A rough set-based bio-inspired fault diagnosis method for electrical substations

Wang, Tao; Liu, Wei; Zhao, Junbo; Guo, Xiaokang; Terzija, Vladimir

doi:10.1016/j.ijepes.2020.105961

Cited by 99 publications

(51 citation statements)

References 29 publications

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“…The vast activity noted in the literature is a clear representation of the technical advances attained lately with bio-inspired computation applied to Big Data. Indeed, manifold domains have capitalized bio-inspired computation in data-based applications, including energy [ 198 , 199 ], transport and mobility [ 60 ], health [ 200 ], industry [ 201 , 202 ], agriculture [ 203 ], cyber-physical systems [ 20 ], social networks [ 204 – 206 ] or sensor networks [ 207 ], among many others. Recent worldwide developments around the COVID-19 pandemic have also ignited research activity on Big Data and Artificial Intelligence (in many cases, using deep neural networks for CT scan-based diagnosis), yet without much evidence that the scales of studies claiming to be Big Data so far can be considered as such [ 208 , 209 ].…”

Section: Critical Analysis Open Challenges and Research Directionsmentioning

confidence: 99%

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

Torre-Bastida

Díaz-de-Arcaya

Osaba

et al. 2021

Neural Comput & Applic

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This overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.

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Section: Critical Analysis Open Challenges and Research Directionsmentioning

confidence: 99%

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

Torre-Bastida

Díaz-de-Arcaya

Osaba

et al. 2021

Neural Comput & Applic

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“…To deal with uncertainty, many tools and models have been proposed [41][42][43][44], such as fuzzy sets [45,46], basic probability assignment [47,48], rough sets [49], ordered weighted average operator [50], entropy [51][52][53][54], game theory [55,56] and complex networks [57][58][59][60][61][62]. In this section, some relative definitions are briefly introduced, such as refined expected value [40], measure [63,64] and Choquet integral [65].…”

Section: Preliminariesmentioning

confidence: 99%

Refined Expected Value Decision Rules under Orthopair Fuzzy Environment

Xue

Deng

2020

Mathematics

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Refined expected value decision rules can refine the calculation of the expected value and make decisions by estimating the expected values of different alternatives, which use many theories, such as Choquet integral, PM function, measure and so on. However, the refined expected value decision rules have not been applied to the orthopair fuzzy environment yet. To address this issue, in this paper we propose the refined expected value decision rules under the orthopair fuzzy environment, which can apply the refined expected value decision rules on the issues of decision making that is described in the orthopair fuzzy environment. Numerical examples were applied to verify the availability and flexibility of the new refined expected value decision rules model. The experimental results demonstrate that the proposed model can apply refined expected value decision rules in the orthopair fuzzy environment and solve the decision making issues with the orthopair fuzzy environment successfully.

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“…erefore, how to improve the abovementioned fault prediction and abductive fault diagnosis methods or put forward new ones is the main issue in the corresponding engineering domain for the motors. On the other hand, with the rapid development of artificial intelligence technology, intelligent analysis and diagnosis methods are gradually developed, such as expert systems (ESs) [15], artificial neural networks (ANNs) [16][17][18][19][20], Petri nets (PNs) [21][22][23], tissue P systems (TPSs) [24][25][26], and spiking neural P systems (SNPSs) [27][28][29][30][31][32][33][34]. Specifically, SNPS is a novel high-performance bioinspired distributed parallel computing model with powerful information processing ability.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, SNPS is a novel high-performance bioinspired distributed parallel computing model with powerful information processing ability. It is a special kind of neural-like P system [29] inspired by the topological structure of biological neural networks and the way that biological neurons store, transmit, and exchange messages, i.e., by sending electrical impulses (spikes) along axons in a distributed and parallel manner [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

Self Cite

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The fault prediction and abductive fault diagnosis of three-phase induction motors are of great importance for improving their working safety, reliability, and economy; however, it is difficult to succeed in solving these issues. This paper proposes a fault analysis method of motors based on modified fuzzy reasoning spiking neural P systems with real numbers (rMFRSNPSs) for fault prediction and abductive fault diagnosis. To achieve this goal, fault fuzzy production rules of three-phase induction motors are first proposed. Then, the rMFRSNPS is presented to model the rules, which provides an intuitive way for modelling the motors. Moreover, to realize the parallel data computing and information reasoning in the fault prediction and diagnosis process, three reasoning algorithms for the rMFRSNPS are proposed: the pulse value reasoning algorithm, the forward fault prediction reasoning algorithm, and the backward abductive fault diagnosis reasoning algorithm. Finally, some case studies are given, in order to verify the feasibility and effectiveness of the proposed method.

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A rough set-based bio-inspired fault diagnosis method for electrical substations

Cited by 99 publications

References 29 publications

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

Refined Expected Value Decision Rules under Orthopair Fuzzy Environment

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

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