Parallel fault diagnosis using hierarchical fuzzy Petri net by reversible and dynamic decomposition mechanism

The state space explosion, a challenge analogous to that encountered in a Petri net (PN), 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...

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Bibliographic Details
Published in:Frontiers of information technology & electronic engineering Vol. 26; no. 1; pp. 93 - 108
Main Authors: Sarkheyli-Hägele, Arezoo, Yusoff, Yusliza, Zain, Azlan Mohd
Format: Journal Article
Language:English
Published: Hangzhou Zhejiang University Press 01.01.2025
Springer Nature B.V
Subjects:
Algorithms
Bidirectional reasoning
Case reports
Communications Engineering
Computer Hardware
Computer Science
Computer Systems Organization and Communication Networks
Decomposition
Electrical Engineering
Electronics and Microelectronics
Euclidean geometry
Fault diagnosis
Fuzzy logic
Fuzzy Petri net (FPN)
Inference
Instrumentation
Networks
Parallel
Petri nets
Reasoning
Research Article
State explosion
TP301
State explosion
Bidirectional reasoning
平行
状态爆炸
分解
Fuzzy Petri net (FPN)
Decomposition
Parallel
双向推理
TP301
模糊Petri网(FPN)
ISSN:2095-9184, 2095-9230
Online Access:Get full text
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Summary:The state space explosion, a challenge analogous to that encountered in a Petri net (PN), 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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ISSN:2095-9184
2095-9230
DOI:10.1631/FITEE.2400184