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Parallel fault diagnosis using hierarchical fuzzy Petri net by reversible and dynamic decomposition mechanism
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Yinhong XIANG, Kaiqing ZHOU, Arezoo SARKHEYLI-HÄGELE, Yusliza YUSOFF, Diwen KANG, Azlan Mohd ZAIN. Parallel fault diagnosis using hierarchical fuzzy Petri net by reversible and dynamic decomposition mechanism[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .
@article{title="Parallel fault diagnosis using hierarchical fuzzy Petri net by reversible and dynamic decomposition mechanism",
author="Yinhong XIANG, Kaiqing ZHOU, Arezoo SARKHEYLI-HÄGELE, Yusliza YUSOFF, Diwen KANG, Azlan Mohd ZAIN",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2400184"
}
%0 Journal Article
%T Parallel fault diagnosis using hierarchical fuzzy Petri net by reversible and dynamic decomposition mechanism
%A Yinhong XIANG
%A Kaiqing ZHOU
%A Arezoo SARKHEYLI-HÄ
%A GELE
%A Yusliza YUSOFF
%A Diwen KANG
%A Azlan Mohd ZAIN
%J Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2400184
TY - JOUR
T1 - Parallel fault diagnosis using hierarchical fuzzy Petri net by reversible and dynamic decomposition mechanism
A1 - Yinhong XIANG
A1 - Kaiqing ZHOU
A1 - Arezoo SARKHEYLI-HÄ
A1 - GELE
A1 - Yusliza YUSOFF
A1 - Diwen KANG
A1 - Azlan Mohd ZAIN
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
SP -
EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2400184
Abstract: The state space explosion, a challenge analogous to that encountered in a Petri net, has constrained the extensive study of fuzzy Petri nets (FPNs). Current reasoning algorithms employing FPNs, which operate through forward, backward, and bidirectional mechanisms, are examined. These algorithms streamline the inference process by eliminating irrelevant components of the FPN. However, as the scale of the FPN grows, the complexity of these algorithms escalates sharply, posing a significant challenge for practical applications. To address the state explosion issue, this work introduces a parallel bidirectional reasoning algorithm for an FPN that utilizes reverse and decomposition strategies to optimize the implementation process. The algorithm involves hierarchically dividing a large-scale FPN into two sub-FPNs, followed by a converse operation to generate the reversal sub-FPN for the right sub-FPN. The detailed mapping between the original and reversed FPNs is thoroughly discussed. parallel reasoning operations are then conducted on the left-sub-FPN and the resulting reversal right-sub-FPN, with the final result derived by computing the Euclidean distance between the outcomes from the output places of the two sub-FPNs. A case study is presented to illustrate the implementation process, demonstrating the algorithm’s significant enhancement of inference efficiency and substantial reduction in execution time.
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