment of Aerosol Pollution Decrease Applying Multiple Attribute Decision-Making Methods , 2016 .

[41]  菅野 道夫,et al.  Theory of fuzzy integrals and its applications , 1975 .

[42]  Chee Peng Lim,et al.  Clustering and visualization of failure modes using an evolving tree , 2015, Expert Syst. Appl..

[43]  J. B. Bowles,et al.  Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis , 1995 .

[44]  Hu-Chen Liu,et al.  Human reliability assessment for medical devices based on failure mode and effects analysis and fuzzy linguistic theory , 2014 .

[45]  D. Molodtsov.  Soft set theory—First results , 1999 .

[46]  Edmundas Kazimieras Zavadskas,et al.  State of art surveys of overviews on MCDM/MADM methods , 2014 .

[47]  Chunhe Xie,et al.  A modified method for risk evaluation in failure modes and effects analysis of aircraft turbine rotor blades , 2016 .