Fuzzy Reasoning Numerical Spiking Neural P Systems for Induction Motor Fault Diagnosis

Yin, Xiu; Liu, Xiyu; Sun, Minghe; Dong, Jianping; Zhang, Gexiang

doi:10.3390/e24101385

Cited by 10 publications

(3 citation statements)

References 38 publications

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“…Spikes and anti-spikes cannot coexist in a neuron. Fuzzy SN P systems use fuzzy logic and reasoning in spiking rules [25]. Adopting request-response methods from computers in communication, SN P systems with communication on request [26] and with request rules [27] were researched.…”

Section: E a A D mentioning

confidence: 99%

On the Universality of Spiking Neural P Systems With Multiple Channels and Autapses

Ning,

Yang,

Sun

et al. 2024

IEEE Access

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Spiking neural P systems (SN P systems) abstract the structure and function of neurons and nervous systems. By adopting some biological observations or mathematical considerations in SN P systems, many variants have been proposed. In this work, through combining two interesting variants, SN P systems with multiple channels (SNP-MC systems) and SN P systems with autapses (SNP-AU systems), we propose SN P systems with multiple channels and autapses (SNP-MCA systems) and research their universality. SNP-MCA systems are proved to be universal in the generation of numbers. For computing functions, a universal SNP-MCA system with 25 neurons is constructed, which requires fewer neurons than the two variants.

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Section: E a A D mentioning

confidence: 99%

On the Universality of Spiking Neural P Systems With Multiple Channels and Autapses

Ning,

Yang,

Sun

et al. 2024

IEEE Access

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“…For instance, in [39], faulty bearings, different levels of short circuits in the stator coils, and different levels of BRB are detected using a multi-agent system based on AI. In other work, for detecting winding insulation burn, bearing damage, and BRB, the authors in [40] proposed using spiking neural networks to analyze the current signals of the IM.…”

Section: Currentmentioning

confidence: 99%

Physical Variable Measurement Techniques for Fault Detection in Electric Motors

et al. 2023

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Induction motors are widely used worldwide for domestic and industrial applications. Fault detection and classification techniques based on signal analysis have increased in popularity due to the growing use of induction motors in new technologies such as electric vehicles, automatic control, maintenance systems, and the inclusion of renewable energy sources in electrical systems, among others. Hence, monitoring, fault detection, and classification are topics of interest for researchers, given that the presence of a fault can lead to catastrophic consequences concerning technical and financial aspects. To detect a fault in an induction motor, several techniques based on different physical variables, such as vibrations, current signals, stray flux, and thermographic images, have been studied. This paper reviews recent investigations into physical variables, instruments, and techniques used in the analysis of faults in induction motors, aiming to provide an overview on the pros and cons of using a certain type of physical variable for fault detection. A discussion about the detection accuracy and complexity of the signals analysis is presented, comparing the results reported in recent years. This work finds that current and vibration are the most popular signals employed to detect faults in induction motors. However, stray flux signal analysis is presented as a promising alternative to detect faults under certain operating conditions where other methods, such as current analysis, may fail.

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“…In most cases, for preference modelling, the complete linguistic term set is elicited through symmetrical triangular membership functions uniformly distributed in the universe of discourse [18][19][20][21]. Two main reasons are usually suggested for this wide-spread use: firstly, the simplest form that one can think of for modeling the graduality is the linear transition between the support and the core of the membership function; secondly, the problems dealing with vague predicates are less concerned with precision, and they are more of a qualitative type and are thus generally written as linearly as possible.…”

Section: Introductionmentioning

confidence: 99%

On the Term Set’s Semantics for Pairwise Comparisons in Fuzzy Linguistic Preference Models

Nieto-Morote

Ruz-Vila

2023

Entropy

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The main objective of this paper is the definition of a membership function assignment procedure based on inherent features of linguistic terms to determine their semantics when they are used for preference modelling. For this purpose, we consider what linguists say about concepts such as language complementarity, the influence of context, or the effects of the use of hedges (modifiers) on adverbs meaning. As a result, specificity, entropy and position in the universe of discourse of the functions assigned to each linguistic term are mainly determined by the intrinsic meaning of the hedges concerned. We uphold that the meaning of weakening hedges is linguistically non-inclusive because their semantics are subordinated to the proximity to the indifference meaning, whereas reinforcement hedges are linguistically inclusive. Consequently, the membership function assignment rules are different: fuzzy relational calculus and the horizon shifting model derived from the Alternative Set Theory are used to handle weakening and reinforcement hedges, respectively. The proposed elicitation method provides for the term set semantics, non-uniform distributions of non-symmetrical triangular fuzzy numbers, depending on the number of terms used and the character of the hedges involved. (This article belongs to the section “Information Theory, Probability and Statistics”).

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Fuzzy Reasoning Numerical Spiking Neural P Systems for Induction Motor Fault Diagnosis

Cited by 10 publications

References 38 publications

On the Universality of Spiking Neural P Systems With Multiple Channels and Autapses

On the Universality of Spiking Neural P Systems With Multiple Channels and Autapses

Physical Variable Measurement Techniques for Fault Detection in Electric Motors

On the Term Set’s Semantics for Pairwise Comparisons in Fuzzy Linguistic Preference Models

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