On the use of fuzzy inference techniques in assessment models: part II: industrial applications

Authors:
Kai Meng Tay;Chee Peng Lim
Affiliations:
Electronic Engineering Department, Faculty of Engineering, University Malaysia Sarawak, Kota Samarahan, Malaysia;School of Electrical and Electronic Engineering, University of Science Malaysia, Minden, Malaysia
Venue:
Fuzzy Optimization and Decision Making
Year:
2008

Citing 6
Cited 4

Practical neural network recipes in C++

Practical neural network recipes in C++
Advanced algorithms for neural networks: a C++ sourcebook

Advanced algorithms for neural networks: a C++ sourcebook
Using fuzzy cognitive maps as a system model for failure modes and effects analysis

Information Sciences: an International Journal
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems

Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
An approach to fuzzy default reasoning for function approximation

Soft Computing - A Fusion of Foundations, Methodologies and Applications
Fuzzy approximation via grid point sampling and singular value decomposition

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

A fuzzy inference system-based criterion-referenced assessment model

Expert Systems with Applications: An International Journal
SIRMs connected fuzzy inference method adopting emphasis and suppression

Fuzzy Sets and Systems
Application of the fuzzy Failure Mode and Effect Analysis methodology to edible bird nest processing

Computers and Electronics in Agriculture
A non-linear programming-based similarity reasoning scheme for modelling of monotonicity-preserving multi-input fuzzy inference systems

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Hybrid approaches for approximate reasoning

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, we study the applicability of the monotone output property and the output resolution property in fuzzy assessment models to two industrial Failure Mode and Effect Analysis (FMEA) problems. First, the effectiveness of the monotone output property in a single-input fuzzy assessment model is demonstrated with a proposed fuzzy occurrence model. Then, the usefulness of the two properties to a multi-input fuzzy assessment model, i.e., the Bowles fuzzy Risk Priority Number (RPN) model, is assessed. The experimental results indicate that both the fuzzy occurrence model and Bowles fuzzy RPN model are able to fulfill the monotone output property, with the derived conditions (in Part I) satisfied. In addition, the proposed rule refinement technique is able to improve the output resolution property of the Bowles fuzzy RPN model.