Loading…
An application of support vector machine to detect anomalies in time series data
Maintaining the equipment in a good performance is mandatory for business. It needs to reduce the production cost, minimize downtime, maintain product quality, safety and reduce risks. The maintenance department shall predict whether the equipment may fail in near future to minimize downtime. Parame...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Conference Proceeding |
| Language: | English |
| Subjects: | |
| 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 | 2217 |
| creator | Satriawan, Yoga S. Saputro, Joko Slamet |
| description | Maintaining the equipment in a good performance is mandatory for business. It needs to reduce the production cost, minimize downtime, maintain product quality, safety and reduce risks. The maintenance department shall predict whether the equipment may fail in near future to minimize downtime. Parameters data which we used in this paper are temperature and vibration. We used anomaly detection to predict failure of the equipment. Anomaly detection is one of the methods which can be used to predict the failure. It detects anomalies in time series data with evenly time-spaced numerical values. Paper proposes to use Support Vector Machine method to detect anomalies. This testing was done by using Azure Machine Learning Development Studio simulation result shows that support vector machine method can detect anomalies with accuracy 0.906. |
| doi_str_mv | 10.1063/5.0000659 |
| format | conference_proceeding |
| fullrecord | <record><control><sourceid>proquest_scita</sourceid><recordid>TN_cdi_proquest_journals_2389371263</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2389371263</sourcerecordid><originalsourceid>FETCH-LOGICAL-p168t-be84efcadb222c93a62b79d5f56ffe0b0f99aa4065ef9d501a35548d1dc079bb3</originalsourceid><addsrcrecordid>eNp9kEtLAzEUhYMoWKsL_0HAnTA1j0lmsizFFxR0oeAuZPLAlM4kJpmC_96pLbjzbg738HEv5wBwjdECI07v2AJNw5k4ATPMGK4ajvkpmCEk6orU9OMcXOS8QYiIpmln4HU5QBXj1mtVfBhgcDCPMYZU4M7qEhLslf70g4UlQGPL5EE1hF5tvc3QD7D43sJs0341qqhLcObUNturo87B-8P92-qpWr88Pq-W6ypi3paqs21tnVamI4RoQRUnXSMMc4w7Z1GHnBBK1VMS6yYbYUUZq1uDjUaN6Do6BzeHuzGFr9HmIjdhTMP0UhLaCtpgwulE3R6orH35TShj8r1K3xIjuW9MMnls7D94F9IfKKNx9AenV2zk</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype><pqid>2389371263</pqid></control><display><type>conference_proceeding</type><title>An application of support vector machine to detect anomalies in time series data</title><source>American Institute of Physics:Jisc Collections:Transitional Journals Agreement 2021-23 (Reading list)</source><creator>Satriawan, Yoga S. ; Saputro, Joko Slamet</creator><contributor>Sutopo, Wahyudi ; Hisjam, Muhammad ; Apribowo, Chico Hermanu Brilianto ; Maghfiroh, Hari ; Ibrahim, Sutrisno ; Anwar, Miftahul ; Ibrahim, Muhammad Hamka</contributor><creatorcontrib>Satriawan, Yoga S. ; Saputro, Joko Slamet ; Sutopo, Wahyudi ; Hisjam, Muhammad ; Apribowo, Chico Hermanu Brilianto ; Maghfiroh, Hari ; Ibrahim, Sutrisno ; Anwar, Miftahul ; Ibrahim, Muhammad Hamka</creatorcontrib><description>Maintaining the equipment in a good performance is mandatory for business. It needs to reduce the production cost, minimize downtime, maintain product quality, safety and reduce risks. The maintenance department shall predict whether the equipment may fail in near future to minimize downtime. Parameters data which we used in this paper are temperature and vibration. We used anomaly detection to predict failure of the equipment. Anomaly detection is one of the methods which can be used to predict the failure. It detects anomalies in time series data with evenly time-spaced numerical values. Paper proposes to use Support Vector Machine method to detect anomalies. This testing was done by using Azure Machine Learning Development Studio simulation result shows that support vector machine method can detect anomalies with accuracy 0.906.</description><identifier>ISSN: 0094-243X</identifier><identifier>EISSN: 1551-7616</identifier><identifier>DOI: 10.1063/5.0000659</identifier><identifier>CODEN: APCPCS</identifier><language>eng</language><publisher>Melville: American Institute of Physics</publisher><subject>Anomalies ; Computer simulation ; Downtime ; Machine learning ; Product safety ; Production costs ; Support vector machines ; Time series</subject><ispartof>AIP Conference Proceedings, 2020, Vol.2217 (1)</ispartof><rights>Author(s)</rights><rights>2020 Author(s). Published by AIP Publishing.</rights><lds50>peer_reviewed</lds50><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>Sutopo, Wahyudi</contributor><contributor>Hisjam, Muhammad</contributor><contributor>Apribowo, Chico Hermanu Brilianto</contributor><contributor>Maghfiroh, Hari</contributor><contributor>Ibrahim, Sutrisno</contributor><contributor>Anwar, Miftahul</contributor><contributor>Ibrahim, Muhammad Hamka</contributor><creatorcontrib>Satriawan, Yoga S.</creatorcontrib><creatorcontrib>Saputro, Joko Slamet</creatorcontrib><title>An application of support vector machine to detect anomalies in time series data</title><title>AIP Conference Proceedings</title><description>Maintaining the equipment in a good performance is mandatory for business. It needs to reduce the production cost, minimize downtime, maintain product quality, safety and reduce risks. The maintenance department shall predict whether the equipment may fail in near future to minimize downtime. Parameters data which we used in this paper are temperature and vibration. We used anomaly detection to predict failure of the equipment. Anomaly detection is one of the methods which can be used to predict the failure. It detects anomalies in time series data with evenly time-spaced numerical values. Paper proposes to use Support Vector Machine method to detect anomalies. This testing was done by using Azure Machine Learning Development Studio simulation result shows that support vector machine method can detect anomalies with accuracy 0.906.</description><subject>Anomalies</subject><subject>Computer simulation</subject><subject>Downtime</subject><subject>Machine learning</subject><subject>Product safety</subject><subject>Production costs</subject><subject>Support vector machines</subject><subject>Time series</subject><issn>0094-243X</issn><issn>1551-7616</issn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2020</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNp9kEtLAzEUhYMoWKsL_0HAnTA1j0lmsizFFxR0oeAuZPLAlM4kJpmC_96pLbjzbg738HEv5wBwjdECI07v2AJNw5k4ATPMGK4ajvkpmCEk6orU9OMcXOS8QYiIpmln4HU5QBXj1mtVfBhgcDCPMYZU4M7qEhLslf70g4UlQGPL5EE1hF5tvc3QD7D43sJs0341qqhLcObUNturo87B-8P92-qpWr88Pq-W6ypi3paqs21tnVamI4RoQRUnXSMMc4w7Z1GHnBBK1VMS6yYbYUUZq1uDjUaN6Do6BzeHuzGFr9HmIjdhTMP0UhLaCtpgwulE3R6orH35TShj8r1K3xIjuW9MMnls7D94F9IfKKNx9AenV2zk</recordid><startdate>20200413</startdate><enddate>20200413</enddate><creator>Satriawan, Yoga S.</creator><creator>Saputro, Joko Slamet</creator><general>American Institute of Physics</general><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>20200413</creationdate><title>An application of support vector machine to detect anomalies in time series data</title><author>Satriawan, Yoga S. ; Saputro, Joko Slamet</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p168t-be84efcadb222c93a62b79d5f56ffe0b0f99aa4065ef9d501a35548d1dc079bb3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Anomalies</topic><topic>Computer simulation</topic><topic>Downtime</topic><topic>Machine learning</topic><topic>Product safety</topic><topic>Production costs</topic><topic>Support vector machines</topic><topic>Time series</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Satriawan, Yoga S.</creatorcontrib><creatorcontrib>Saputro, Joko Slamet</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>Satriawan, Yoga S.</au><au>Saputro, Joko Slamet</au><au>Sutopo, Wahyudi</au><au>Hisjam, Muhammad</au><au>Apribowo, Chico Hermanu Brilianto</au><au>Maghfiroh, Hari</au><au>Ibrahim, Sutrisno</au><au>Anwar, Miftahul</au><au>Ibrahim, Muhammad Hamka</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>An application of support vector machine to detect anomalies in time series data</atitle><btitle>AIP Conference Proceedings</btitle><date>2020-04-13</date><risdate>2020</risdate><volume>2217</volume><issue>1</issue><issn>0094-243X</issn><eissn>1551-7616</eissn><coden>APCPCS</coden><abstract>Maintaining the equipment in a good performance is mandatory for business. It needs to reduce the production cost, minimize downtime, maintain product quality, safety and reduce risks. The maintenance department shall predict whether the equipment may fail in near future to minimize downtime. Parameters data which we used in this paper are temperature and vibration. We used anomaly detection to predict failure of the equipment. Anomaly detection is one of the methods which can be used to predict the failure. It detects anomalies in time series data with evenly time-spaced numerical values. Paper proposes to use Support Vector Machine method to detect anomalies. This testing was done by using Azure Machine Learning Development Studio simulation result shows that support vector machine method can detect anomalies with accuracy 0.906.</abstract><cop>Melville</cop><pub>American Institute of Physics</pub><doi>10.1063/5.0000659</doi><tpages>5</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0094-243X |
| ispartof | AIP Conference Proceedings, 2020, Vol.2217 (1) |
| issn | 0094-243X 1551-7616 |
| language | eng |
| recordid | cdi_proquest_journals_2389371263 |
| source | American Institute of Physics:Jisc Collections:Transitional Journals Agreement 2021-23 (Reading list) |
| subjects | Anomalies Computer simulation Downtime Machine learning Product safety Production costs Support vector machines Time series |
| title | An application of support vector machine to detect anomalies in time series data |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-29T01%3A23%3A41IST&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=An%20application%20of%20support%20vector%20machine%20to%20detect%20anomalies%20in%20time%20series%20data&rft.btitle=AIP%20Conference%20Proceedings&rft.au=Satriawan,%20Yoga%20S.&rft.date=2020-04-13&rft.volume=2217&rft.issue=1&rft.issn=0094-243X&rft.eissn=1551-7616&rft.coden=APCPCS&rft_id=info:doi/10.1063/5.0000659&rft_dat=%3Cproquest_scita%3E2389371263%3C/proquest_scita%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-p168t-be84efcadb222c93a62b79d5f56ffe0b0f99aa4065ef9d501a35548d1dc079bb3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2389371263&rft_id=info:pmid/&rfr_iscdi=true |