Loading…
Fuzzy Reasoning Numerical Spiking Neural P Systems for Induction Motor Fault Diagnosis
The fuzzy reasoning numerical spiking neural P systems (FRNSN P systems) are proposed by introducing the interval-valued triangular fuzzy numbers into the numerical spiking neural P systems (NSN P systems). The NSN P systems were applied to the SAT problem and the FRNSN P systems were applied to ind...
Saved in:
| Published in: | Entropy (Basel, Switzerland) Switzerland), 2022-09, Vol.24 (10), p.1385 |
|---|---|
| Main Authors: | , , , , |
| Format: | Article |
| Language: | English |
| Subjects: | |
| Citations: | Items that this one cites Items that cite this one |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | cdi_FETCH-LOGICAL-c438t-83224c7c3c551e02a709ae351aea1e0016a2f0dca1732c6a47902e410ace56b63 |
|---|---|
| cites | cdi_FETCH-LOGICAL-c438t-83224c7c3c551e02a709ae351aea1e0016a2f0dca1732c6a47902e410ace56b63 |
| container_end_page | |
| container_issue | 10 |
| container_start_page | 1385 |
| container_title | Entropy (Basel, Switzerland) |
| container_volume | 24 |
| creator | Yin, Xiu Liu, Xiyu Sun, Minghe Dong, Jianping Zhang, Gexiang |
| description | The fuzzy reasoning numerical spiking neural P systems (FRNSN P systems) are proposed by introducing the interval-valued triangular fuzzy numbers into the numerical spiking neural P systems (NSN P systems). The NSN P systems were applied to the SAT problem and the FRNSN P systems were applied to induction motor fault diagnosis. The FRNSN P system can easily model fuzzy production rules for motor faults and perform fuzzy reasoning. To perform the inference process, a FRNSN P reasoning algorithm was designed. During inference, the interval-valued triangular fuzzy numbers were used to characterize the incomplete and uncertain motor fault information. The relative preference relationship was used to estimate the severity of various faults, so as to warn and repair the motors in time when minor faults occur. The results of the case studies showed that the FRNSN P reasoning algorithm can successfully diagnose single and multiple induction motor faults and has certain advantages over other existing methods. |
| doi_str_mv | 10.3390/e24101385 |
| format | article |
| fullrecord | <record><control><sourceid>gale_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_dde3aa11b00e4a0da1ded3f3fdceff90</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A745709295</galeid><doaj_id>oai_doaj_org_article_dde3aa11b00e4a0da1ded3f3fdceff90</doaj_id><sourcerecordid>A745709295</sourcerecordid><originalsourceid>FETCH-LOGICAL-c438t-83224c7c3c551e02a709ae351aea1e0016a2f0dca1732c6a47902e410ace56b63</originalsourceid><addsrcrecordid>eNpdUstuFDEQHCEQCYEDP4BG4kIOG9qveVyQopCFlcJDBLhavZ724mXG3tgzSJuvx8mGVYJ8sKtcXXa1uiheMjgRooW3xCUDJhr1qDhk0LYzKQAe3zsfFM9SWgNwwVn1tDgQteQgQR0WP-fT9fW2_EaYgnd-VX6eBorOYF9ebtzvW4ammOHX8nKbRhpSaUMsF76bzOiCLz-FMeM5Tv1Yvne48iG59Lx4YrFP9OJuPyp-zM-_n32cXXz5sDg7vZgZKZpx1gjOpamNMEoxAo41tEhCMSTMGFiF3EJnkNWCmwpl3QKnHBYNqWpZiaNisfPtAq71JroB41YHdPqWCHGlMY7O9KS7jgQiY0sAkggdso46YYXtDFnbQvZ6t_PaTMuBMuvHnPuB6cMb737pVfij2wqYamU2eHNnEMPVRGnUg0uG-h49hSlp3gjF60a2dZa-_k-6DlP0uVWa17yRFZfNTbqTnWqFOYDzNuR3TV4dDc4ET9Zl_rSWKveNtyoXHO8KTAwpRbL73zPQN6Oi96OSta_ux90r_82G-AuXJrj5</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2728462486</pqid></control><display><type>article</type><title>Fuzzy Reasoning Numerical Spiking Neural P Systems for Induction Motor Fault Diagnosis</title><source>Open Access: PubMed Central</source><source>Publicly Available Content Database</source><source>DOAJ Directory of Open Access Journals</source><creator>Yin, Xiu ; Liu, Xiyu ; Sun, Minghe ; Dong, Jianping ; Zhang, Gexiang</creator><creatorcontrib>Yin, Xiu ; Liu, Xiyu ; Sun, Minghe ; Dong, Jianping ; Zhang, Gexiang</creatorcontrib><description>The fuzzy reasoning numerical spiking neural P systems (FRNSN P systems) are proposed by introducing the interval-valued triangular fuzzy numbers into the numerical spiking neural P systems (NSN P systems). The NSN P systems were applied to the SAT problem and the FRNSN P systems were applied to induction motor fault diagnosis. The FRNSN P system can easily model fuzzy production rules for motor faults and perform fuzzy reasoning. To perform the inference process, a FRNSN P reasoning algorithm was designed. During inference, the interval-valued triangular fuzzy numbers were used to characterize the incomplete and uncertain motor fault information. The relative preference relationship was used to estimate the severity of various faults, so as to warn and repair the motors in time when minor faults occur. The results of the case studies showed that the FRNSN P reasoning algorithm can successfully diagnose single and multiple induction motor faults and has certain advantages over other existing methods.</description><identifier>ISSN: 1099-4300</identifier><identifier>EISSN: 1099-4300</identifier><identifier>DOI: 10.3390/e24101385</identifier><identifier>PMID: 37420405</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Algorithms ; Analysis ; Artificial neural networks ; Boolean ; Fault diagnosis ; Fault location (Engineering) ; Faults ; Fuzzy algorithms ; Fuzzy logic ; fuzzy reasoning numerical spiking neural P systems ; Fuzzy systems ; Induction electric motors ; Induction motors ; Inference ; Inspection ; interval-valued triangular fuzzy numbers ; Methods ; Neural networks ; Spiking ; Wavelet transforms</subject><ispartof>Entropy (Basel, Switzerland), 2022-09, Vol.24 (10), p.1385</ispartof><rights>COPYRIGHT 2022 MDPI AG</rights><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 by the authors. 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c438t-83224c7c3c551e02a709ae351aea1e0016a2f0dca1732c6a47902e410ace56b63</citedby><cites>FETCH-LOGICAL-c438t-83224c7c3c551e02a709ae351aea1e0016a2f0dca1732c6a47902e410ace56b63</cites><orcidid>0000-0001-8034-0977 ; 0000-0003-4976-9227 ; 0000-0001-8503-9761</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2728462486/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2728462486?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,25753,27924,27925,37012,37013,44590,53791,53793,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37420405$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Yin, Xiu</creatorcontrib><creatorcontrib>Liu, Xiyu</creatorcontrib><creatorcontrib>Sun, Minghe</creatorcontrib><creatorcontrib>Dong, Jianping</creatorcontrib><creatorcontrib>Zhang, Gexiang</creatorcontrib><title>Fuzzy Reasoning Numerical Spiking Neural P Systems for Induction Motor Fault Diagnosis</title><title>Entropy (Basel, Switzerland)</title><addtitle>Entropy (Basel)</addtitle><description>The fuzzy reasoning numerical spiking neural P systems (FRNSN P systems) are proposed by introducing the interval-valued triangular fuzzy numbers into the numerical spiking neural P systems (NSN P systems). The NSN P systems were applied to the SAT problem and the FRNSN P systems were applied to induction motor fault diagnosis. The FRNSN P system can easily model fuzzy production rules for motor faults and perform fuzzy reasoning. To perform the inference process, a FRNSN P reasoning algorithm was designed. During inference, the interval-valued triangular fuzzy numbers were used to characterize the incomplete and uncertain motor fault information. The relative preference relationship was used to estimate the severity of various faults, so as to warn and repair the motors in time when minor faults occur. The results of the case studies showed that the FRNSN P reasoning algorithm can successfully diagnose single and multiple induction motor faults and has certain advantages over other existing methods.</description><subject>Algorithms</subject><subject>Analysis</subject><subject>Artificial neural networks</subject><subject>Boolean</subject><subject>Fault diagnosis</subject><subject>Fault location (Engineering)</subject><subject>Faults</subject><subject>Fuzzy algorithms</subject><subject>Fuzzy logic</subject><subject>fuzzy reasoning numerical spiking neural P systems</subject><subject>Fuzzy systems</subject><subject>Induction electric motors</subject><subject>Induction motors</subject><subject>Inference</subject><subject>Inspection</subject><subject>interval-valued triangular fuzzy numbers</subject><subject>Methods</subject><subject>Neural networks</subject><subject>Spiking</subject><subject>Wavelet transforms</subject><issn>1099-4300</issn><issn>1099-4300</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpdUstuFDEQHCEQCYEDP4BG4kIOG9qveVyQopCFlcJDBLhavZ724mXG3tgzSJuvx8mGVYJ8sKtcXXa1uiheMjgRooW3xCUDJhr1qDhk0LYzKQAe3zsfFM9SWgNwwVn1tDgQteQgQR0WP-fT9fW2_EaYgnd-VX6eBorOYF9ebtzvW4ammOHX8nKbRhpSaUMsF76bzOiCLz-FMeM5Tv1Yvne48iG59Lx4YrFP9OJuPyp-zM-_n32cXXz5sDg7vZgZKZpx1gjOpamNMEoxAo41tEhCMSTMGFiF3EJnkNWCmwpl3QKnHBYNqWpZiaNisfPtAq71JroB41YHdPqWCHGlMY7O9KS7jgQiY0sAkggdso46YYXtDFnbQvZ6t_PaTMuBMuvHnPuB6cMb737pVfij2wqYamU2eHNnEMPVRGnUg0uG-h49hSlp3gjF60a2dZa-_k-6DlP0uVWa17yRFZfNTbqTnWqFOYDzNuR3TV4dDc4ET9Zl_rSWKveNtyoXHO8KTAwpRbL73zPQN6Oi96OSta_ux90r_82G-AuXJrj5</recordid><startdate>20220928</startdate><enddate>20220928</enddate><creator>Yin, Xiu</creator><creator>Liu, Xiyu</creator><creator>Sun, Minghe</creator><creator>Dong, Jianping</creator><creator>Zhang, Gexiang</creator><general>MDPI AG</general><general>MDPI</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>HCIFZ</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-8034-0977</orcidid><orcidid>https://orcid.org/0000-0003-4976-9227</orcidid><orcidid>https://orcid.org/0000-0001-8503-9761</orcidid></search><sort><creationdate>20220928</creationdate><title>Fuzzy Reasoning Numerical Spiking Neural P Systems for Induction Motor Fault Diagnosis</title><author>Yin, Xiu ; Liu, Xiyu ; Sun, Minghe ; Dong, Jianping ; Zhang, Gexiang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c438t-83224c7c3c551e02a709ae351aea1e0016a2f0dca1732c6a47902e410ace56b63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Analysis</topic><topic>Artificial neural networks</topic><topic>Boolean</topic><topic>Fault diagnosis</topic><topic>Fault location (Engineering)</topic><topic>Faults</topic><topic>Fuzzy algorithms</topic><topic>Fuzzy logic</topic><topic>fuzzy reasoning numerical spiking neural P systems</topic><topic>Fuzzy systems</topic><topic>Induction electric motors</topic><topic>Induction motors</topic><topic>Inference</topic><topic>Inspection</topic><topic>interval-valued triangular fuzzy numbers</topic><topic>Methods</topic><topic>Neural networks</topic><topic>Spiking</topic><topic>Wavelet transforms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yin, Xiu</creatorcontrib><creatorcontrib>Liu, Xiyu</creatorcontrib><creatorcontrib>Sun, Minghe</creatorcontrib><creatorcontrib>Dong, Jianping</creatorcontrib><creatorcontrib>Zhang, Gexiang</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>SciTech Premium Collection</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Entropy (Basel, Switzerland)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yin, Xiu</au><au>Liu, Xiyu</au><au>Sun, Minghe</au><au>Dong, Jianping</au><au>Zhang, Gexiang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fuzzy Reasoning Numerical Spiking Neural P Systems for Induction Motor Fault Diagnosis</atitle><jtitle>Entropy (Basel, Switzerland)</jtitle><addtitle>Entropy (Basel)</addtitle><date>2022-09-28</date><risdate>2022</risdate><volume>24</volume><issue>10</issue><spage>1385</spage><pages>1385-</pages><issn>1099-4300</issn><eissn>1099-4300</eissn><abstract>The fuzzy reasoning numerical spiking neural P systems (FRNSN P systems) are proposed by introducing the interval-valued triangular fuzzy numbers into the numerical spiking neural P systems (NSN P systems). The NSN P systems were applied to the SAT problem and the FRNSN P systems were applied to induction motor fault diagnosis. The FRNSN P system can easily model fuzzy production rules for motor faults and perform fuzzy reasoning. To perform the inference process, a FRNSN P reasoning algorithm was designed. During inference, the interval-valued triangular fuzzy numbers were used to characterize the incomplete and uncertain motor fault information. The relative preference relationship was used to estimate the severity of various faults, so as to warn and repair the motors in time when minor faults occur. The results of the case studies showed that the FRNSN P reasoning algorithm can successfully diagnose single and multiple induction motor faults and has certain advantages over other existing methods.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>37420405</pmid><doi>10.3390/e24101385</doi><orcidid>https://orcid.org/0000-0001-8034-0977</orcidid><orcidid>https://orcid.org/0000-0003-4976-9227</orcidid><orcidid>https://orcid.org/0000-0001-8503-9761</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1099-4300 |
| ispartof | Entropy (Basel, Switzerland), 2022-09, Vol.24 (10), p.1385 |
| issn | 1099-4300 1099-4300 |
| language | eng |
| recordid | cdi_doaj_primary_oai_doaj_org_article_dde3aa11b00e4a0da1ded3f3fdceff90 |
| source | Open Access: PubMed Central; Publicly Available Content Database; DOAJ Directory of Open Access Journals |
| subjects | Algorithms Analysis Artificial neural networks Boolean Fault diagnosis Fault location (Engineering) Faults Fuzzy algorithms Fuzzy logic fuzzy reasoning numerical spiking neural P systems Fuzzy systems Induction electric motors Induction motors Inference Inspection interval-valued triangular fuzzy numbers Methods Neural networks Spiking Wavelet transforms |
| title | Fuzzy Reasoning Numerical Spiking Neural P Systems for Induction Motor Fault Diagnosis |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-01T18%3A54%3A17IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Fuzzy%20Reasoning%20Numerical%20Spiking%20Neural%20P%20Systems%20for%20Induction%20Motor%20Fault%20Diagnosis&rft.jtitle=Entropy%20(Basel,%20Switzerland)&rft.au=Yin,%20Xiu&rft.date=2022-09-28&rft.volume=24&rft.issue=10&rft.spage=1385&rft.pages=1385-&rft.issn=1099-4300&rft.eissn=1099-4300&rft_id=info:doi/10.3390/e24101385&rft_dat=%3Cgale_doaj_%3EA745709295%3C/gale_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c438t-83224c7c3c551e02a709ae351aea1e0016a2f0dca1732c6a47902e410ace56b63%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2728462486&rft_id=info:pmid/37420405&rft_galeid=A745709295&rfr_iscdi=true |