Algorithm to detect growing gas leakage based on a statistical criterion

The current commercial production technologies often involve the generation and pipeline transportation of gaseous substances in utilities and premises. Airborne concentration is a quantitative gas leakage characteristic, which may increase up to threshold values causing danger to humans and the env...

Full description

Saved in:
Bibliographic Details
Main Authors: Prokofiev, Oleg V., Savochkin, Alexander E.
Format: Conference Proceeding
Language:English
Subjects:
Abnormalities
Algorithms
Criteria
Impurities
Leakage
Time series
Trends
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page
container_issue 1
container_start_page
container_title
container_volume 2402
creator Prokofiev, Oleg V.
Savochkin, Alexander E.
description The current commercial production technologies often involve the generation and pipeline transportation of gaseous substances in utilities and premises. Airborne concentration is a quantitative gas leakage characteristic, which may increase up to threshold values causing danger to humans and the environment and creating an emergency. Herein, changing the airborne concentration of impurities is described by a time series model. The possibility of creating an algorithm to automatically search for a trend break and estimate a new trend slope as a sign of emerging gas leakage has been studied. The authors propose a new way of applying a trend break as a criterion for detecting abnormalities. This allows identifying an event that leads to growing gas leakage. The authors have tested an algorithm that allows detecting new trends in the time series and predicting rapidly approaching threshold gas impurity concentration.
doi_str_mv 10.1063/5.0073462
format conference_proceeding
fullrecord <record><control><sourceid>proquest_scita</sourceid><recordid>TN_cdi_scitation_primary_10_1063_5_0073462</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2597589292</sourcerecordid><originalsourceid>FETCH-LOGICAL-p2032-99e59e24a1994e6b6492f0a0633dce4c449852482d2b3bb56953f75840d363203</originalsourceid><addsrcrecordid>eNp9kE1LwzAcxoMoWKcHv0HAm9CZ_PPS5jiGOmHgRcFbSNu0ZnZNTTLFb2_HBt48PZff88KD0DUlc0okuxNzQgrGJZygjApB80JSeYoyQhTPgbO3c3QR44YQUEVRZmi16DsfXHrf4uRxY5OtE-6C_3ZDhzsTcW_Nh-ksrky0DfYDNjgmk1xMrjY9rievDc4Pl-isNX20V0edodeH-5flKl8_Pz4tF-t8BMIgV8oKZYEbqhS3spJcQUvMNJ01teU156oUwEtooGJVJaQSrC1EyUnDJJsiZujmkDsG_7mzMemN34VhqtQg1EQqUDBRtwcq1m6_1g96DG5rwo_-8kELfXxJj037H0yJ3t_6Z2C_Zp9nkA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype><pqid>2597589292</pqid></control><display><type>conference_proceeding</type><title>Algorithm to detect growing gas leakage based on a statistical criterion</title><source>American Institute of Physics:Jisc Collections:Transitional Journals Agreement 2021-23 (Reading list)</source><creator>Prokofiev, Oleg V. ; Savochkin, Alexander E.</creator><contributor>Kovalev, Igor V. ; Stupina, Alena A. ; Voroshilova, Anna A.</contributor><creatorcontrib>Prokofiev, Oleg V. ; Savochkin, Alexander E. ; Kovalev, Igor V. ; Stupina, Alena A. ; Voroshilova, Anna A.</creatorcontrib><description>The current commercial production technologies often involve the generation and pipeline transportation of gaseous substances in utilities and premises. Airborne concentration is a quantitative gas leakage characteristic, which may increase up to threshold values causing danger to humans and the environment and creating an emergency. Herein, changing the airborne concentration of impurities is described by a time series model. The possibility of creating an algorithm to automatically search for a trend break and estimate a new trend slope as a sign of emerging gas leakage has been studied. The authors propose a new way of applying a trend break as a criterion for detecting abnormalities. This allows identifying an event that leads to growing gas leakage. The authors have tested an algorithm that allows detecting new trends in the time series and predicting rapidly approaching threshold gas impurity concentration.</description><identifier>ISSN: 0094-243X</identifier><identifier>EISSN: 1551-7616</identifier><identifier>DOI: 10.1063/5.0073462</identifier><identifier>CODEN: APCPCS</identifier><language>eng</language><publisher>Melville: American Institute of Physics</publisher><subject>Abnormalities ; Algorithms ; Criteria ; Impurities ; Leakage ; Time series ; Trends</subject><ispartof>AIP conference proceedings, 2021, Vol.2402 (1)</ispartof><rights>Author(s)</rights><rights>2021 Author(s). Published by AIP Publishing.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>309,310,314,780,784,789,790,23930,23931,25140,27924,27925</link.rule.ids></links><search><contributor>Kovalev, Igor V.</contributor><contributor>Stupina, Alena A.</contributor><contributor>Voroshilova, Anna A.</contributor><creatorcontrib>Prokofiev, Oleg V.</creatorcontrib><creatorcontrib>Savochkin, Alexander E.</creatorcontrib><title>Algorithm to detect growing gas leakage based on a statistical criterion</title><title>AIP conference proceedings</title><description>The current commercial production technologies often involve the generation and pipeline transportation of gaseous substances in utilities and premises. Airborne concentration is a quantitative gas leakage characteristic, which may increase up to threshold values causing danger to humans and the environment and creating an emergency. Herein, changing the airborne concentration of impurities is described by a time series model. The possibility of creating an algorithm to automatically search for a trend break and estimate a new trend slope as a sign of emerging gas leakage has been studied. The authors propose a new way of applying a trend break as a criterion for detecting abnormalities. This allows identifying an event that leads to growing gas leakage. The authors have tested an algorithm that allows detecting new trends in the time series and predicting rapidly approaching threshold gas impurity concentration.</description><subject>Abnormalities</subject><subject>Algorithms</subject><subject>Criteria</subject><subject>Impurities</subject><subject>Leakage</subject><subject>Time series</subject><subject>Trends</subject><issn>0094-243X</issn><issn>1551-7616</issn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2021</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNp9kE1LwzAcxoMoWKcHv0HAm9CZ_PPS5jiGOmHgRcFbSNu0ZnZNTTLFb2_HBt48PZff88KD0DUlc0okuxNzQgrGJZygjApB80JSeYoyQhTPgbO3c3QR44YQUEVRZmi16DsfXHrf4uRxY5OtE-6C_3ZDhzsTcW_Nh-ksrky0DfYDNjgmk1xMrjY9rievDc4Pl-isNX20V0edodeH-5flKl8_Pz4tF-t8BMIgV8oKZYEbqhS3spJcQUvMNJ01teU156oUwEtooGJVJaQSrC1EyUnDJJsiZujmkDsG_7mzMemN34VhqtQg1EQqUDBRtwcq1m6_1g96DG5rwo_-8kELfXxJj037H0yJ3t_6Z2C_Zp9nkA</recordid><startdate>20211115</startdate><enddate>20211115</enddate><creator>Prokofiev, Oleg V.</creator><creator>Savochkin, Alexander E.</creator><general>American Institute of Physics</general><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>20211115</creationdate><title>Algorithm to detect growing gas leakage based on a statistical criterion</title><author>Prokofiev, Oleg V. ; Savochkin, Alexander E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p2032-99e59e24a1994e6b6492f0a0633dce4c449852482d2b3bb56953f75840d363203</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Abnormalities</topic><topic>Algorithms</topic><topic>Criteria</topic><topic>Impurities</topic><topic>Leakage</topic><topic>Time series</topic><topic>Trends</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Prokofiev, Oleg V.</creatorcontrib><creatorcontrib>Savochkin, Alexander E.</creatorcontrib><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Prokofiev, Oleg V.</au><au>Savochkin, Alexander E.</au><au>Kovalev, Igor V.</au><au>Stupina, Alena A.</au><au>Voroshilova, Anna A.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Algorithm to detect growing gas leakage based on a statistical criterion</atitle><btitle>AIP conference proceedings</btitle><date>2021-11-15</date><risdate>2021</risdate><volume>2402</volume><issue>1</issue><issn>0094-243X</issn><eissn>1551-7616</eissn><coden>APCPCS</coden><abstract>The current commercial production technologies often involve the generation and pipeline transportation of gaseous substances in utilities and premises. Airborne concentration is a quantitative gas leakage characteristic, which may increase up to threshold values causing danger to humans and the environment and creating an emergency. Herein, changing the airborne concentration of impurities is described by a time series model. The possibility of creating an algorithm to automatically search for a trend break and estimate a new trend slope as a sign of emerging gas leakage has been studied. The authors propose a new way of applying a trend break as a criterion for detecting abnormalities. This allows identifying an event that leads to growing gas leakage. The authors have tested an algorithm that allows detecting new trends in the time series and predicting rapidly approaching threshold gas impurity concentration.</abstract><cop>Melville</cop><pub>American Institute of Physics</pub><doi>10.1063/5.0073462</doi><tpages>7</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0094-243X
ispartof AIP conference proceedings, 2021, Vol.2402 (1)
issn 0094-243X
1551-7616
language eng
recordid cdi_scitation_primary_10_1063_5_0073462
source American Institute of Physics:Jisc Collections:Transitional Journals Agreement 2021-23 (Reading list)
subjects Abnormalities
Algorithms
Criteria
Impurities
Leakage
Time series
Trends
title Algorithm to detect growing gas leakage based on a statistical criterion
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-08T04%3A49%3A59IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_scita&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Algorithm%20to%20detect%20growing%20gas%20leakage%20based%20on%20a%20statistical%20criterion&rft.btitle=AIP%20conference%20proceedings&rft.au=Prokofiev,%20Oleg%20V.&rft.date=2021-11-15&rft.volume=2402&rft.issue=1&rft.issn=0094-243X&rft.eissn=1551-7616&rft.coden=APCPCS&rft_id=info:doi/10.1063/5.0073462&rft_dat=%3Cproquest_scita%3E2597589292%3C/proquest_scita%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-p2032-99e59e24a1994e6b6492f0a0633dce4c449852482d2b3bb56953f75840d363203%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2597589292&rft_id=info:pmid/&rfr_iscdi=true