Operation state prediction model training method and equipment operation state detection method

The invention discloses an operation state prediction model training method and an equipment operation state detection method. The prediction model is trained by using the vibration time-frequency data of the plurality of devices corresponding to the target device category at the client to obtain th...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: WANG XIULI, LYU HONGQIANG, YU YIJUN, WANG HONGZHANG, ZHAO SHUBAO, GUO SHENG, DING QIJIE
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 WANG XIULI
LYU HONGQIANG
YU YIJUN
WANG HONGZHANG
ZHAO SHUBAO
GUO SHENG
DING QIJIE
description The invention discloses an operation state prediction model training method and an equipment operation state detection method. The prediction model is trained by using the vibration time-frequency data of the plurality of devices corresponding to the target device category at the client to obtain the first model parameter, and the first model parameter and the target category identifier corresponding to the target device category are sent to the server, so that the pertinence of the prediction model can be improved, and the prediction efficiency is improved. The influence of the training data distribution difference on the model training result can be reduced, and the detection accuracy of the operation state prediction model on the equipment operation state can be improved. Further, the server performs classification aggregation processing on the first model parameters sent by the plurality of clients according to the target category identifier to obtain a second model parameter corresponding to the target c
format Patent
fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN115577280A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN115577280A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN115577280A3</originalsourceid><addsrcrecordid>eNqNi7EKwjAURbM4iPoPzw8QrFLqKqXipIt7eDRXDTQvafL8f6F0cnI6cDhnaew9IbP6KFSUFZQynO8nEaLDQJrZi5cXBeg7OmJxhPHjU4AoxZ_dQTHfU742iycPBZuZK7O9dI_2ukOKFiVxD4Ha9lZVdd00h9P-fPyn-QL5oD3c</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Operation state prediction model training method and equipment operation state detection method</title><source>esp@cenet</source><creator>WANG XIULI ; LYU HONGQIANG ; YU YIJUN ; WANG HONGZHANG ; ZHAO SHUBAO ; GUO SHENG ; DING QIJIE</creator><creatorcontrib>WANG XIULI ; LYU HONGQIANG ; YU YIJUN ; WANG HONGZHANG ; ZHAO SHUBAO ; GUO SHENG ; DING QIJIE</creatorcontrib><description>The invention discloses an operation state prediction model training method and an equipment operation state detection method. The prediction model is trained by using the vibration time-frequency data of the plurality of devices corresponding to the target device category at the client to obtain the first model parameter, and the first model parameter and the target category identifier corresponding to the target device category are sent to the server, so that the pertinence of the prediction model can be improved, and the prediction efficiency is improved. The influence of the training data distribution difference on the model training result can be reduced, and the detection accuracy of the operation state prediction model on the equipment operation state can be improved. Further, the server performs classification aggregation processing on the first model parameters sent by the plurality of clients according to the target category identifier to obtain a second model parameter corresponding to the target c</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; PHYSICS</subject><creationdate>2023</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=20230106&amp;DB=EPODOC&amp;CC=CN&amp;NR=115577280A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20230106&amp;DB=EPODOC&amp;CC=CN&amp;NR=115577280A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>WANG XIULI</creatorcontrib><creatorcontrib>LYU HONGQIANG</creatorcontrib><creatorcontrib>YU YIJUN</creatorcontrib><creatorcontrib>WANG HONGZHANG</creatorcontrib><creatorcontrib>ZHAO SHUBAO</creatorcontrib><creatorcontrib>GUO SHENG</creatorcontrib><creatorcontrib>DING QIJIE</creatorcontrib><title>Operation state prediction model training method and equipment operation state detection method</title><description>The invention discloses an operation state prediction model training method and an equipment operation state detection method. The prediction model is trained by using the vibration time-frequency data of the plurality of devices corresponding to the target device category at the client to obtain the first model parameter, and the first model parameter and the target category identifier corresponding to the target device category are sent to the server, so that the pertinence of the prediction model can be improved, and the prediction efficiency is improved. The influence of the training data distribution difference on the model training result can be reduced, and the detection accuracy of the operation state prediction model on the equipment operation state can be improved. Further, the server performs classification aggregation processing on the first model parameters sent by the plurality of clients according to the target category identifier to obtain a second model parameter corresponding to the target c</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>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNi7EKwjAURbM4iPoPzw8QrFLqKqXipIt7eDRXDTQvafL8f6F0cnI6cDhnaew9IbP6KFSUFZQynO8nEaLDQJrZi5cXBeg7OmJxhPHjU4AoxZ_dQTHfU742iycPBZuZK7O9dI_2ukOKFiVxD4Ha9lZVdd00h9P-fPyn-QL5oD3c</recordid><startdate>20230106</startdate><enddate>20230106</enddate><creator>WANG XIULI</creator><creator>LYU HONGQIANG</creator><creator>YU YIJUN</creator><creator>WANG HONGZHANG</creator><creator>ZHAO SHUBAO</creator><creator>GUO SHENG</creator><creator>DING QIJIE</creator><scope>EVB</scope></search><sort><creationdate>20230106</creationdate><title>Operation state prediction model training method and equipment operation state detection method</title><author>WANG XIULI ; LYU HONGQIANG ; YU YIJUN ; WANG HONGZHANG ; ZHAO SHUBAO ; GUO SHENG ; DING QIJIE</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN115577280A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2023</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>WANG XIULI</creatorcontrib><creatorcontrib>LYU HONGQIANG</creatorcontrib><creatorcontrib>YU YIJUN</creatorcontrib><creatorcontrib>WANG HONGZHANG</creatorcontrib><creatorcontrib>ZHAO SHUBAO</creatorcontrib><creatorcontrib>GUO SHENG</creatorcontrib><creatorcontrib>DING QIJIE</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>WANG XIULI</au><au>LYU HONGQIANG</au><au>YU YIJUN</au><au>WANG HONGZHANG</au><au>ZHAO SHUBAO</au><au>GUO SHENG</au><au>DING QIJIE</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Operation state prediction model training method and equipment operation state detection method</title><date>2023-01-06</date><risdate>2023</risdate><abstract>The invention discloses an operation state prediction model training method and an equipment operation state detection method. The prediction model is trained by using the vibration time-frequency data of the plurality of devices corresponding to the target device category at the client to obtain the first model parameter, and the first model parameter and the target category identifier corresponding to the target device category are sent to the server, so that the pertinence of the prediction model can be improved, and the prediction efficiency is improved. The influence of the training data distribution difference on the model training result can be reduced, and the detection accuracy of the operation state prediction model on the equipment operation state can be improved. Further, the server performs classification aggregation processing on the first model parameters sent by the plurality of clients according to the target category identifier to obtain a second model parameter corresponding to the target c</abstract><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language chi ; eng
recordid cdi_epo_espacenet_CN115577280A
source esp@cenet
subjects CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
title Operation state prediction model training method and equipment operation state detection method
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-29T13%3A04%3A13IST&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=WANG%20XIULI&rft.date=2023-01-06&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN115577280A%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