An integrated fuzzy-genetic failure mode and effect analysis for aircraft wing reliability

The purpose of this paper is to propose a model for assessing the hazard system based on the failure structure of aircraft wing. The model considers reliability maintenance and repair factors. Failure Mode and Effect Analysis (FMEA) is one of the well-known methods of quality management being used for continuous improvement in product or process plans. One serious issue of FMEA is the definition of the risk priorities of failure modes. This paper proposes an analytical method based on failure modes and failure analysis in criticality and suggests a fuzzy evaluation method for evaluating the risk level of the wing of aircraft. The analytical technique includes qualitatively analyzing the operation, failure modes, failure cause, failure rate, and severity through FMEA and quantitatively assessing the safety by using the fuzzy evaluation method. Fuzzy logic and Genetic Algorithms are integrated using a risk-cost model based on FMEA and comparisons with Simulated Annealing algorithms.

[1]  R. Gnanadass,et al.  Optimization of process parameters through fuzzy logic and genetic algorithm - A case study in a process industry , 2015, Appl. Soft Comput..

[2]  Bryony Dean Franklin,et al.  Failure mode and effects analysis: too little for too much? , 2012, BMJ quality & safety.

[3]  Nadia Belu,et al.  Application of Fuzzy Logic with Genetic Algorithms to FMEA Method , 2014 .

[4]  Bhabani Shankar Prasad Mishra,et al.  Techniques and Environments for Big Data Analysis: Parallel, Cloud, and Grid Computing , 2016 .

[5]  Charles E Ebeling,et al.  An Introduction to Reliability and Maintainability Engineering , 1996 .

[6]  Chee Peng Lim,et al.  An Analytical Interval Fuzzy Inference System for Risk Evaluation and Prioritization in Failure Mode and Effect Analysis , 2017, IEEE Systems Journal.

[7]  Lacey Colligan,et al.  Assessing the validity of prospective hazard analysis methods: a comparison of two techniques , 2014, BMC Health Services Research.

[8]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[9]  V. Cerný.  Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm , 1985 .

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

[11]  Chee Peng Lim,et al.  A New Two-Stage Fuzzy Inference System-Based Approach to Prioritize Failures in Failure Mode and Effect Analysis , 2015, IEEE Transactions on Reliability.

[12]  S. Vijayarani,et al.  An Efficient Algorithm for Facial Image Classification , 2015 .

[13]  Bhabani Shankar Prasad Mishra,et al.  Techniques and Environments for Big Data Analysis , 2016 .

[14]  L. Zadeh.  Fuzzy sets as a basis for a theory of possibility , 1999 .

[15]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[16]  Terje Aven,et al.  A framework for reliability and risk centered maintenance , 2011, Reliab. Eng. Syst. Saf..

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

[18]  Warren Gilchrist,et al.  Modelling Failure Modes and Effects Analysis , 1993 .

[19]  M. K. Dyer,et al.  Applicability of NASA contract quality management and failure mode effect analysis procedures to the USGS Outer Continental Shelf oil and gas lease management program , 1972 .