A fuzzy TOPSIS and Rough Set based approach for mechanism analysis of product infant failure

Root causes identification of product infant failure is nowadays one of the critical topics in product quality improvements. This paper puts forward a novel technical approach for mechanism analysis of product infant failure based on domain mapping in Axiomatic Design and the quality and reliability data from product lifecycle in the form of relational tree. The proposed method could intelligently decompose the early fault symptoms into root causes of critical functional parameters in function domain, design parameters in physical domain and process parameters in process domain successively. More specifically, both qualitative and quantitative attributes of quality and reliability types are considered for solving the root causes weight computation problem of product infant failure, this approach emphasizes the integrated application of artificial intelligence techniques of Rough Set and fuzzy TOPSIS to compute the weight of root causes. In order to enumerate the latent root causes of product infant failure, connotation of product infant failure based on the product reliability evolution model in the life cycle and data integration model of quality and reliability in production based on the extended QR chain are presented firstly. Then, a decomposition method for relational tree of product infant failure is studied based on domains of functional, physical and process in Axiomatic Design. The failure relation weight computation of root causes (nodes of relational tree) is considered as multi-criteria decision making problem (MCDM) by integrated application of Rough Set and fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), which the Rough Set is used to mining the quality data and fuzzy TOPSIS is adopted to model the computation process of failure relation weight. Finally, the validity of the proposed method is verified by a case study of analyzing a car infant failure about body noise vibration harshness complaint, and the result proves that the proposed approach is conducive to improve the intelligent level of root causes identification for complex product infant failure.

[1]  Francisco Herrera,et al.  Implementing algorithms of rough set theory and fuzzy rough set theory in the R package "RoughSets" , 2014, Inf. Sci..

[2]  H. Kunzi,et al.  Lectu re Notes in Economics and Mathematical Systems , 1975 .

[3]  Jionghua Jin,et al.  Quality and reliability information integration for design evaluation of fixture system reliability , 2001 .

[4]  R. K. Singh,et al.  A fuzzy TOPSIS based approach for e-sourcing , 2011, Eng. Appl. Artif. Intell..

[5]  Douglas C. Montgomery.  Big Data and the Quality Profession , 2014, Qual. Reliab. Eng. Int..

[6]  Tao Xie,et al.  Reliability Engineering , 2017, IEEE Softw..

[7]  P. O'Connor,et al.  Practical Reliability Engineering , 1981 .

[8]  S. K. Bhaumik.  Root cause analysis in engineering failures , 2010 .

[9]  Amit Kumar,et al.  A novel general approach to evaluating the reliability of gas turbine system , 2014, Eng. Appl. Artif. Intell..

[10]  Mario Cantú-Sifuentes,et al.  Fuzzy reliability analysis with only censored data , 2014, Eng. Appl. Artif. Intell..

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

[12]  Amin Hammad,et al.  Knowledge-assisted BIM-based visual analytics for failure root cause detection in facilities management , 2014 .

[13]  Tomasz Wachowicz,et al.  Application of fuzzy TOPSIS to scoring the negotiation offers in ill-structured negotiation problems , 2015, Eur. J. Oper. Res..

[14]  Fang Liu,et al.  TOPSIS-Based Consensus Model for Group Decision-Making With Incomplete Interval Fuzzy Preference Relations , 2014, IEEE Transactions on Cybernetics.

[15]  David L. Ransom.  A Practical Guideline For A Successful Root Cause Failure Analysis. , 2007 .

[16]  Zhang Wu,et al.  Fuzzy theory applied in quality management of distributed manufacturing system: A literature review and classification , 2011, Eng. Appl. Artif. Intell..

[17]  Joseph Bernstein.  Reliability Prediction from Burn-In Data Fit to Reliability Models , 2014 .

[18]  Pra Murthy.  New research in reliability, warranty and maintenance , 2010 .

[19]  Mahmood Shafiee,et al.  A fuzzy analytic network process model to mitigate the risks associated with offshore wind farms , 2015, Expert Syst. Appl..

[20]  Hamid Reza Karimi,et al.  Observer-Based Robust Control for Hydraulic Velocity Control System , 2013 .

[21]  Chin-Diew Lai,et al.  Modelling N- and W-shaped hazard rate functions without mixing distributions , 2009 .

[22]  Kyungmee O. Kim.  Effects of manufacturing defects on the device failure rate , 2013 .

[23]  M.J. Zuo,et al.  On the relationship of semiconductor yield and reliability , 2005, IEEE Transactions on Semiconductor Manufacturing.

[24]  Ting-Yu Chen,et al.  The inclusion-based TOPSIS method with interval-valued intuitionistic fuzzy sets for multiple criteria group decision making , 2015, Appl. Soft Comput..

[25]  J. Hendler,et al.  Amplify scientific discovery with artificial intelligence , 2014, Science.

[26]  Way Kuo,et al.  An overview of manufacturing yield and reliability modeling for semiconductor products , 1999, Proc. IEEE.

[27]  Torbjorn Thiringer,et al.  Field-Experience Based Root-Cause Analysis of Power-Converter Failure in Wind Turbines , 2015, IEEE Transactions on Power Electronics.

[28]  Inci Batmaz,et al.  A review of data mining applications for quality improvement in manufacturing industry , 2011, Expert Syst. Appl..

[29]  Jonathan Levin,et al.  Economics in the age of big data , 2014, Science.

[30]  Fredrik Engelhardt,et al.  Improving Systems by Combining Axiomatic Design, Quality Control Tools and Designed Experiments , 2000 .

[31]  Jürgen Pilz,et al.  Advanced Bayesian Estimation of Weibull Early Life Failure Distributions , 2014, Qual. Reliab. Eng. Int..

[32]  D. N. Prabhakar Murthy,et al.  Impact of quality variations on product reliability , 2009, Reliab. Eng. Syst. Saf..

[33]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[34]  Pierluigi Siano,et al.  Failure Identification in Smart Grids Based on Petri Net Modeling , 2011, IEEE Transactions on Industrial Electronics.

[35]  Yili Hong,et al.  Reliability Meets Big Data: Opportunities and Challenges , 2014 .

[36]  Jianjun Shi.  Data Fusion for In-Process Quality Improvement , 2013 .

[37]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[38]  Kensuke Tsuchiya,et al.  Three typical failure scenarios of the mind process of design from the Axiomatic Design perspective , 2009 .

[39]  William J. Roesch.  Using a new bathtub curve to correlate quality and reliability , 2012, Microelectron. Reliab..

[40]  Hisao Kawasaki,et al.  Statistical analysis of early failures in electromigration , 2001 .

[41]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[42]  Willi Hock,et al.  Lecture Notes in Economics and Mathematical Systems , 1981 .

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

[44]  Changyong Liang,et al.  An intuitionsitic fuzzy judgement matrix and TOPSIS integrated multi-criteria decision making method for green supplier selection , 2015, J. Intell. Fuzzy Syst..

[45]  Lyès Benyoucef,et al.  Simulation based fuzzy TOPSIS approach for group multi-criteria supplier selection problem , 2012, Eng. Appl. Artif. Intell..

[46]  Qing-Hui Wang,et al.  A rough set-based fault ranking prototype system for fault diagnosis , 2004, Eng. Appl. Artif. Intell..

[47]  Jafar Razmi,et al.  Employing fuzzy TOPSIS and SWOT for supplier selection and order allocation problem , 2015 .

[48]  Y J Wang,et al.  FUZZY TOPSIS FOR MULTI-CRITERIA DECISION MAKING , 2003 .

[49]  Chung-Ho Su,et al.  Rough Set Theory Based Fuzzy TOPSIS on Serious Game Design Evaluation Framework , 2013 .

[50]  Emel Kizilkaya Aydogan,et al.  Performance measurement model for Turkish aviation firms using the rough-AHP and TOPSIS methods under fuzzy environment , 2011, Expert Syst. Appl..

[51]  T. Warren Liao,et al.  An investigation of a hybrid CBR method for failure mechanisms identification , 2004, Eng. Appl. Artif. Intell..