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An expert system for optimizing the operation of a technical system
PurposeThe main purpose of the expert system presented in the paper is to support proper decision-making to perform the operation of the complex and crucial technical system in a rational way.Design/methodology/approachThe proposed system was developed using the universal concepts of a semi-Markov p...
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Published in: | Journal of quality in maintenance engineering 2022-02, Vol.28 (1), p.131-153 |
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description | PurposeThe main purpose of the expert system presented in the paper is to support proper decision-making to perform the operation of the complex and crucial technical system in a rational way.Design/methodology/approachThe proposed system was developed using the universal concepts of a semi-Markov process, quality space and a multi-objective analysis. The maintenance and operation processes of a machine were modelled in the form of a semi-Markov process, the quality space was used to exclude the operation and maintenance process of critical quality and finally, thanks to implementation of a multi-objective analysis, the assessment system was build.FindingsBy generating each flow of the process, the expert system supports optimization of a technical system operation to choose the best maintenance strategy. Application of the expert system created based on a real industrial system is presented at the end of the paper.Research limitations/implicationsThe limitations of the proposed approach can be found in the parts of simulation and assessment. As the number of states to be taken into consideration increases, the time of calculation gets longer as well. As regards the assessment, ranges of the criteria argument have to be determined. Unfortunately, in some industrial systems, they are difficult to define or they are infinite and should be artificially limited.Practical implicationsThe system provides three most important benefits as compared to other solutions. The first benefit is the system ability to make a choice of the best strategy from the perspective of the accepted criteria. The second advantage is the ability to choose the best operation and maintenance strategy from the point of view of a decision-maker. And the third is that the decision-maker can be completely sure that the chosen way of operation is not of critical quality.Originality/valueThe novelty of the proposed solution involves the system approach to the expert system design, thanks to the described procedure which is flexible and can be easily implemented in different technical systems which have a crucial impact on reliability and safety of their operation. It is the unique combination of probability-based simulation, multi-dimensional quality considerations and multi-objective analysis. |
doi_str_mv | 10.1108/JQME-05-2020-0033 |
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The maintenance and operation processes of a machine were modelled in the form of a semi-Markov process, the quality space was used to exclude the operation and maintenance process of critical quality and finally, thanks to implementation of a multi-objective analysis, the assessment system was build.FindingsBy generating each flow of the process, the expert system supports optimization of a technical system operation to choose the best maintenance strategy. Application of the expert system created based on a real industrial system is presented at the end of the paper.Research limitations/implicationsThe limitations of the proposed approach can be found in the parts of simulation and assessment. As the number of states to be taken into consideration increases, the time of calculation gets longer as well. As regards the assessment, ranges of the criteria argument have to be determined. Unfortunately, in some industrial systems, they are difficult to define or they are infinite and should be artificially limited.Practical implicationsThe system provides three most important benefits as compared to other solutions. The first benefit is the system ability to make a choice of the best strategy from the perspective of the accepted criteria. The second advantage is the ability to choose the best operation and maintenance strategy from the point of view of a decision-maker. And the third is that the decision-maker can be completely sure that the chosen way of operation is not of critical quality.Originality/valueThe novelty of the proposed solution involves the system approach to the expert system design, thanks to the described procedure which is flexible and can be easily implemented in different technical systems which have a crucial impact on reliability and safety of their operation. 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The maintenance and operation processes of a machine were modelled in the form of a semi-Markov process, the quality space was used to exclude the operation and maintenance process of critical quality and finally, thanks to implementation of a multi-objective analysis, the assessment system was build.FindingsBy generating each flow of the process, the expert system supports optimization of a technical system operation to choose the best maintenance strategy. Application of the expert system created based on a real industrial system is presented at the end of the paper.Research limitations/implicationsThe limitations of the proposed approach can be found in the parts of simulation and assessment. As the number of states to be taken into consideration increases, the time of calculation gets longer as well. As regards the assessment, ranges of the criteria argument have to be determined. 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It is the unique combination of probability-based simulation, multi-dimensional quality considerations and multi-objective analysis.</description><subject>Criteria</subject><subject>Decision making</subject><subject>Dimensional analysis</subject><subject>Efficiency</subject><subject>Expert systems</subject><subject>Maintenance</subject><subject>Maintenance costs</subject><subject>Markov analysis</subject><subject>Markov processes</subject><subject>Mathematical models</subject><subject>Multiple objective analysis</subject><subject>Optimization</subject><subject>Random variables</subject><subject>Stochastic models</subject><subject>Systems design</subject><issn>1355-2511</issn><issn>1758-7832</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>M0C</sourceid><recordid>eNptkE1PwzAMhiMEEmPwA7hF4lywk6ZNj9M0vjSEkOAcda3LOq1NSTKJ8etJtV2QONmW_dp-H8auEW4RQd89v70sElCJAAEJgJQnbIK50kmupTiNuVSxqRDP2YX3G4gjRQ4TNp_1nL4HcoH7vQ_U8cY6bofQdu1P23_ysKZYkitDa3tuG17yQNW6b6tye5RcsrOm3Hq6OsYp-7hfvM8fk-Xrw9N8tkwqkWUhwQZLLWWVCShK0CovGsizjCBVKCmtkVYrlKmuSSuqdV6lUKS1WGUS46s1yCm7OewdnP3akQ9mY3eujyeNyJTQqGV0O2V4mKqc9d5RYwbXdqXbGwQzsjIjKwPKjKzMyCpq4KChLjrd1v9K_uCVv-iuaVQ</recordid><startdate>20220211</startdate><enddate>20220211</enddate><creator>Muślewski, Łukasz</creator><creator>Pająk, Michał</creator><creator>Migawa, Klaudiusz</creator><creator>Landowski, Bogdan</creator><general>Emerald Publishing Limited</general><general>Emerald Group Publishing Limited</general><scope>AAYXX</scope><scope>CITATION</scope><scope>0U~</scope><scope>1-H</scope><scope>7TB</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K6~</scope><scope>L.-</scope><scope>L.0</scope><scope>L6V</scope><scope>M0C</scope><scope>M2P</scope><scope>M7S</scope><scope>PQBIZ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYYUZ</scope><scope>Q9U</scope><scope>S0W</scope><orcidid>https://orcid.org/0000-0002-9842-288X</orcidid><orcidid>https://orcid.org/0000-0002-0149-1310</orcidid></search><sort><creationdate>20220211</creationdate><title>An expert system for optimizing the operation of a technical system</title><author>Muślewski, Łukasz ; Pająk, Michał ; Migawa, Klaudiusz ; Landowski, Bogdan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c266t-1f1a833c6209a08579f0766e04513e4d1ebb1348de85ed87c4094d2b631970d03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Criteria</topic><topic>Decision making</topic><topic>Dimensional analysis</topic><topic>Efficiency</topic><topic>Expert systems</topic><topic>Maintenance</topic><topic>Maintenance costs</topic><topic>Markov analysis</topic><topic>Markov processes</topic><topic>Mathematical models</topic><topic>Multiple objective analysis</topic><topic>Optimization</topic><topic>Random variables</topic><topic>Stochastic models</topic><topic>Systems design</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Muślewski, Łukasz</creatorcontrib><creatorcontrib>Pająk, Michał</creatorcontrib><creatorcontrib>Migawa, Klaudiusz</creatorcontrib><creatorcontrib>Landowski, Bogdan</creatorcontrib><collection>CrossRef</collection><collection>Global News & ABI/Inform Professional</collection><collection>Trade PRO</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>ProQuest Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Business Collection</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Professional Standard</collection><collection>ProQuest Engineering Collection</collection><collection>ABI/INFORM global</collection><collection>ProQuest Science Journals</collection><collection>Engineering Database</collection><collection>One Business (ProQuest)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering collection</collection><collection>ABI/INFORM Collection China</collection><collection>ProQuest Central Basic</collection><collection>DELNET Engineering & Technology Collection</collection><jtitle>Journal of quality in maintenance engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Muślewski, Łukasz</au><au>Pająk, Michał</au><au>Migawa, Klaudiusz</au><au>Landowski, Bogdan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An expert system for optimizing the operation of a technical system</atitle><jtitle>Journal of quality in maintenance engineering</jtitle><date>2022-02-11</date><risdate>2022</risdate><volume>28</volume><issue>1</issue><spage>131</spage><epage>153</epage><pages>131-153</pages><issn>1355-2511</issn><eissn>1758-7832</eissn><abstract>PurposeThe main purpose of the expert system presented in the paper is to support proper decision-making to perform the operation of the complex and crucial technical system in a rational way.Design/methodology/approachThe proposed system was developed using the universal concepts of a semi-Markov process, quality space and a multi-objective analysis. The maintenance and operation processes of a machine were modelled in the form of a semi-Markov process, the quality space was used to exclude the operation and maintenance process of critical quality and finally, thanks to implementation of a multi-objective analysis, the assessment system was build.FindingsBy generating each flow of the process, the expert system supports optimization of a technical system operation to choose the best maintenance strategy. Application of the expert system created based on a real industrial system is presented at the end of the paper.Research limitations/implicationsThe limitations of the proposed approach can be found in the parts of simulation and assessment. As the number of states to be taken into consideration increases, the time of calculation gets longer as well. As regards the assessment, ranges of the criteria argument have to be determined. Unfortunately, in some industrial systems, they are difficult to define or they are infinite and should be artificially limited.Practical implicationsThe system provides three most important benefits as compared to other solutions. The first benefit is the system ability to make a choice of the best strategy from the perspective of the accepted criteria. The second advantage is the ability to choose the best operation and maintenance strategy from the point of view of a decision-maker. And the third is that the decision-maker can be completely sure that the chosen way of operation is not of critical quality.Originality/valueThe novelty of the proposed solution involves the system approach to the expert system design, thanks to the described procedure which is flexible and can be easily implemented in different technical systems which have a crucial impact on reliability and safety of their operation. It is the unique combination of probability-based simulation, multi-dimensional quality considerations and multi-objective analysis.</abstract><cop>Bradford</cop><pub>Emerald Publishing Limited</pub><doi>10.1108/JQME-05-2020-0033</doi><tpages>23</tpages><orcidid>https://orcid.org/0000-0002-9842-288X</orcidid><orcidid>https://orcid.org/0000-0002-0149-1310</orcidid></addata></record> |
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subjects | Criteria Decision making Dimensional analysis Efficiency Expert systems Maintenance Maintenance costs Markov analysis Markov processes Mathematical models Multiple objective analysis Optimization Random variables Stochastic models Systems design |
title | An expert system for optimizing the operation of a technical system |
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