Data-based linear Gaussian state-space model for dynamic process monitoring
This article develops a data‐based linear Gaussian state‐space model for monitoring of dynamic processes under noisy environment. The Kalman filter is introduced for construction of the linear Gaussian state‐space model, and an iterative expectation‐maximization algorithm is used for model parameter...
Gespeichert in:
| Veröffentlicht in: | AIChE journal 2012-12, Vol.58 (12), p.3763-3776 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 3776 |
|---|---|
| container_issue | 12 |
| container_start_page | 3763 |
| container_title | AIChE journal |
| container_volume | 58 |
| creator | Wen, Qiaojun Ge, Zhiqiang Song, Zhihuan |
| description | This article develops a data‐based linear Gaussian state‐space model for monitoring of dynamic processes under noisy environment. The Kalman filter is introduced for construction of the linear Gaussian state‐space model, and an iterative expectation‐maximization algorithm is used for model parameters learning. With the incorporation of the dynamic data information, a new fault detection and identification approach is proposed. The feasibility and effectiveness of the two monitoring statistics in the new method are theoretically evaluated and further confirmed through two case studies. Furthermore, detailed fault smearing effect analysis of the proposed method is provided and compared with other identification methods. Based on the simulation results of two case studies, the superiority of the proposed method is explored. © 2012 American Institute of Chemical Engineers AIChE J, 2012 |
| doi_str_mv | 10.1002/aic.13776 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1671559446</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2813415011</sourcerecordid><originalsourceid>FETCH-LOGICAL-c4686-eb71a58392f8f18ed41ecc1d0bac653db3db644f403977cfa073b0be5208a0173</originalsourceid><addsrcrecordid>eNqFkU9r3DAQxUVpods0h34DQym0Bycay_rjY9i225CQXBL2KMbyuCj12luNl3a_fZVskkOhFARCo997vOEJ8Q7kCUhZnWIMJ6CsNS_EAnRtS91I_VIspJRQ5gG8Fm-Y7_Krsq5aiIvPOGPZIlNXDHEkTMUKd8wRx4JnnKnkLQYqNlNHQ9FPqej2I25iKLZpCsScf8Y4TymO39-KVz0OTMeP95G4_frlZvmtvLxenS_PLstQG2dKai2gdqqpeteDo64GCgE62WIwWnVtPqau-1qqxtrQo7SqlS3pSjqUYNWR-HjwzRF-7ohnv4kcaBhwpGnHHowFrZu6Nv9HK6csgFP36Pu_0Ltpl8a8iAfQkEOAcZn6dKBCmpgT9X6b4gbT3oP09w343IB_aCCzHx4dkQMOfcIxRH4WVMY47SqZudMD9ysOtP-3oT87Xz45lwdF5Jl-Pysw_fDGKqv9-mrlb1wj12t15VfqD0CHodE</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1151073168</pqid></control><display><type>article</type><title>Data-based linear Gaussian state-space model for dynamic process monitoring</title><source>Access via Wiley Online Library</source><creator>Wen, Qiaojun ; Ge, Zhiqiang ; Song, Zhihuan</creator><creatorcontrib>Wen, Qiaojun ; Ge, Zhiqiang ; Song, Zhihuan</creatorcontrib><description>This article develops a data‐based linear Gaussian state‐space model for monitoring of dynamic processes under noisy environment. The Kalman filter is introduced for construction of the linear Gaussian state‐space model, and an iterative expectation‐maximization algorithm is used for model parameters learning. With the incorporation of the dynamic data information, a new fault detection and identification approach is proposed. The feasibility and effectiveness of the two monitoring statistics in the new method are theoretically evaluated and further confirmed through two case studies. Furthermore, detailed fault smearing effect analysis of the proposed method is provided and compared with other identification methods. Based on the simulation results of two case studies, the superiority of the proposed method is explored. © 2012 American Institute of Chemical Engineers AIChE J, 2012</description><identifier>ISSN: 0001-1541</identifier><identifier>EISSN: 1547-5905</identifier><identifier>DOI: 10.1002/aic.13776</identifier><identifier>CODEN: AICEAC</identifier><language>eng</language><publisher>Hoboken: Wiley Subscription Services, Inc., A Wiley Company</publisher><subject>Algorithms ; Applied sciences ; Chemical engineering ; Computer simulation ; dynamic ; Dynamics ; Exact sciences and technology ; fault detection ; fault detection, fault identification ; Fault diagnosis ; fault identification ; Faults ; Feasibility ; Gaussian ; Kalman filters ; Learning ; linear Gaussian state-space model ; Mathematical models ; Monitoring ; Monitoring systems ; Normal distribution ; probabilistic ; Safety ; Simulation ; Statistics</subject><ispartof>AIChE journal, 2012-12, Vol.58 (12), p.3763-3776</ispartof><rights>Copyright © 2012 American Institute of Chemical Engineers (AIChE)</rights><rights>2014 INIST-CNRS</rights><rights>Copyright American Institute of Chemical Engineers Dec 2012</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4686-eb71a58392f8f18ed41ecc1d0bac653db3db644f403977cfa073b0be5208a0173</citedby><cites>FETCH-LOGICAL-c4686-eb71a58392f8f18ed41ecc1d0bac653db3db644f403977cfa073b0be5208a0173</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Faic.13776$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Faic.13776$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=26685820$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Wen, Qiaojun</creatorcontrib><creatorcontrib>Ge, Zhiqiang</creatorcontrib><creatorcontrib>Song, Zhihuan</creatorcontrib><title>Data-based linear Gaussian state-space model for dynamic process monitoring</title><title>AIChE journal</title><addtitle>AIChE J</addtitle><description>This article develops a data‐based linear Gaussian state‐space model for monitoring of dynamic processes under noisy environment. The Kalman filter is introduced for construction of the linear Gaussian state‐space model, and an iterative expectation‐maximization algorithm is used for model parameters learning. With the incorporation of the dynamic data information, a new fault detection and identification approach is proposed. The feasibility and effectiveness of the two monitoring statistics in the new method are theoretically evaluated and further confirmed through two case studies. Furthermore, detailed fault smearing effect analysis of the proposed method is provided and compared with other identification methods. Based on the simulation results of two case studies, the superiority of the proposed method is explored. © 2012 American Institute of Chemical Engineers AIChE J, 2012</description><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Chemical engineering</subject><subject>Computer simulation</subject><subject>dynamic</subject><subject>Dynamics</subject><subject>Exact sciences and technology</subject><subject>fault detection</subject><subject>fault detection, fault identification</subject><subject>Fault diagnosis</subject><subject>fault identification</subject><subject>Faults</subject><subject>Feasibility</subject><subject>Gaussian</subject><subject>Kalman filters</subject><subject>Learning</subject><subject>linear Gaussian state-space model</subject><subject>Mathematical models</subject><subject>Monitoring</subject><subject>Monitoring systems</subject><subject>Normal distribution</subject><subject>probabilistic</subject><subject>Safety</subject><subject>Simulation</subject><subject>Statistics</subject><issn>0001-1541</issn><issn>1547-5905</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><recordid>eNqFkU9r3DAQxUVpods0h34DQym0Bycay_rjY9i225CQXBL2KMbyuCj12luNl3a_fZVskkOhFARCo997vOEJ8Q7kCUhZnWIMJ6CsNS_EAnRtS91I_VIspJRQ5gG8Fm-Y7_Krsq5aiIvPOGPZIlNXDHEkTMUKd8wRx4JnnKnkLQYqNlNHQ9FPqej2I25iKLZpCsScf8Y4TymO39-KVz0OTMeP95G4_frlZvmtvLxenS_PLstQG2dKai2gdqqpeteDo64GCgE62WIwWnVtPqau-1qqxtrQo7SqlS3pSjqUYNWR-HjwzRF-7ohnv4kcaBhwpGnHHowFrZu6Nv9HK6csgFP36Pu_0Ltpl8a8iAfQkEOAcZn6dKBCmpgT9X6b4gbT3oP09w343IB_aCCzHx4dkQMOfcIxRH4WVMY47SqZudMD9ysOtP-3oT87Xz45lwdF5Jl-Pysw_fDGKqv9-mrlb1wj12t15VfqD0CHodE</recordid><startdate>201212</startdate><enddate>201212</enddate><creator>Wen, Qiaojun</creator><creator>Ge, Zhiqiang</creator><creator>Song, Zhihuan</creator><general>Wiley Subscription Services, Inc., A Wiley Company</general><general>Wiley</general><general>American Institute of Chemical Engineers</general><scope>BSCLL</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7U5</scope><scope>8FD</scope><scope>C1K</scope><scope>L7M</scope><scope>SOI</scope></search><sort><creationdate>201212</creationdate><title>Data-based linear Gaussian state-space model for dynamic process monitoring</title><author>Wen, Qiaojun ; Ge, Zhiqiang ; Song, Zhihuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4686-eb71a58392f8f18ed41ecc1d0bac653db3db644f403977cfa073b0be5208a0173</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Algorithms</topic><topic>Applied sciences</topic><topic>Chemical engineering</topic><topic>Computer simulation</topic><topic>dynamic</topic><topic>Dynamics</topic><topic>Exact sciences and technology</topic><topic>fault detection</topic><topic>fault detection, fault identification</topic><topic>Fault diagnosis</topic><topic>fault identification</topic><topic>Faults</topic><topic>Feasibility</topic><topic>Gaussian</topic><topic>Kalman filters</topic><topic>Learning</topic><topic>linear Gaussian state-space model</topic><topic>Mathematical models</topic><topic>Monitoring</topic><topic>Monitoring systems</topic><topic>Normal distribution</topic><topic>probabilistic</topic><topic>Safety</topic><topic>Simulation</topic><topic>Statistics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wen, Qiaojun</creatorcontrib><creatorcontrib>Ge, Zhiqiang</creatorcontrib><creatorcontrib>Song, Zhihuan</creatorcontrib><collection>Istex</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Environment Abstracts</collection><jtitle>AIChE journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wen, Qiaojun</au><au>Ge, Zhiqiang</au><au>Song, Zhihuan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Data-based linear Gaussian state-space model for dynamic process monitoring</atitle><jtitle>AIChE journal</jtitle><addtitle>AIChE J</addtitle><date>2012-12</date><risdate>2012</risdate><volume>58</volume><issue>12</issue><spage>3763</spage><epage>3776</epage><pages>3763-3776</pages><issn>0001-1541</issn><eissn>1547-5905</eissn><coden>AICEAC</coden><abstract>This article develops a data‐based linear Gaussian state‐space model for monitoring of dynamic processes under noisy environment. The Kalman filter is introduced for construction of the linear Gaussian state‐space model, and an iterative expectation‐maximization algorithm is used for model parameters learning. With the incorporation of the dynamic data information, a new fault detection and identification approach is proposed. The feasibility and effectiveness of the two monitoring statistics in the new method are theoretically evaluated and further confirmed through two case studies. Furthermore, detailed fault smearing effect analysis of the proposed method is provided and compared with other identification methods. Based on the simulation results of two case studies, the superiority of the proposed method is explored. © 2012 American Institute of Chemical Engineers AIChE J, 2012</abstract><cop>Hoboken</cop><pub>Wiley Subscription Services, Inc., A Wiley Company</pub><doi>10.1002/aic.13776</doi><tpages>14</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0001-1541 |
| ispartof | AIChE journal, 2012-12, Vol.58 (12), p.3763-3776 |
| issn | 0001-1541 1547-5905 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1671559446 |
| source | Access via Wiley Online Library |
| subjects | Algorithms Applied sciences Chemical engineering Computer simulation dynamic Dynamics Exact sciences and technology fault detection fault detection, fault identification Fault diagnosis fault identification Faults Feasibility Gaussian Kalman filters Learning linear Gaussian state-space model Mathematical models Monitoring Monitoring systems Normal distribution probabilistic Safety Simulation Statistics |
| title | Data-based linear Gaussian state-space model for dynamic process monitoring |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-03T13%3A43%3A30IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Data-based%20linear%20Gaussian%20state-space%20model%20for%20dynamic%20process%20monitoring&rft.jtitle=AIChE%20journal&rft.au=Wen,%20Qiaojun&rft.date=2012-12&rft.volume=58&rft.issue=12&rft.spage=3763&rft.epage=3776&rft.pages=3763-3776&rft.issn=0001-1541&rft.eissn=1547-5905&rft.coden=AICEAC&rft_id=info:doi/10.1002/aic.13776&rft_dat=%3Cproquest_cross%3E2813415011%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1151073168&rft_id=info:pmid/&rfr_iscdi=true |