A New Monotone Fuzzy Rule Relabeling Framework With Application to Failure Mode and Effect Analysis Methodology

A monotone fuzzy rule relabeling (MFRR) algorithm has been introduced previously for tackling the issue of a non-monotone fuzzy rule base in the Takagi-Sugeno-Kang (TSK) Fuzzy Inference System (FIS). In this paper, we further propose a new three-stage framework to develop a computationally efficient...

Full description

Bibliographic Details
Main Authors: Lie Meng Pang, Kai Meng Tay, Chee Peng Lim, Hisao Ishibuchi
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9159567/
id doaj-27266719ae094fc581427110b3d7188c
record_format Article
spelling doaj-27266719ae094fc581427110b3d7188c2021-03-30T04:03:26ZengIEEEIEEE Access2169-35362020-01-01814490814493010.1109/ACCESS.2020.30145099159567A New Monotone Fuzzy Rule Relabeling Framework With Application to Failure Mode and Effect Analysis MethodologyLie Meng Pang0https://orcid.org/0000-0001-7037-1630Kai Meng Tay1https://orcid.org/0000-0002-0076-6167Chee Peng Lim2https://orcid.org/0000-0003-4191-9083Hisao Ishibuchi3https://orcid.org/0000-0001-9186-6472Department of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, ChinaFaculty of Engineering, Universiti Malaysia Sarawak, Kota Samarahan, MalaysiaInstitute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, VIC, AustraliaDepartment of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, ChinaA monotone fuzzy rule relabeling (MFRR) algorithm has been introduced previously for tackling the issue of a non-monotone fuzzy rule base in the Takagi-Sugeno-Kang (TSK) Fuzzy Inference System (FIS). In this paper, we further propose a new three-stage framework to develop a computationally efficient MFRR algorithm. The first stage determines the combinations of fuzzy rules to be relabeled by exploiting the prior information derived from a given non-monotone fuzzy rule base. This prior information includes the minimum number of fuzzy rules to be relabeled (denoted as $k$ ), as well as the states of fuzzy rules that must be, must not be, or may be relabeled. The second stage relabels the consequent parts of multiple sets of $k$ noisy fuzzy rules obtained from the first stage, such that a monotone fuzzy rule base is produced. The third stage selects the most suitable relabeled fuzzy rule base among the potential monotone fuzzy rule bases obtained from the second stage, either objectively or subjectively. We provide insights into MFRR and discuss its practical implementation. In addition, a network flow method is fused with the proposed MFRR framework, resulting in an efficient computation scheme. The MFRR framework is applied to Failure Mode and Effect Analysis (FMEA) problems related to a sewage treatment plant and a public hospital. It is also evaluated with real FMEA information from a semiconductor plant. The results are analyzed and discussed, which positively demonstrate the effectiveness of the proposed MFRR framework in formulating a monotone TSK-FIS model for undertaking FMEA problems.https://ieeexplore.ieee.org/document/9159567/Fuzzy inference systemsfailure mode and effect analysisgraph theorymonotonicitymonotone fuzzy rule relabelingnetwork flow method
collection DOAJ
language English
format Article
sources DOAJ
author Lie Meng Pang
Kai Meng Tay
Chee Peng Lim
Hisao Ishibuchi
spellingShingle Lie Meng Pang
Kai Meng Tay
Chee Peng Lim
Hisao Ishibuchi
A New Monotone Fuzzy Rule Relabeling Framework With Application to Failure Mode and Effect Analysis Methodology
IEEE Access
Fuzzy inference systems
failure mode and effect analysis
graph theory
monotonicity
monotone fuzzy rule relabeling
network flow method
author_facet Lie Meng Pang
Kai Meng Tay
Chee Peng Lim
Hisao Ishibuchi
author_sort Lie Meng Pang
title A New Monotone Fuzzy Rule Relabeling Framework With Application to Failure Mode and Effect Analysis Methodology
title_short A New Monotone Fuzzy Rule Relabeling Framework With Application to Failure Mode and Effect Analysis Methodology
title_full A New Monotone Fuzzy Rule Relabeling Framework With Application to Failure Mode and Effect Analysis Methodology
title_fullStr A New Monotone Fuzzy Rule Relabeling Framework With Application to Failure Mode and Effect Analysis Methodology
title_full_unstemmed A New Monotone Fuzzy Rule Relabeling Framework With Application to Failure Mode and Effect Analysis Methodology
title_sort new monotone fuzzy rule relabeling framework with application to failure mode and effect analysis methodology
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2020-01-01
description A monotone fuzzy rule relabeling (MFRR) algorithm has been introduced previously for tackling the issue of a non-monotone fuzzy rule base in the Takagi-Sugeno-Kang (TSK) Fuzzy Inference System (FIS). In this paper, we further propose a new three-stage framework to develop a computationally efficient MFRR algorithm. The first stage determines the combinations of fuzzy rules to be relabeled by exploiting the prior information derived from a given non-monotone fuzzy rule base. This prior information includes the minimum number of fuzzy rules to be relabeled (denoted as $k$ ), as well as the states of fuzzy rules that must be, must not be, or may be relabeled. The second stage relabels the consequent parts of multiple sets of $k$ noisy fuzzy rules obtained from the first stage, such that a monotone fuzzy rule base is produced. The third stage selects the most suitable relabeled fuzzy rule base among the potential monotone fuzzy rule bases obtained from the second stage, either objectively or subjectively. We provide insights into MFRR and discuss its practical implementation. In addition, a network flow method is fused with the proposed MFRR framework, resulting in an efficient computation scheme. The MFRR framework is applied to Failure Mode and Effect Analysis (FMEA) problems related to a sewage treatment plant and a public hospital. It is also evaluated with real FMEA information from a semiconductor plant. The results are analyzed and discussed, which positively demonstrate the effectiveness of the proposed MFRR framework in formulating a monotone TSK-FIS model for undertaking FMEA problems.
topic Fuzzy inference systems
failure mode and effect analysis
graph theory
monotonicity
monotone fuzzy rule relabeling
network flow method
url https://ieeexplore.ieee.org/document/9159567/
work_keys_str_mv AT liemengpang anewmonotonefuzzyrulerelabelingframeworkwithapplicationtofailuremodeandeffectanalysismethodology
AT kaimengtay anewmonotonefuzzyrulerelabelingframeworkwithapplicationtofailuremodeandeffectanalysismethodology
AT cheepenglim anewmonotonefuzzyrulerelabelingframeworkwithapplicationtofailuremodeandeffectanalysismethodology
AT hisaoishibuchi anewmonotonefuzzyrulerelabelingframeworkwithapplicationtofailuremodeandeffectanalysismethodology
AT liemengpang newmonotonefuzzyrulerelabelingframeworkwithapplicationtofailuremodeandeffectanalysismethodology
AT kaimengtay newmonotonefuzzyrulerelabelingframeworkwithapplicationtofailuremodeandeffectanalysismethodology
AT cheepenglim newmonotonefuzzyrulerelabelingframeworkwithapplicationtofailuremodeandeffectanalysismethodology
AT hisaoishibuchi newmonotonefuzzyrulerelabelingframeworkwithapplicationtofailuremodeandeffectanalysismethodology
_version_ 1724182384668049408