Deep learning-based bridge acceleration monitoring data anomaly detection method, system and device
The invention discloses a deep learning-based bridge acceleration monitoring data anomaly detection method, system and device. The method comprises the following steps of obtaining real bridge acceleration time history data; processing the acquired acceleration time history data to obtain a 9-dimens...
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| creator | DING YOULIANG ZHAO DACHENG CHEN BIN HE QILONG JIAN ZHENZHEN LIU XINGWANG DAI XINJUN MEI DAPENG WAN CHUNFENG JIA WENWEN |
| description | The invention discloses a deep learning-based bridge acceleration monitoring data anomaly detection method, system and device. The method comprises the following steps of obtaining real bridge acceleration time history data; processing the acquired acceleration time history data to obtain a 9-dimensional acceleration characteristic matrix; inputting the 9-dimensional acceleration characteristic matrix into a pre-trained LSTM neural network model; and determining the abnormal type of the bridge acceleration monitoring data according to the output of the LSTM neural network model. The method can effectively avoid a large amount of manual intervention required by a traditional threshold setting method, can intelligently recognize and detect the abnormality of the bridge acceleration monitoring data, saves manpower, and is high in detection efficiency.
本发明公开了一种基于深度学习的桥梁加速度监测数据异常检测方法、系统和装置，包括以下步骤：获取实桥加速度时程数据；对获取到的加速度时程数据进行处理，得到9维加速度特征矩阵；将所述9维加速度特征矩阵输入预先训练的LSTM神经网络模型；根据所述LSTM神经网络模型的输出，确定桥梁加速度监测数据异常类型。本发明可以有效避免传统设置阈 |
| format | Patent |
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本发明公开了一种基于深度学习的桥梁加速度监测数据异常检测方法、系统和装置，包括以下步骤：获取实桥加速度时程数据；对获取到的加速度时程数据进行处理，得到9维加速度特征矩阵；将所述9维加速度特征矩阵输入预先训练的LSTM神经网络模型；根据所述LSTM神经网络模型的输出，确定桥梁加速度监测数据异常类型。本发明可以有效避免传统设置阈</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT ; MEASURING ; MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION,OR SHOCK ; PHYSICS ; TESTING ; TESTING STATIC OR DYNAMIC BALANCE OF MACHINES ORSTRUCTURES ; TESTING STRUCTURES OR APPARATUS NOT OTHERWISE PROVIDED FOR</subject><creationdate>2021</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=20211231&DB=EPODOC&CC=CN&NR=113866455A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20211231&DB=EPODOC&CC=CN&NR=113866455A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>DING YOULIANG</creatorcontrib><creatorcontrib>ZHAO DACHENG</creatorcontrib><creatorcontrib>CHEN BIN</creatorcontrib><creatorcontrib>HE QILONG</creatorcontrib><creatorcontrib>JIAN ZHENZHEN</creatorcontrib><creatorcontrib>LIU XINGWANG</creatorcontrib><creatorcontrib>DAI XINJUN</creatorcontrib><creatorcontrib>MEI DAPENG</creatorcontrib><creatorcontrib>WAN CHUNFENG</creatorcontrib><creatorcontrib>JIA WENWEN</creatorcontrib><title>Deep learning-based bridge acceleration monitoring data anomaly detection method, system and device</title><description>The invention discloses a deep learning-based bridge acceleration monitoring data anomaly detection method, system and device. The method comprises the following steps of obtaining real bridge acceleration time history data; processing the acquired acceleration time history data to obtain a 9-dimensional acceleration characteristic matrix; inputting the 9-dimensional acceleration characteristic matrix into a pre-trained LSTM neural network model; and determining the abnormal type of the bridge acceleration monitoring data according to the output of the LSTM neural network model. The method can effectively avoid a large amount of manual intervention required by a traditional threshold setting method, can intelligently recognize and detect the abnormality of the bridge acceleration monitoring data, saves manpower, and is high in detection efficiency.
本发明公开了一种基于深度学习的桥梁加速度监测数据异常检测方法、系统和装置，包括以下步骤：获取实桥加速度时程数据；对获取到的加速度时程数据进行处理，得到9维加速度特征矩阵；将所述9维加速度特征矩阵输入预先训练的LSTM神经网络模型；根据所述LSTM神经网络模型的输出，确定桥梁加速度监测数据异常类型。本发明可以有效避免传统设置阈</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT</subject><subject>MEASURING</subject><subject>MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION,OR SHOCK</subject><subject>PHYSICS</subject><subject>TESTING</subject><subject>TESTING STATIC OR DYNAMIC BALANCE OF MACHINES ORSTRUCTURES</subject><subject>TESTING STRUCTURES OR APPARATUS NOT OTHERWISE PROVIDED FOR</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2021</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNyrEKwjAURuEsDqK-w3W3Q6ktrlIVJyf3cpv8toE0KclF6Nsb0AdwOsP51kpfgJkcOHrrh6LnBEN9tGYAsdZwiCw2eJqCtxJiRmRYmNiHid1CBgL9FZAxmAOlJQmmDEyeb6uxVasXu4Tdrxu1v12f7b3AHDqkmTU8pGsfZVmdmuZY1-fqH_MBolc-jg</recordid><startdate>20211231</startdate><enddate>20211231</enddate><creator>DING YOULIANG</creator><creator>ZHAO DACHENG</creator><creator>CHEN BIN</creator><creator>HE QILONG</creator><creator>JIAN ZHENZHEN</creator><creator>LIU XINGWANG</creator><creator>DAI XINJUN</creator><creator>MEI DAPENG</creator><creator>WAN CHUNFENG</creator><creator>JIA WENWEN</creator><scope>EVB</scope></search><sort><creationdate>20211231</creationdate><title>Deep learning-based bridge acceleration monitoring data anomaly detection method, system and device</title><author>DING YOULIANG ; ZHAO DACHENG ; CHEN BIN ; HE QILONG ; JIAN ZHENZHEN ; LIU XINGWANG ; DAI XINJUN ; MEI DAPENG ; WAN CHUNFENG ; JIA WENWEN</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN113866455A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2021</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT</topic><topic>MEASURING</topic><topic>MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION,OR SHOCK</topic><topic>PHYSICS</topic><topic>TESTING</topic><topic>TESTING STATIC OR DYNAMIC BALANCE OF MACHINES ORSTRUCTURES</topic><topic>TESTING STRUCTURES OR APPARATUS NOT OTHERWISE PROVIDED FOR</topic><toplevel>online_resources</toplevel><creatorcontrib>DING YOULIANG</creatorcontrib><creatorcontrib>ZHAO DACHENG</creatorcontrib><creatorcontrib>CHEN BIN</creatorcontrib><creatorcontrib>HE QILONG</creatorcontrib><creatorcontrib>JIAN ZHENZHEN</creatorcontrib><creatorcontrib>LIU XINGWANG</creatorcontrib><creatorcontrib>DAI XINJUN</creatorcontrib><creatorcontrib>MEI DAPENG</creatorcontrib><creatorcontrib>WAN CHUNFENG</creatorcontrib><creatorcontrib>JIA WENWEN</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>DING YOULIANG</au><au>ZHAO DACHENG</au><au>CHEN BIN</au><au>HE QILONG</au><au>JIAN ZHENZHEN</au><au>LIU XINGWANG</au><au>DAI XINJUN</au><au>MEI DAPENG</au><au>WAN CHUNFENG</au><au>JIA WENWEN</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Deep learning-based bridge acceleration monitoring data anomaly detection method, system and device</title><date>2021-12-31</date><risdate>2021</risdate><abstract>The invention discloses a deep learning-based bridge acceleration monitoring data anomaly detection method, system and device. The method comprises the following steps of obtaining real bridge acceleration time history data; processing the acquired acceleration time history data to obtain a 9-dimensional acceleration characteristic matrix; inputting the 9-dimensional acceleration characteristic matrix into a pre-trained LSTM neural network model; and determining the abnormal type of the bridge acceleration monitoring data according to the output of the LSTM neural network model. The method can effectively avoid a large amount of manual intervention required by a traditional threshold setting method, can intelligently recognize and detect the abnormality of the bridge acceleration monitoring data, saves manpower, and is high in detection efficiency.
本发明公开了一种基于深度学习的桥梁加速度监测数据异常检测方法、系统和装置，包括以下步骤：获取实桥加速度时程数据；对获取到的加速度时程数据进行处理，得到9维加速度特征矩阵；将所述9维加速度特征矩阵输入预先训练的LSTM神经网络模型；根据所述LSTM神经网络模型的输出，确定桥梁加速度监测数据异常类型。本发明可以有效避免传统设置阈</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT MEASURING MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION,OR SHOCK PHYSICS TESTING TESTING STATIC OR DYNAMIC BALANCE OF MACHINES ORSTRUCTURES TESTING STRUCTURES OR APPARATUS NOT OTHERWISE PROVIDED FOR |
| title | Deep learning-based bridge acceleration monitoring data anomaly detection method, system and device |
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