Bridge health early warning method based on time sequence and multi-sensor fusion

The invention relates to a bridge health early warning method based on time series and multi-sensor fusion. Selecting W sensor data streams of a public bridge as a data set; checking the data to determine that the data set is an available data set; building an ARMA model and carrying out order deter...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: GE YONGXIN, YOO SEUNG-JUN, WU SHANYING, PANG QIAOZHI
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 GE YONGXIN
YOO SEUNG-JUN
WU SHANYING
PANG QIAOZHI
description The invention relates to a bridge health early warning method based on time series and multi-sensor fusion. Selecting W sensor data streams of a public bridge as a data set; checking the data to determine that the data set is an available data set; building an ARMA model and carrying out order determination on the model; performing data processing on the available data set to obtain a new data set; removing abnormal sensor data in the new data set to obtain ARMA model input data; initializing a fixed-order ARMA model and training the model by adopting long-time monitoring data to obtain a pre-trained ARMA model; presetting an alarm level, and performing step-by-step updating on the pre-training model by using the short-time monitoring data; and taking all monitoring data of the target bridge at the current time as model input after step-by-step updating, outputting and obtaining prediction results of various types of monitoring indexes of the target bridge, then carrying out information fusion on the predicti
format Patent
fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN114925518A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN114925518A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN114925518A3</originalsourceid><addsrcrecordid>eNqNyrEKwjAQBuAuDqK-w_kAHaoWdNSiOAmCezmbv20gudRcgvj2Lj6A07d88-J-itYMoBHs0kjg6D705ihWBvJIYzD0ZIWhIJSsByleGdKBWAz57JItFaIhUp_VBlkWs56dYvVzUawv50dzLTGFFjpxB0Fqm1tV7Q6buq72x-0_5wu7xDeU</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Bridge health early warning method based on time sequence and multi-sensor fusion</title><source>esp@cenet</source><creator>GE YONGXIN ; YOO SEUNG-JUN ; WU SHANYING ; PANG QIAOZHI</creator><creatorcontrib>GE YONGXIN ; YOO SEUNG-JUN ; WU SHANYING ; PANG QIAOZHI</creatorcontrib><description>The invention relates to a bridge health early warning method based on time series and multi-sensor fusion. Selecting W sensor data streams of a public bridge as a data set; checking the data to determine that the data set is an available data set; building an ARMA model and carrying out order determination on the model; performing data processing on the available data set to obtain a new data set; removing abnormal sensor data in the new data set to obtain ARMA model input data; initializing a fixed-order ARMA model and training the model by adopting long-time monitoring data to obtain a pre-trained ARMA model; presetting an alarm level, and performing step-by-step updating on the pre-training model by using the short-time monitoring data; and taking all monitoring data of the target bridge at the current time as model input after step-by-step updating, outputting and obtaining prediction results of various types of monitoring indexes of the target bridge, then carrying out information fusion on the predicti</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; HANDLING RECORD CARRIERS ; PHYSICS ; PRESENTATION OF DATA ; RECOGNITION OF DATA ; RECORD CARRIERS</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=20220819&amp;DB=EPODOC&amp;CC=CN&amp;NR=114925518A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,777,882,25545,76296</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20220819&amp;DB=EPODOC&amp;CC=CN&amp;NR=114925518A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>GE YONGXIN</creatorcontrib><creatorcontrib>YOO SEUNG-JUN</creatorcontrib><creatorcontrib>WU SHANYING</creatorcontrib><creatorcontrib>PANG QIAOZHI</creatorcontrib><title>Bridge health early warning method based on time sequence and multi-sensor fusion</title><description>The invention relates to a bridge health early warning method based on time series and multi-sensor fusion. Selecting W sensor data streams of a public bridge as a data set; checking the data to determine that the data set is an available data set; building an ARMA model and carrying out order determination on the model; performing data processing on the available data set to obtain a new data set; removing abnormal sensor data in the new data set to obtain ARMA model input data; initializing a fixed-order ARMA model and training the model by adopting long-time monitoring data to obtain a pre-trained ARMA model; presetting an alarm level, and performing step-by-step updating on the pre-training model by using the short-time monitoring data; and taking all monitoring data of the target bridge at the current time as model input after step-by-step updating, outputting and obtaining prediction results of various types of monitoring indexes of the target bridge, then carrying out information fusion on the predicti</description><subject>CALCULATING</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>HANDLING RECORD CARRIERS</subject><subject>PHYSICS</subject><subject>PRESENTATION OF DATA</subject><subject>RECOGNITION OF DATA</subject><subject>RECORD CARRIERS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2022</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNyrEKwjAQBuAuDqK-w_kAHaoWdNSiOAmCezmbv20gudRcgvj2Lj6A07d88-J-itYMoBHs0kjg6D705ihWBvJIYzD0ZIWhIJSsByleGdKBWAz57JItFaIhUp_VBlkWs56dYvVzUawv50dzLTGFFjpxB0Fqm1tV7Q6buq72x-0_5wu7xDeU</recordid><startdate>20220819</startdate><enddate>20220819</enddate><creator>GE YONGXIN</creator><creator>YOO SEUNG-JUN</creator><creator>WU SHANYING</creator><creator>PANG QIAOZHI</creator><scope>EVB</scope></search><sort><creationdate>20220819</creationdate><title>Bridge health early warning method based on time sequence and multi-sensor fusion</title><author>GE YONGXIN ; YOO SEUNG-JUN ; WU SHANYING ; PANG QIAOZHI</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN114925518A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2022</creationdate><topic>CALCULATING</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>HANDLING RECORD CARRIERS</topic><topic>PHYSICS</topic><topic>PRESENTATION OF DATA</topic><topic>RECOGNITION OF DATA</topic><topic>RECORD CARRIERS</topic><toplevel>online_resources</toplevel><creatorcontrib>GE YONGXIN</creatorcontrib><creatorcontrib>YOO SEUNG-JUN</creatorcontrib><creatorcontrib>WU SHANYING</creatorcontrib><creatorcontrib>PANG QIAOZHI</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>GE YONGXIN</au><au>YOO SEUNG-JUN</au><au>WU SHANYING</au><au>PANG QIAOZHI</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Bridge health early warning method based on time sequence and multi-sensor fusion</title><date>2022-08-19</date><risdate>2022</risdate><abstract>The invention relates to a bridge health early warning method based on time series and multi-sensor fusion. Selecting W sensor data streams of a public bridge as a data set; checking the data to determine that the data set is an available data set; building an ARMA model and carrying out order determination on the model; performing data processing on the available data set to obtain a new data set; removing abnormal sensor data in the new data set to obtain ARMA model input data; initializing a fixed-order ARMA model and training the model by adopting long-time monitoring data to obtain a pre-trained ARMA model; presetting an alarm level, and performing step-by-step updating on the pre-training model by using the short-time monitoring data; and taking all monitoring data of the target bridge at the current time as model input after step-by-step updating, outputting and obtaining prediction results of various types of monitoring indexes of the target bridge, then carrying out information fusion on the predicti</abstract><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language chi ; eng
recordid cdi_epo_espacenet_CN114925518A
source esp@cenet
subjects CALCULATING
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
HANDLING RECORD CARRIERS
PHYSICS
PRESENTATION OF DATA
RECOGNITION OF DATA
RECORD CARRIERS
title Bridge health early warning method based on time sequence and multi-sensor fusion
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-21T03%3A52%3A20IST&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=GE%20YONGXIN&rft.date=2022-08-19&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN114925518A%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