Loading…
Implementation of System Operation Modes for Health Management and Failure Prognosis in Cyber-Physical Systems
Cyber-physical systems (CPSs) have sophisticated control mechanisms that help achieve optimal system operations and services. These mechanisms, imply considering multiple signal inputs in parallel, to timely respond to varying working conditions. Despite the advantages that control mechanisms convey...
Saved in:
Published in: | Sensors (Basel, Switzerland) Switzerland), 2020-04, Vol.20 (8), p.2429 |
---|---|
Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c469t-e21b283b74672aa94a7c7b97ca5bf4a155a85a7923c5f19bf68056ef382dc01f3 |
---|---|
cites | cdi_FETCH-LOGICAL-c469t-e21b283b74672aa94a7c7b97ca5bf4a155a85a7923c5f19bf68056ef382dc01f3 |
container_end_page | |
container_issue | 8 |
container_start_page | 2429 |
container_title | Sensors (Basel, Switzerland) |
container_volume | 20 |
creator | Ruiz-Arenas, Santiago Rusák, Zoltán Mejía-Gutiérrez, Ricardo Horváth, Imre |
description | Cyber-physical systems (CPSs) have sophisticated control mechanisms that help achieve optimal system operations and services. These mechanisms, imply considering multiple signal inputs in parallel, to timely respond to varying working conditions. Despite the advantages that control mechanisms convey, they bring new challenges in terms of failure prevention. The compensatory action the control exerts cause a fault masking effect, hampering fault diagnosis. Likewise, the multiple information inputs CPSs have to process can affect the timely system response to faults. This article proposes a failure prognosis method, which combines time series-based forecasting methods with statistically based classification techniques in order to investigate system degradation and failure forming on system levels. This method utilizes a new approach based on the concept of the system operation mode (SOM) that offers a novel perspective for health management that allows monitoring the system behavior, through the frequency and duration of SOMs. Validation of this method was conducted by systematically injecting faults in a cyber-physical greenhouse testbed. The obtained results demonstrate that the degradation and fault forming process can be monitored by analyzing the changes of the frequency and duration of SOMs. These indicators made possible to estimate the time to failure caused by various failures in the conducted experiments. |
doi_str_mv | 10.3390/s20082429 |
format | article |
fullrecord | <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_a18224e31b084a49a785b031da23adca</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_a18224e31b084a49a785b031da23adca</doaj_id><sourcerecordid>2395474339</sourcerecordid><originalsourceid>FETCH-LOGICAL-c469t-e21b283b74672aa94a7c7b97ca5bf4a155a85a7923c5f19bf68056ef382dc01f3</originalsourceid><addsrcrecordid>eNpdkl1rFDEUhoNYbK1e-Ack4E29mJrPSXIjyGLtQksL6nU4k8nsZplJ1mRG2H_faXddWq8STp48nJy8CH2g5JJzQ74URohmgplX6IwKJirNGHn9bH-K3payIYRxzvUbdMoZF0IReYbictj2fvBxhDGkiFOHf-7K6Ad8t_V5X7tNrS-4Sxlfe-jHNb6FCKunSxhii68g9FP2-D6nVUwlFBwiXuwan6v79a4EB_1BWt6hkw764t8f1nP0--r7r8V1dXP3Y7n4dlM5UZux8ow2TPNGiVoxACNAOdUY5UA2nQAqJWgJyjDuZEdN09WayNp3XLPWEdrxc7Tce9sEG7vNYYC8swmCfSqkvLKQx-B6b4HOExKe04ZoAcKA0rIhnLbAOLQOZtfXvWs7NYNv3fzsDP0L6cuTGNZ2lf5axaiRRM-Ci4Mgpz-TL6MdQnG-7yH6NBXLuKk5kUTJGf30H7pJU47zqB4pKZSYP3ymPu8pl1Mp2XfHZiixj4mwx0TM7Mfn3R_JfxHgD3TUsTY</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2395474339</pqid></control><display><type>article</type><title>Implementation of System Operation Modes for Health Management and Failure Prognosis in Cyber-Physical Systems</title><source>ProQuest - Publicly Available Content Database</source><source>PubMed Central</source><creator>Ruiz-Arenas, Santiago ; Rusák, Zoltán ; Mejía-Gutiérrez, Ricardo ; Horváth, Imre</creator><creatorcontrib>Ruiz-Arenas, Santiago ; Rusák, Zoltán ; Mejía-Gutiérrez, Ricardo ; Horváth, Imre</creatorcontrib><description>Cyber-physical systems (CPSs) have sophisticated control mechanisms that help achieve optimal system operations and services. These mechanisms, imply considering multiple signal inputs in parallel, to timely respond to varying working conditions. Despite the advantages that control mechanisms convey, they bring new challenges in terms of failure prevention. The compensatory action the control exerts cause a fault masking effect, hampering fault diagnosis. Likewise, the multiple information inputs CPSs have to process can affect the timely system response to faults. This article proposes a failure prognosis method, which combines time series-based forecasting methods with statistically based classification techniques in order to investigate system degradation and failure forming on system levels. This method utilizes a new approach based on the concept of the system operation mode (SOM) that offers a novel perspective for health management that allows monitoring the system behavior, through the frequency and duration of SOMs. Validation of this method was conducted by systematically injecting faults in a cyber-physical greenhouse testbed. The obtained results demonstrate that the degradation and fault forming process can be monitored by analyzing the changes of the frequency and duration of SOMs. These indicators made possible to estimate the time to failure caused by various failures in the conducted experiments.</description><identifier>ISSN: 1424-8220</identifier><identifier>EISSN: 1424-8220</identifier><identifier>DOI: 10.3390/s20082429</identifier><identifier>PMID: 32344705</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Adaptation ; Cyber-physical systems ; Failure ; Failure prevention ; failure prognosis ; Fault diagnosis ; health management ; Masking ; Methods ; Probability distribution ; Prognosis ; Statistical methods ; system maintenance ; system operation modes ; system reliability ; Trends ; Useful life</subject><ispartof>Sensors (Basel, Switzerland), 2020-04, Vol.20 (8), p.2429</ispartof><rights>2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2020 by the authors. 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c469t-e21b283b74672aa94a7c7b97ca5bf4a155a85a7923c5f19bf68056ef382dc01f3</citedby><cites>FETCH-LOGICAL-c469t-e21b283b74672aa94a7c7b97ca5bf4a155a85a7923c5f19bf68056ef382dc01f3</cites><orcidid>0000-0002-4018-7370 ; 0000-0002-0855-7001 ; 0000-0002-6999-5881 ; 0000-0002-6008-0570</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2395474339/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2395474339?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,74998</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32344705$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ruiz-Arenas, Santiago</creatorcontrib><creatorcontrib>Rusák, Zoltán</creatorcontrib><creatorcontrib>Mejía-Gutiérrez, Ricardo</creatorcontrib><creatorcontrib>Horváth, Imre</creatorcontrib><title>Implementation of System Operation Modes for Health Management and Failure Prognosis in Cyber-Physical Systems</title><title>Sensors (Basel, Switzerland)</title><addtitle>Sensors (Basel)</addtitle><description>Cyber-physical systems (CPSs) have sophisticated control mechanisms that help achieve optimal system operations and services. These mechanisms, imply considering multiple signal inputs in parallel, to timely respond to varying working conditions. Despite the advantages that control mechanisms convey, they bring new challenges in terms of failure prevention. The compensatory action the control exerts cause a fault masking effect, hampering fault diagnosis. Likewise, the multiple information inputs CPSs have to process can affect the timely system response to faults. This article proposes a failure prognosis method, which combines time series-based forecasting methods with statistically based classification techniques in order to investigate system degradation and failure forming on system levels. This method utilizes a new approach based on the concept of the system operation mode (SOM) that offers a novel perspective for health management that allows monitoring the system behavior, through the frequency and duration of SOMs. Validation of this method was conducted by systematically injecting faults in a cyber-physical greenhouse testbed. The obtained results demonstrate that the degradation and fault forming process can be monitored by analyzing the changes of the frequency and duration of SOMs. These indicators made possible to estimate the time to failure caused by various failures in the conducted experiments.</description><subject>Adaptation</subject><subject>Cyber-physical systems</subject><subject>Failure</subject><subject>Failure prevention</subject><subject>failure prognosis</subject><subject>Fault diagnosis</subject><subject>health management</subject><subject>Masking</subject><subject>Methods</subject><subject>Probability distribution</subject><subject>Prognosis</subject><subject>Statistical methods</subject><subject>system maintenance</subject><subject>system operation modes</subject><subject>system reliability</subject><subject>Trends</subject><subject>Useful life</subject><issn>1424-8220</issn><issn>1424-8220</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpdkl1rFDEUhoNYbK1e-Ack4E29mJrPSXIjyGLtQksL6nU4k8nsZplJ1mRG2H_faXddWq8STp48nJy8CH2g5JJzQ74URohmgplX6IwKJirNGHn9bH-K3payIYRxzvUbdMoZF0IReYbictj2fvBxhDGkiFOHf-7K6Ad8t_V5X7tNrS-4Sxlfe-jHNb6FCKunSxhii68g9FP2-D6nVUwlFBwiXuwan6v79a4EB_1BWt6hkw764t8f1nP0--r7r8V1dXP3Y7n4dlM5UZux8ow2TPNGiVoxACNAOdUY5UA2nQAqJWgJyjDuZEdN09WayNp3XLPWEdrxc7Tce9sEG7vNYYC8swmCfSqkvLKQx-B6b4HOExKe04ZoAcKA0rIhnLbAOLQOZtfXvWs7NYNv3fzsDP0L6cuTGNZ2lf5axaiRRM-Ci4Mgpz-TL6MdQnG-7yH6NBXLuKk5kUTJGf30H7pJU47zqB4pKZSYP3ymPu8pl1Mp2XfHZiixj4mwx0TM7Mfn3R_JfxHgD3TUsTY</recordid><startdate>20200424</startdate><enddate>20200424</enddate><creator>Ruiz-Arenas, Santiago</creator><creator>Rusák, Zoltán</creator><creator>Mejía-Gutiérrez, Ricardo</creator><creator>Horváth, Imre</creator><general>MDPI AG</general><general>MDPI</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-4018-7370</orcidid><orcidid>https://orcid.org/0000-0002-0855-7001</orcidid><orcidid>https://orcid.org/0000-0002-6999-5881</orcidid><orcidid>https://orcid.org/0000-0002-6008-0570</orcidid></search><sort><creationdate>20200424</creationdate><title>Implementation of System Operation Modes for Health Management and Failure Prognosis in Cyber-Physical Systems</title><author>Ruiz-Arenas, Santiago ; Rusák, Zoltán ; Mejía-Gutiérrez, Ricardo ; Horváth, Imre</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c469t-e21b283b74672aa94a7c7b97ca5bf4a155a85a7923c5f19bf68056ef382dc01f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adaptation</topic><topic>Cyber-physical systems</topic><topic>Failure</topic><topic>Failure prevention</topic><topic>failure prognosis</topic><topic>Fault diagnosis</topic><topic>health management</topic><topic>Masking</topic><topic>Methods</topic><topic>Probability distribution</topic><topic>Prognosis</topic><topic>Statistical methods</topic><topic>system maintenance</topic><topic>system operation modes</topic><topic>system reliability</topic><topic>Trends</topic><topic>Useful life</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ruiz-Arenas, Santiago</creatorcontrib><creatorcontrib>Rusák, Zoltán</creatorcontrib><creatorcontrib>Mejía-Gutiérrez, Ricardo</creatorcontrib><creatorcontrib>Horváth, Imre</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>ProQuest - Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Sensors (Basel, Switzerland)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ruiz-Arenas, Santiago</au><au>Rusák, Zoltán</au><au>Mejía-Gutiérrez, Ricardo</au><au>Horváth, Imre</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Implementation of System Operation Modes for Health Management and Failure Prognosis in Cyber-Physical Systems</atitle><jtitle>Sensors (Basel, Switzerland)</jtitle><addtitle>Sensors (Basel)</addtitle><date>2020-04-24</date><risdate>2020</risdate><volume>20</volume><issue>8</issue><spage>2429</spage><pages>2429-</pages><issn>1424-8220</issn><eissn>1424-8220</eissn><abstract>Cyber-physical systems (CPSs) have sophisticated control mechanisms that help achieve optimal system operations and services. These mechanisms, imply considering multiple signal inputs in parallel, to timely respond to varying working conditions. Despite the advantages that control mechanisms convey, they bring new challenges in terms of failure prevention. The compensatory action the control exerts cause a fault masking effect, hampering fault diagnosis. Likewise, the multiple information inputs CPSs have to process can affect the timely system response to faults. This article proposes a failure prognosis method, which combines time series-based forecasting methods with statistically based classification techniques in order to investigate system degradation and failure forming on system levels. This method utilizes a new approach based on the concept of the system operation mode (SOM) that offers a novel perspective for health management that allows monitoring the system behavior, through the frequency and duration of SOMs. Validation of this method was conducted by systematically injecting faults in a cyber-physical greenhouse testbed. The obtained results demonstrate that the degradation and fault forming process can be monitored by analyzing the changes of the frequency and duration of SOMs. These indicators made possible to estimate the time to failure caused by various failures in the conducted experiments.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>32344705</pmid><doi>10.3390/s20082429</doi><orcidid>https://orcid.org/0000-0002-4018-7370</orcidid><orcidid>https://orcid.org/0000-0002-0855-7001</orcidid><orcidid>https://orcid.org/0000-0002-6999-5881</orcidid><orcidid>https://orcid.org/0000-0002-6008-0570</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1424-8220 |
ispartof | Sensors (Basel, Switzerland), 2020-04, Vol.20 (8), p.2429 |
issn | 1424-8220 1424-8220 |
language | eng |
recordid | cdi_doaj_primary_oai_doaj_org_article_a18224e31b084a49a785b031da23adca |
source | ProQuest - Publicly Available Content Database; PubMed Central |
subjects | Adaptation Cyber-physical systems Failure Failure prevention failure prognosis Fault diagnosis health management Masking Methods Probability distribution Prognosis Statistical methods system maintenance system operation modes system reliability Trends Useful life |
title | Implementation of System Operation Modes for Health Management and Failure Prognosis in Cyber-Physical Systems |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-08T03%3A19%3A16IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Implementation%20of%20System%20Operation%20Modes%20for%20Health%20Management%20and%20Failure%20Prognosis%20in%20Cyber-Physical%20Systems&rft.jtitle=Sensors%20(Basel,%20Switzerland)&rft.au=Ruiz-Arenas,%20Santiago&rft.date=2020-04-24&rft.volume=20&rft.issue=8&rft.spage=2429&rft.pages=2429-&rft.issn=1424-8220&rft.eissn=1424-8220&rft_id=info:doi/10.3390/s20082429&rft_dat=%3Cproquest_doaj_%3E2395474339%3C/proquest_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c469t-e21b283b74672aa94a7c7b97ca5bf4a155a85a7923c5f19bf68056ef382dc01f3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2395474339&rft_id=info:pmid/32344705&rfr_iscdi=true |