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...
Gespeichert in:
Hauptverfasser: | , , , |
---|---|
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&date=20220819&DB=EPODOC&CC=CN&NR=114925518A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,777,882,25545,76296</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20220819&DB=EPODOC&CC=CN&NR=114925518A$$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 |