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...
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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/ |
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