Equipment fault diagnosis method

The invention belongs to the technical field of equipment operation guarantee, and particularly relates to an equipment fault diagnosis method. The method comprises the following steps: determining the category, rated parameters and initial life cycle of research equipment; establishing an expert sy...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: PENG XIANMIN, LI XINGWANG, WEI YAN, SU FANGWEI, FAN HUIPENG, ZHOU WEI, FENG GUANG
Format: Patent
Sprache:chi ; eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator PENG XIANMIN
LI XINGWANG
WEI YAN
SU FANGWEI
FAN HUIPENG
ZHOU WEI
FENG GUANG
description The invention belongs to the technical field of equipment operation guarantee, and particularly relates to an equipment fault diagnosis method. The method comprises the following steps: determining the category, rated parameters and initial life cycle of research equipment; establishing an expert system model rule base according to object category characteristics; establishing an object historical database; introducing an artificial neural network to model a high-dimensional nonlinear problem; fault diagnosis is realized by using a BP neural network; according to the equipment fault diagnosis method, technologies such as big data and an artificial intelligence algorithm are introduced to digitalize, model and standardize equipment rated parameters, equipment operation conditions, equipment operation and maintenance history and experience and the like, meanwhile, a diagnosis system is endowed with intelligent learning and changing capabilities, and debugging parameters are fed back and corrected according to r
format Patent
fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN114386312A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN114386312A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN114386312A3</originalsourceid><addsrcrecordid>eNrjZFBwLSzNLMhNzStRSEsszSlRSMlMTM_LL84sVshNLcnIT-FhYE1LzClO5YXS3AyKbq4hzh66qQX58anFBYnJqXmpJfHOfoaGJsYWZsaGRo7GxKgBAGxCJXc</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Equipment fault diagnosis method</title><source>esp@cenet</source><creator>PENG XIANMIN ; LI XINGWANG ; WEI YAN ; SU FANGWEI ; FAN HUIPENG ; ZHOU WEI ; FENG GUANG</creator><creatorcontrib>PENG XIANMIN ; LI XINGWANG ; WEI YAN ; SU FANGWEI ; FAN HUIPENG ; ZHOU WEI ; FENG GUANG</creatorcontrib><description>The invention belongs to the technical field of equipment operation guarantee, and particularly relates to an equipment fault diagnosis method. The method comprises the following steps: determining the category, rated parameters and initial life cycle of research equipment; establishing an expert system model rule base according to object category characteristics; establishing an object historical database; introducing an artificial neural network to model a high-dimensional nonlinear problem; fault diagnosis is realized by using a BP neural network; according to the equipment fault diagnosis method, technologies such as big data and an artificial intelligence algorithm are introduced to digitalize, model and standardize equipment rated parameters, equipment operation conditions, equipment operation and maintenance history and experience and the like, meanwhile, a diagnosis system is endowed with intelligent learning and changing capabilities, and debugging parameters are fed back and corrected according to r</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; PHYSICS</subject><creationdate>2022</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20220422&amp;DB=EPODOC&amp;CC=CN&amp;NR=114386312A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,309,781,886,25569,76552</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20220422&amp;DB=EPODOC&amp;CC=CN&amp;NR=114386312A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>PENG XIANMIN</creatorcontrib><creatorcontrib>LI XINGWANG</creatorcontrib><creatorcontrib>WEI YAN</creatorcontrib><creatorcontrib>SU FANGWEI</creatorcontrib><creatorcontrib>FAN HUIPENG</creatorcontrib><creatorcontrib>ZHOU WEI</creatorcontrib><creatorcontrib>FENG GUANG</creatorcontrib><title>Equipment fault diagnosis method</title><description>The invention belongs to the technical field of equipment operation guarantee, and particularly relates to an equipment fault diagnosis method. The method comprises the following steps: determining the category, rated parameters and initial life cycle of research equipment; establishing an expert system model rule base according to object category characteristics; establishing an object historical database; introducing an artificial neural network to model a high-dimensional nonlinear problem; fault diagnosis is realized by using a BP neural network; according to the equipment fault diagnosis method, technologies such as big data and an artificial intelligence algorithm are introduced to digitalize, model and standardize equipment rated parameters, equipment operation conditions, equipment operation and maintenance history and experience and the like, meanwhile, a diagnosis system is endowed with intelligent learning and changing capabilities, and debugging parameters are fed back and corrected according to r</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2022</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZFBwLSzNLMhNzStRSEsszSlRSMlMTM_LL84sVshNLcnIT-FhYE1LzClO5YXS3AyKbq4hzh66qQX58anFBYnJqXmpJfHOfoaGJsYWZsaGRo7GxKgBAGxCJXc</recordid><startdate>20220422</startdate><enddate>20220422</enddate><creator>PENG XIANMIN</creator><creator>LI XINGWANG</creator><creator>WEI YAN</creator><creator>SU FANGWEI</creator><creator>FAN HUIPENG</creator><creator>ZHOU WEI</creator><creator>FENG GUANG</creator><scope>EVB</scope></search><sort><creationdate>20220422</creationdate><title>Equipment fault diagnosis method</title><author>PENG XIANMIN ; LI XINGWANG ; WEI YAN ; SU FANGWEI ; FAN HUIPENG ; ZHOU WEI ; FENG GUANG</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN114386312A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2022</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>PENG XIANMIN</creatorcontrib><creatorcontrib>LI XINGWANG</creatorcontrib><creatorcontrib>WEI YAN</creatorcontrib><creatorcontrib>SU FANGWEI</creatorcontrib><creatorcontrib>FAN HUIPENG</creatorcontrib><creatorcontrib>ZHOU WEI</creatorcontrib><creatorcontrib>FENG GUANG</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>PENG XIANMIN</au><au>LI XINGWANG</au><au>WEI YAN</au><au>SU FANGWEI</au><au>FAN HUIPENG</au><au>ZHOU WEI</au><au>FENG GUANG</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Equipment fault diagnosis method</title><date>2022-04-22</date><risdate>2022</risdate><abstract>The invention belongs to the technical field of equipment operation guarantee, and particularly relates to an equipment fault diagnosis method. The method comprises the following steps: determining the category, rated parameters and initial life cycle of research equipment; establishing an expert system model rule base according to object category characteristics; establishing an object historical database; introducing an artificial neural network to model a high-dimensional nonlinear problem; fault diagnosis is realized by using a BP neural network; according to the equipment fault diagnosis method, technologies such as big data and an artificial intelligence algorithm are introduced to digitalize, model and standardize equipment rated parameters, equipment operation conditions, equipment operation and maintenance history and experience and the like, meanwhile, a diagnosis system is endowed with intelligent learning and changing capabilities, and debugging parameters are fed back and corrected according to r</abstract><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language chi ; eng
recordid cdi_epo_espacenet_CN114386312A
source esp@cenet
subjects CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
title Equipment fault diagnosis method
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-12T11%3A30%3A45IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=PENG%20XIANMIN&rft.date=2022-04-22&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN114386312A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true