Prioritizing failure risks of components based on information axiom for product redesign considering fuzzy and random uncertainties

The prioritization of the failure risks of the components in an existing product is critical for product redesign decision-making considering various uncertainties. Two issues need to be addressed in the failure risk prioritization process. One is the evaluation of the failure risk considering each failure mode for each component. Currently, many failure mode effects and analysis (FMEA) methods based on fuzzy logic seldom deal with the randomness in failure mode occurrence during the product operation stage. Therefore, in this research, the information axiom is extended to calculate the information contents of risk indices considering these two types of uncertainty. The second issue is the evaluation of the degree of failure risk for each of the components. The weighted sum of information content considering all failure modes is used to assess the risk of components based on a fuzzy logarithmic least squares method (FLLSM). Additionally, a case study to prioritize the failure risks of various components in a crawler crane is implemented to demonstrate the effectiveness of the developed approach.

[1]  Gordon H. Huang,et al.  A hybrid fuzzy-stochastic technique for planning peak electricity management under multiple uncertainties , 2017, Eng. Appl. Artif. Intell..

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

[3]  Qi Wang,et al.  Managing engineering change requirements during the product development process , 2017, Concurr. Eng. Res. Appl..

[4]  Tsu-Ming Yeh,et al.  Fuzzy-based risk priority number in FMEA for semiconductor wafer processes , 2014 .

[5]  Shuchi Chawla,et al.  The power of randomness in bayesian optimal mechanism design , 2010, EC '10.

[6]  Nam P. Suh,et al.  Axiomatic Design: Advances and Applications , 2001 .

[7]  Frédéric Vanderhaegen,et al.  Using the BCD model for risk analysis: An influence diagram based approach , 2013, Eng. Appl. Artif. Intell..

[8]  Jose Eduardo Munive-Hernandez,et al.  Using EWGM method to optimise the FMEA as a risk assessment methodology , 2019, Concurr. Eng. Res. Appl..

[9]  Stefanka Chukova,et al.  Modeling uncertainty in periodic random environment: applications to environmental studies , 1999 .

[10]  Yupeng Li,et al.  An Information Axiom based decision making approach under hybrid uncertain environments , 2015, Inf. Sci..

[11]  Ping Li,et al.  Failure mode and effects analysis using intuitionistic fuzzy hybrid weighted Euclidean distance operator , 2014, Int. J. Syst. Sci..

[12]  Lotfi A. Zadeh,et al.  Please Scroll down for Article International Journal of General Systems Fuzzy Sets and Systems* Fuzzy Sets and Systems* , 2022 .

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

[14]  Shuchi Chawla,et al.  The power of randomness in Bayesian optimal mechanism design , 2015, Games Econ. Behav..

[15]  Haibin Li,et al.  Structural reliability analysis with fuzzy random variables using error principle , 2018, Eng. Appl. Artif. Intell..

[16]  Lars Hvam,et al.  Including product features in process redesign , 2017, Concurr. Eng. Res. Appl..

[17]  Ching-Hsue Cheng,et al.  Evaluating the risk of failure using the fuzzy OWA and DEMATEL method , 2011, J. Intell. Manuf..

[18]  O. P. Gandhi,et al.  Failure cause analysis of machine tools using digraph and matrix methods , 2002 .

[19]  Cengiz Kahraman,et al.  A novel trapezoidal intuitionistic fuzzy information axiom approach: An application to multicriteria landfill site selection , 2018, Eng. Appl. Artif. Intell..

[20]  Cengiz Kahraman,et al.  Structuring ship design project approval mechanism towards installation of operator-system interfaces via fuzzy axiomatic design principles , 2010, Inf. Sci..

[21]  Hu-Chen Liu,et al.  Improving Risk Evaluation in FMEA With Cloud Model and Hierarchical TOPSIS Method , 2019, IEEE Transactions on Fuzzy Systems.

[22]  LiYupeng,et al.  An Information Axiom based decision making approach under hybrid uncertain environments , 2015 .

[23]  Nam P. Suh Designing-in of quality through axiomatic design , 1995 .

[24]  Zhongsheng Hua,et al.  A modified fuzzy logarithmic least squares method for fuzzy analytic hierarchy process , 2006, Fuzzy Sets Syst..

[25]  Yupeng Li,et al.  PSS solution evaluation considering sustainability under hybrid uncertain environments , 2015, Expert Syst. Appl..

[26]  Yupeng Li,et al.  An integrated module partition approach for complex products and systems based on weighted complex networks , 2014 .

[27]  Hossein Sayyadi Tooranloo,et al.  Evaluating knowledge management failure factors using intuitionistic fuzzy FMEA approach , 2018, Knowledge and Information Systems.

[28]  Chiu-Chi Wei,et al.  A new fuzzy decision-making approach for selecting new product development project , 2016, Concurr. Eng. Res. Appl..

[29]  Yao-Tsung Ko,et al.  Optimizing product architecture for complex design , 2013, Concurr. Eng. Res. Appl..

[30]  C. Kahraman,et al.  Multi-attribute comparison of advanced manufacturing systems using fuzzy vs. crisp axiomatic design approach , 2005 .

[31]  Bernd Möller,et al.  Fuzzy randomness – a contribution to imprecise probability , 2004 .

[32]  Deyi Xue,et al.  Identification of to-be-improved components for redesign of complex products and systems based on fuzzy QFD and FMEA , 2019, J. Intell. Manuf..

[33]  Matteo Giacomo Maria Kalchschmidt,et al.  Product and process modularity: improving flexibility and reducing supplier failure risk , 2013 .