A Bidirectional Diagnosis Algorithm of Fuzzy Petri Net Using Inner-Reasoning-Path

Abstract

Fuzzy Petri net (FPN) is a powerful tool to execute the fault diagnosis function for various industrial applications. One of the most popular approaches for fault diagnosis is to calculate the corresponding algebra forms which record flow information and three parameters of value of all places and transitions of the FPN model. However, with the rapid growth of the complexity of the real system, the scale of the corresponding FPN is also increased sharply. It indicates that the complexity of the fault diagnosis algorithm is also raised due to the increased scale of vectors and matrix. Focusing on this situation, a bidirectional adaptive fault diagnosis algorithm is presented in this article to reduce the complexity of the fault diagnosis process via removing irrelevant places and transitions of the large-scale FPN, followed by the correctness and algorithm complexity of the proposed approach that are also discussed in detail. A practical example is utilized to show the feasibility and efficacy of the proposed method. The results of the experiments illustrated that the proposed algorithm owns the ability to simplify the inference process and to reduce the algorithm complexity due to the removal of unnecessary places and transitions in the reasoning path of the appointed output place.

A Fault Analysis Method for Three‐Phase Induction Motors Based on Spiking Neural P Systems

“…In this section, the usefulness of the proposed method is justified by comparison with different approaches: the method of selection of amplitudes of frequencies (MSAF-12) [2], improved artificial ant clustering (IAAC) [14], fuzzy fault Petri net (FFPN) [23], and fuzzy Petri net (FPN) [44] for the abductive fault diagnosis.…”

Motor Fault Diagnosis Algorithm Based on Wavelet and Attention Mechanism

“…In reference [11], a colored Petri net model is established by using colored Petri nets and modelling tools cpntools, which improves the efficiency of fault diagnosis and the intuitiveness of the model. In reference [12], Petri net and fuzzy reasoning are combined to solve some stochastic and uncertain fault problems effectively. It greatly improves the ability to locate the fault source in fault diagnosis.…”

Picture Fuzzy Petri Nets for Knowledge Representation and Acquisition in Considering Conflicting Opinions

“…The combination of the graphical power of Petri nets and ability of fuzzy sets to express vague information makes FPNs suitable for modeling uncertain rule-based expert systems [3,4]. An FPN is a marked graphical system containing places and transitions [5]. Due to the capacity to depict imprecise knowledge and support inference processes, FPNs have garnered an increasing interest in both academics and practitioners and have been used in a lot of fields, such as fault diagnosis [6,7], adaptive software systems modeling [8], reliability optimization design [9], genetic regulatory network design [10], and DNA sequencing prediction [11,12].…”