Process mining on machine event logs for profiling abnormal behaviour and root cause analysis

Process mining is a set of techniques in the field of process management that have primarily been used to analyse business processes, for example for the optimisation of enterprise resources. In this research, the feasibility of using process mining techniques for the analysis of event data from mac...

Full description

Saved in:
Bibliographic Details
Published in:Annales des télécommunications 2020-10, Vol.75 (9-10), p.563-572
Main Authors: Maeyens, Jonas, Vorstermans, Annemie, Verbeke, Mathias
Format: Article
Language:English
Subjects:
Business process management
Circuits
Communications Engineering
Computer Communication Networks
Engineering
Information and Communication
Information Systems and Communication Service
Networks
Optimization
R & D/Technology Policy
Root cause analysis
Signal,Image and Speech Processing
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-c363t-7263423e5c16ec0b0c5c27bb6cca9021a8f3ed55e0e2e1deeaf65668d056ba593
cites cdi_FETCH-LOGICAL-c363t-7263423e5c16ec0b0c5c27bb6cca9021a8f3ed55e0e2e1deeaf65668d056ba593
container_end_page 572
container_issue 9-10
container_start_page 563
container_title Annales des télécommunications
container_volume 75
creator Maeyens, Jonas
Vorstermans, Annemie
Verbeke, Mathias
description Process mining is a set of techniques in the field of process management that have primarily been used to analyse business processes, for example for the optimisation of enterprise resources. In this research, the feasibility of using process mining techniques for the analysis of event data from machine logs is investigated. A novel methodology, based on process mining, for profiling abnormal machine behaviour is proposed. Firstly, a process model is constructed from the event logs of the healthy machines. This model can subsequently be used as a benchmark to compare process models of other machines by means of conformance checking. This comparison results in a set of conformance scores related to the structure of the model and other more complex aspects such as the differences in duration of particular traces, the time spent in individual events, and the relative path frequency. The identified differences can subsequently be used as a basis for root cause analysis. The proposed approach is evaluated on a real-world industrial data set from the renewable energy domain, more specifically event logs of a fleet of inverters from several solar plants.
doi_str_mv 10.1007/s12243-020-00809-9
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2473804553</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2473804553</sourcerecordid><originalsourceid>FETCH-LOGICAL-c363t-7263423e5c16ec0b0c5c27bb6cca9021a8f3ed55e0e2e1deeaf65668d056ba593</originalsourceid><addsrcrecordid>eNp9kE1LxDAURYMoOI7-AVcB19GXpEnbpQx-wYAudCkhTV9nOrTJmHQG5t_bsYI7V48H514uh5BrDrccIL9LXIhMMhDAAAooWXlCZrxUBStlqU7JDAAky2SWn5OLlDYAGnKlZuTzLQaHKdG-9a1f0eBpb9269Uhxj36gXVgl2oRItzE0bXdkbOVD7G1HK1zbfRt2kVpf0xjCQJ3dJRxf2x1Smy7JWWO7hFe_d04-Hh_eF89s-fr0srhfMie1HFgutMyEROW4RgcVOOVEXlXaOVuC4LZoJNZKIaBAXiPaRiutixqUrqwq5ZzcTL3jyK8dpsFsxlXjiGRElssCMqXkSImJcjGkFLEx29j2Nh4MB3PUaCaNZtRofjSaY7WcQmmE_QrjX_U_qW-1zHaO</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2473804553</pqid></control><display><type>article</type><title>Process mining on machine event logs for profiling abnormal behaviour and root cause analysis</title><source>Springer Link</source><creator>Maeyens, Jonas ; Vorstermans, Annemie ; Verbeke, Mathias</creator><creatorcontrib>Maeyens, Jonas ; Vorstermans, Annemie ; Verbeke, Mathias</creatorcontrib><description>Process mining is a set of techniques in the field of process management that have primarily been used to analyse business processes, for example for the optimisation of enterprise resources. In this research, the feasibility of using process mining techniques for the analysis of event data from machine logs is investigated. A novel methodology, based on process mining, for profiling abnormal machine behaviour is proposed. Firstly, a process model is constructed from the event logs of the healthy machines. This model can subsequently be used as a benchmark to compare process models of other machines by means of conformance checking. This comparison results in a set of conformance scores related to the structure of the model and other more complex aspects such as the differences in duration of particular traces, the time spent in individual events, and the relative path frequency. The identified differences can subsequently be used as a basis for root cause analysis. The proposed approach is evaluated on a real-world industrial data set from the renewable energy domain, more specifically event logs of a fleet of inverters from several solar plants.</description><identifier>ISSN: 0003-4347</identifier><identifier>EISSN: 1958-9395</identifier><identifier>DOI: 10.1007/s12243-020-00809-9</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Business process management ; Circuits ; Communications Engineering ; Computer Communication Networks ; Engineering ; Information and Communication ; Information Systems and Communication Service ; Networks ; Optimization ; R &amp; D/Technology Policy ; Root cause analysis ; Signal,Image and Speech Processing</subject><ispartof>Annales des télécommunications, 2020-10, Vol.75 (9-10), p.563-572</ispartof><rights>Institut Mines-Télécom and Springer Nature Switzerland AG 2020</rights><rights>Institut Mines-Télécom and Springer Nature Switzerland AG 2020.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c363t-7263423e5c16ec0b0c5c27bb6cca9021a8f3ed55e0e2e1deeaf65668d056ba593</citedby><cites>FETCH-LOGICAL-c363t-7263423e5c16ec0b0c5c27bb6cca9021a8f3ed55e0e2e1deeaf65668d056ba593</cites><orcidid>0000-0001-8297-6071</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,786,790,27957,27958</link.rule.ids></links><search><creatorcontrib>Maeyens, Jonas</creatorcontrib><creatorcontrib>Vorstermans, Annemie</creatorcontrib><creatorcontrib>Verbeke, Mathias</creatorcontrib><title>Process mining on machine event logs for profiling abnormal behaviour and root cause analysis</title><title>Annales des télécommunications</title><addtitle>Ann. Telecommun</addtitle><description>Process mining is a set of techniques in the field of process management that have primarily been used to analyse business processes, for example for the optimisation of enterprise resources. In this research, the feasibility of using process mining techniques for the analysis of event data from machine logs is investigated. A novel methodology, based on process mining, for profiling abnormal machine behaviour is proposed. Firstly, a process model is constructed from the event logs of the healthy machines. This model can subsequently be used as a benchmark to compare process models of other machines by means of conformance checking. This comparison results in a set of conformance scores related to the structure of the model and other more complex aspects such as the differences in duration of particular traces, the time spent in individual events, and the relative path frequency. The identified differences can subsequently be used as a basis for root cause analysis. The proposed approach is evaluated on a real-world industrial data set from the renewable energy domain, more specifically event logs of a fleet of inverters from several solar plants.</description><subject>Business process management</subject><subject>Circuits</subject><subject>Communications Engineering</subject><subject>Computer Communication Networks</subject><subject>Engineering</subject><subject>Information and Communication</subject><subject>Information Systems and Communication Service</subject><subject>Networks</subject><subject>Optimization</subject><subject>R &amp; D/Technology Policy</subject><subject>Root cause analysis</subject><subject>Signal,Image and Speech Processing</subject><issn>0003-4347</issn><issn>1958-9395</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LxDAURYMoOI7-AVcB19GXpEnbpQx-wYAudCkhTV9nOrTJmHQG5t_bsYI7V48H514uh5BrDrccIL9LXIhMMhDAAAooWXlCZrxUBStlqU7JDAAky2SWn5OLlDYAGnKlZuTzLQaHKdG-9a1f0eBpb9269Uhxj36gXVgl2oRItzE0bXdkbOVD7G1HK1zbfRt2kVpf0xjCQJ3dJRxf2x1Smy7JWWO7hFe_d04-Hh_eF89s-fr0srhfMie1HFgutMyEROW4RgcVOOVEXlXaOVuC4LZoJNZKIaBAXiPaRiutixqUrqwq5ZzcTL3jyK8dpsFsxlXjiGRElssCMqXkSImJcjGkFLEx29j2Nh4MB3PUaCaNZtRofjSaY7WcQmmE_QrjX_U_qW-1zHaO</recordid><startdate>20201001</startdate><enddate>20201001</enddate><creator>Maeyens, Jonas</creator><creator>Vorstermans, Annemie</creator><creator>Verbeke, Mathias</creator><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0001-8297-6071</orcidid></search><sort><creationdate>20201001</creationdate><title>Process mining on machine event logs for profiling abnormal behaviour and root cause analysis</title><author>Maeyens, Jonas ; Vorstermans, Annemie ; Verbeke, Mathias</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c363t-7263423e5c16ec0b0c5c27bb6cca9021a8f3ed55e0e2e1deeaf65668d056ba593</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Business process management</topic><topic>Circuits</topic><topic>Communications Engineering</topic><topic>Computer Communication Networks</topic><topic>Engineering</topic><topic>Information and Communication</topic><topic>Information Systems and Communication Service</topic><topic>Networks</topic><topic>Optimization</topic><topic>R &amp; D/Technology Policy</topic><topic>Root cause analysis</topic><topic>Signal,Image and Speech Processing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Maeyens, Jonas</creatorcontrib><creatorcontrib>Vorstermans, Annemie</creatorcontrib><creatorcontrib>Verbeke, Mathias</creatorcontrib><collection>CrossRef</collection><jtitle>Annales des télécommunications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Maeyens, Jonas</au><au>Vorstermans, Annemie</au><au>Verbeke, Mathias</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Process mining on machine event logs for profiling abnormal behaviour and root cause analysis</atitle><jtitle>Annales des télécommunications</jtitle><stitle>Ann. Telecommun</stitle><date>2020-10-01</date><risdate>2020</risdate><volume>75</volume><issue>9-10</issue><spage>563</spage><epage>572</epage><pages>563-572</pages><issn>0003-4347</issn><eissn>1958-9395</eissn><abstract>Process mining is a set of techniques in the field of process management that have primarily been used to analyse business processes, for example for the optimisation of enterprise resources. In this research, the feasibility of using process mining techniques for the analysis of event data from machine logs is investigated. A novel methodology, based on process mining, for profiling abnormal machine behaviour is proposed. Firstly, a process model is constructed from the event logs of the healthy machines. This model can subsequently be used as a benchmark to compare process models of other machines by means of conformance checking. This comparison results in a set of conformance scores related to the structure of the model and other more complex aspects such as the differences in duration of particular traces, the time spent in individual events, and the relative path frequency. The identified differences can subsequently be used as a basis for root cause analysis. The proposed approach is evaluated on a real-world industrial data set from the renewable energy domain, more specifically event logs of a fleet of inverters from several solar plants.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><doi>10.1007/s12243-020-00809-9</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0001-8297-6071</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0003-4347
ispartof Annales des télécommunications, 2020-10, Vol.75 (9-10), p.563-572
issn 0003-4347
1958-9395
language eng
recordid cdi_proquest_journals_2473804553
source Springer Link
subjects Business process management
Circuits
Communications Engineering
Computer Communication Networks
Engineering
Information and Communication
Information Systems and Communication Service
Networks
Optimization
R & D/Technology Policy
Root cause analysis
Signal,Image and Speech Processing
title Process mining on machine event logs for profiling abnormal behaviour and root cause analysis
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-09-23T02%3A17%3A13IST&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=Process%20mining%20on%20machine%20event%20logs%20for%20profiling%20abnormal%20behaviour%20and%20root%20cause%20analysis&rft.jtitle=Annales%20des%20t%C3%A9l%C3%A9communications&rft.au=Maeyens,%20Jonas&rft.date=2020-10-01&rft.volume=75&rft.issue=9-10&rft.spage=563&rft.epage=572&rft.pages=563-572&rft.issn=0003-4347&rft.eissn=1958-9395&rft_id=info:doi/10.1007/s12243-020-00809-9&rft_dat=%3Cproquest_cross%3E2473804553%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c363t-7263423e5c16ec0b0c5c27bb6cca9021a8f3ed55e0e2e1deeaf65668d056ba593%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2473804553&rft_id=info:pmid/&rfr_iscdi=true