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% If the paper title is too long for the running head, you can set
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% an abbreviated paper title here
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\author{
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Liliya~Kamaletdinova\inst{1}\orcidID{0009-0006-2929-9757} \and
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Anton~Romanov\inst{1}\orcidID{0000-0001-5275-7628} \and
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Aleksey~Filippov\inst{1}\orcidID{0000-0003-0008-5035} \and
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Nadezhda~Yarushkina\inst{1}\orcidID{0000-0002-5718-8732}
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}%
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\authorrunning{L. Kamaletdinova et al.}
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%\author{
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% Liliya~Kamaletdinova\inst{1}\orcidID{0009-0006-2929-9757} \and
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% Anton~Romanov\inst{1}\orcidID{0000-0001-5275-7628} \and
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% Aleksey~Filippov\inst{1}\orcidID{0000-0003-0008-5035} \and
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% Nadezhda~Yarushkina\inst{1}\orcidID{0000-0002-5718-8732}
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%}%
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%\authorrunning{L. Kamaletdinova et al.}
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% First names are abbreviated in the running head.
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% If there are more than two authors, 'et al.' is used.
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\institute{Ulyanovsk State Technical University, Ulyanovsk, Russian Federation
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\email{romanov73@gmail.com}\\
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\url{https://ulstu.ru/}}
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%\institute{Ulyanovsk State Technical University, Ulyanovsk, Russian Federation
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%\email{romanov73@gmail.com}\\
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%\url{https://ulstu.ru/}}
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\maketitle % typeset the header of the contribution
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@ -142,7 +142,9 @@
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Таким образом, согласно диаграмме база знаний состоит из нескольких уровней условий, учитывающих возможные изменения в метамодели и в базе данных интегрируемой информационной системы, и нескольких уровней следствий, формирующих четкий логический вывод на основе лингвистического представления правил в базе правил.
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\section{Модель базы знаний поведения интегрируемой информационной системы}
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Согласно рисунку \ref{fig:kb-structure} база правил будет представлена в виде иерархической структуры с двумя уровнями правил. Ранее авторами была получена структурная модель метаданных $M$ интегрируемой ИС \cite{Kamaletdinova-2024}. Так первый уровень будет представлен в виде правил, состоящих из лингвистических термов, и зависеть от изменений метамодели. Второй уровень правил будет формироваться динамически на основе результатов, полученных на первом уровне.
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Согласно рисунку \ref{fig:kb-structure} база правил будет представлена в виде иерархической структуры с двумя уровнями правил. Ранее авторами была получена структурная модель метаданных $M$ интегрируемой ИС [].
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%\cite{Kamaletdinova-2024}.
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Так первый уровень будет представлен в виде правил, состоящих из лингвистических термов, и зависеть от изменений метамодели. Второй уровень правил будет формироваться динамически на основе результатов, полученных на первом уровне.
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Пусть $INP=\{INP_1,INP_2, ...,INP_z\}, z \in N$ -- множество лингвистических термов, представляющих входные данные модели метаданных $M$, а $OUT=\{OUT_1, OUT_2, ..., OUT_w\}, w \in N$ -- множество лингвистических термов, представляющих ключевые процессы модели метаданных $M$, тогда правило, описывающее первый уровень будет иметь теоретико-множественное представление в виде:
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\begin{equation}
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@ -216,7 +218,8 @@ $Y=\{y^i\}, i = [1,n], n \in N$, -- $n$ состояний интегрируе
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Таким образом, происходит процесс принятия решений на основе иерархической нечеткой базы правил с нечетким логическим выводом.
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\section{Иллюстративный пример иерархической базы правил}
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В качестве примера рассмотрим полученную базу правил на основе информационной системы, предназначенной для обработки заявок, faveo service desk \cite{faveo}. В работе \cite{Kamaletdinova-2024-2} отображена метамодель данных, представленная на рисунке \ref{fig:metamodel}.
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В качестве примера рассмотрим полученную базу правил на основе информационной системы, предназначенной для обработки заявок, faveo service desk \cite{faveo}. В работе [] %\cite{Kamaletdinova-2024-2}
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отображена метамодель данных, представленная на рисунке \ref{fig:metamodel}.
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\begin{figure}
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\centering
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\includegraphics[width=1.0\linewidth]{figures/metamodel}
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@ -247,7 +250,9 @@ $Y=\{y^i\}, i = [1,n], n \in N$, -- $n$ состояний интегрируе
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\section{Управляющая система в общем комплексе управления}
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Предлагаемый подход подразумевает использование управляющей системы для сокращения временных и трудозатрат оператора, участвующего в процессе управления. Управляющая система (УС) — это программный инструмент, реализующий предлагаемый подход управления на основе данных \cite{Kamaletdinova-2024}. Управление включает в себя конфигурирование и обмен данными, в данном случае, за счет использования метамодели интегрируемой ИС и иерархической базы правил с нечетким логическим выводом. Управляющая система выступает адаптером для обеспечения взаимодействия интегрируемой ИС с программным комплексом PLM.
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Предлагаемый подход подразумевает использование управляющей системы для сокращения временных и трудозатрат оператора, участвующего в процессе управления. Управляющая система (УС) — это программный инструмент, реализующий предлагаемый подход управления на основе данных []
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%\cite{Kamaletdinova-2024}.
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Управление включает в себя конфигурирование и обмен данными, в данном случае, за счет использования метамодели интегрируемой ИС и иерархической базы правил с нечетким логическим выводом. Управляющая система выступает адаптером для обеспечения взаимодействия интегрируемой ИС с программным комплексом PLM.
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На рисунке \ref{fig:control_system} представлен процесс организации управления информационной системой с участием управляющей системой.
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@ -271,7 +276,7 @@ $Y=\{y^i\}, i = [1,n], n \in N$, -- $n$ состояний интегрируе
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В статье приведен подход управления на основе данных применительно к производственным процессам. Подход подразумевает использование метамодели данных информационной системы и иерархическую базу правил поведения информационной системы с нечетким логическим выводом. Приведены основные выводы, подтверждающие целесообразность использования данного подхода в задачах отображения данных информационных систем и управления информационной системой с целью снижения человеческого фактора при организации управления сторонними системами на производстве. Задачи являются актуальными на многих производствах Российской Федерации, в том числе на производственных предприятиях Ульяновской области.
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\begin{credits}
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\subsubsection{\ackname} This study was supported the Ministry of Science and Higher Education of Russia in framework of project No. 075-03-2023-143 "The study of intelligent predictive analytics based on the integration of methods for constructing features of heterogeneous dynamic data for machine learn-ing and methods of predictive multimodal data analysis".
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%\subsubsection{\ackname} This study was supported the Ministry of Science and Higher Education of Russia in framework of project No. 075-03-2023-143 "The study of intelligent predictive analytics based on the integration of methods for constructing features of heterogeneous dynamic data for machine learn-ing and methods of predictive multimodal data analysis".
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\end{credits}
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\begin{thebibliography}{8}
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@ -334,14 +339,14 @@ Jang, J.-S.R., et al.: Fuzzy modeling using generalized neural networks and Kalm
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\bibitem{Komartsova-2011}
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Komartsova L. G. Evolutionary methods for forming fuzzy rule bases. – 2011.
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\bibitem{Kamaletdinova-2024}
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Kamaletdinova, Lilia \& Romanov, Anton. (2024). Control System Design (In russian). System Analysis \& Mathematical Modeling. 6. 60-77. 10.17150/2713-1734.2024.6(1).60-77.
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%\bibitem{Kamaletdinova-2024}
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%Kamaletdinova, Lilia \& Romanov, Anton. (2024). Control System Design (In russian). System Analysis \& Mathematical Modeling. 6. 60-77. 10.17150/2713-1734.2024.6(1).60-77.
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\bibitem{faveo}
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Продукт службы поддержки Faveo Service Desk — URL: https://www.faveohelpdesk.com/servicedesk/?lang=ru (accessed at 1.05.2024)
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\bibitem{Kamaletdinova-2024-2}
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L. Kamaletdinova, A. Romanov and N. Yarushkina, "Industrial Data-Driven Control," 2024 International Russian Smart Industry Conference (SmartIndustryCon), Sochi, Russian Federation, 2024, pp. 62-67
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%\bibitem{Kamaletdinova-2024-2}
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%L. Kamaletdinova, A. Romanov and N. Yarushkina, "Industrial Data-Driven Control," 2024 International Russian Smart Industry Conference (SmartIndustryCon), Sochi, Russian Federation, 2024, pp. 62-67
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\end{thebibliography}
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\end{document}
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\begin{document}
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\title{Forming a rule base for PLM systems}
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\title{Forming a rule base for product lifecycle management systems}
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%
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%\titlerunning{Abbreviated paper title}
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% If the paper title is too long for the running head, you can set
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% an abbreviated paper title here
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%
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\author{
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Liliya~Kamaletdinova\inst{1}\orcidID{0009-0006-2929-9757} \and
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Anton~Romanov\inst{1}\orcidID{0000-0001-5275-7628} \and
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Aleksey~Filippov\inst{1}\orcidID{0000-0003-0008-5035} \and
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Nadezhda~Yarushkina\inst{1}\orcidID{0000-0002-5718-8732}
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}%
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\authorrunning{L. Kamaletdinova et al.}
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%\author{
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% Liliya~Kamaletdinova\inst{1}\orcidID{0009-0006-2929-9757} \and
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% Anton~Romanov\inst{1}\orcidID{0000-0001-5275-7628} \and
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% Aleksey~Filippov\inst{1}\orcidID{0000-0003-0008-5035} \and
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% Nadezhda~Yarushkina\inst{1}\orcidID{0000-0002-5718-8732}
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%}%
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%\authorrunning{L. Kamaletdinova et al.}
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% First names are abbreviated in the running head.
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% If there are more than two authors, 'et al.' is used.
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%
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\institute{Ulyanovsk State Technical University, Ulyanovsk, Russian Federation
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\email{romanov73@gmail.com}\\
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\url{https://ulstu.ru/}}
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%\institute{Ulyanovsk State Technical University, Ulyanovsk, Russian Federation
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%\email{romanov73@gmail.com}\\
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%\url{https://ulstu.ru/}}
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\maketitle % typeset the header of the contribution
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\begin{abstract}
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In production, there are tasks of the integration of a PLM systems and a third-party information system. This system may not be part of the PLM complex, but provides information support for managing production processes. An analyst is currently responsible for carrying out the integration. He must form a structural and process models of the integrated information system to make interaction rules with the system. During the operation process, besides the analyst, there is an operator involved, who performs maintaining the relevance of the integrated information system data and the entire complex, and a decision maker, who performs managing the integrated information system, namely maintaining its operational state.
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In production, there are tasks of the integration of a product lifecycle management (PLM) systems and a third-party information system (IS). This system may not be part of the PLM complex, but provides information support for managing production processes. An analyst is currently responsible for carrying out the integration. He must form a structural and process models of the integrated information system to make interaction rules with the system. During the operation process, besides the analyst, there is an operator involved, who performs maintaining the relevance of the integrated information system data and the entire complex, and a decision maker, who performs managing the integrated information system, namely maintaining its operational state.
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An approach is to reduce the load both on the analyst, who configures the interaction and display of data, and on the operator, who is involved in ensuring the relevance of data structures, and on the decision-maker, who is involved in making important decisions related to risks in production.
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We propose using data-based management by forming a data meta-model of the integrated information system based on the analysis of its storage; mapping of the data of PLM systems and the integrated information system on the enterprise through the use of a rule base for the behavior of the integrated information system.
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The novelty is in using data-based management by forming a data meta-model of the integrated information system based on the analysis of its storage; mapping of the data of PLM systems and the integrated information system on the enterprise through the use of a rule base for the behavior of the integrated information system. This allow to reduce in a 67\% the workload of the decision maker.
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\keywords{Rule base \and PLM systems \and Data-driven management}
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\end{abstract}
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\section{Introduction}
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As of today, many large manufacturing facilities use PLM systems \cite{Terzi-2010} to manage the product lifecycle. PLM systems are used to control data flows in production, including tasks such as storing, integrating, and maintaining data in each information subsystem within the complex. The data bus plays a key role in this \cite{Menge-2007}. Each complex of systems with a data bus has its own implementation features \cite{Swedenko-2016}. However, the common feature of data bus-based integration is the use of rule-based interaction. To configure the interaction between subsystems, analysts perform the following tasks: building a model of the information system behavior, identifying key features, forming system behavior rules.
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This work considers not only the approach to forming interactions between systems, but also the approach to managing PLM subsystems. There is often a need to integrate a PLM system with an external information system (referred to here as the integrable IS) that is not part of the PLM complex but is involved in tasks related to supporting information for managing production processes. At present, an analyst or decision-maker (DM) performs integrating the two systems. The analyst must create structural and process models of the integrable IS based on which interaction rules with the system are formed. During operation, the analyst’s (DM) tasks include managing the integrable IS, ensuring the data is up to date, and maintaining the system’s operational state.
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This work considers not only the approach to forming interactions between systems, but also the approach to managing PLM subsystems. There is often a need to integrate a PLM system with an external information system (referred to here as the integrable IS) that is not part of the PLM complex but is involved in tasks related to supporting information for managing production processes. At present, an analyst or decision-maker (DM) performs integrating the two systems. The analyst must create structural and process models of the integrable inforamtion systems based on which interaction rules with the system are formed. During operation, the analyst’s (DM) tasks include managing the integrable IS, ensuring the data is up to date, and maintaining the system’s operational state.
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\begin{figure}
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\centering
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\includegraphics[width=1.0\linewidth]{figures/interaction}
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The analyst must take into account changes in the data structure of the integrable IS. The operator monitors potential risks associated with ensuring the data matches the PLM complex. The DM makes decisions regarding minimizing possible risks. An approach is proposed that helps reduce the workload on both the analyst responsible for setting up interaction and data display and the operator involved in ensuring the data structure’s timeliness, and the DM making crucial decisions related to potential risks in production.
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\subsection{Overview of existing methods and approaches to management}
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The novelty is in using data-based management by forming a data meta-model of the integrated information system based on the analysis of its storage; mapping of the data of PLM systems and the integrated information system on the enterprise through the use of a rule base for the behavior of the integrated information system.
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The primary advantage of this approach is the ability to form exchange rules not only based on the metadata of information systems, but also based on interaction logs during information exchange.
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\section{Overview of existing methods and approaches to management}
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Currently, approaches to managing production information systems are discussed in the following sources: \cite{Feng-2006,Sudarsan-2005,Alahi-2023,Sacks-2020,Cimini-2020,Hou-2009}. Sources \cite{Feng-2006,Sudarsan-2005,Sacks-2020} suggest using an integrated IS model. These approaches directly depend on the accuracy and completeness of the model, with the model being formulated either by an analyst \cite{Feng-2006,Sacks-2020} or automatically \cite{Sudarsan-2005}, which does not always guarantee the adequacy and accuracy of the model. Source \cite{Feng-2006} presents classical control based on the model of processes and data in the information system, where the role of the analyst is significant. Source \cite{Sudarsan-2005} suggests using a framework for model formation. Despite the fact that the model formation process occurs automatically, the controlling component still depends on the accuracy and completeness of the model. Source \cite{Alahi-2023} proposes using a training sample and unsupervised machine learning. However, depending on the completeness and quality of the data sample, the model of the integrated IS being formed may lead to misinterpretation of the results of control mechanisms forecasting, especially if the data contains errors or is heavily noisy. In source \cite{Sacks-2020}, a digital twins approach is proposed. Despite the advantages of this approach overall, in the context of the set task, namely integration of information systems, data representation, and reducing the workload of analysts, operators, and managers, the digital twins approach requires additional qualitative analysis and building a model of the information system, leading to an increased workload on the analyst.
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In source \cite{Cimini-2020}, an approach to human-in-the-loop management is described. The main idea of this approach is complete control of the control system behavior by a human and boils down to supervised machine learning. This approach is the safest in terms of preventing production-related risks, but it is more complex and requires the involvement of managers at all stages of the control system lifecycle, from forming the training sample to monitoring the control system behavior. Therefore, a data-driven control approach is proposed \cite{Hou-2009}. It is assumed to reduce the workload of the analyst and operator by integrating and partially managing the tasks of the production information system with the developed control system.
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In source \cite{Cimini-2020}, an approach to human-in-the-loop management is described. The main idea of this approach is complete control of the control system behavior by a human and boils down to supervised machine learning. This approach is the safest in terms of preventing production-related risks, but it is more complex and requires the involvement of managers at all stages of the control system lifecycle, from forming the training sample to monitoring the control system behavior. Therefore, a data-driven control approach is proposed \cite{Hou-2009}. It is assumed to reduce the workload of the analyst and operator by integrating and partially managing the tasks of the production information system with the developed control system. The data-driven control approach implies:
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\begin{enumerate}
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\item performing system modeling, but not in the classical sense \cite{Feng-2006}, but by forming a data metamodel of the integrated IS based on analyzing its repository;
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To solve the control problem, it is proposed to use a production rule-based model. Classical fuzzy systems are based on the Mamdani approach \cite{Mamdani-1974}. In such systems, there are 2 modules for converting regular data into fuzzy data. The fuzzification module establishes a correspondence between real values of input data and fuzzy values, based on the membership function. On the other hand, the defuzzification module establishes a correspondence between fuzzy values and real values of output data of the subject area. Fuzzy rule bases (systems based on fuzzy rules) are based on the principle of converting crisp values into fuzzy values. In this case, rules represent a set of linguistic terms, and output data is associated with them; for example, a rule may have multiple input data and only a certain value of the output parameter. There are variants of non-classical systems based on fuzzy rules:Hierarchical fuzzy systems \cite{Razak-2021,Zhang-2014,Duan-2001}, Neuro-fuzzy systems \cite{Jang-1991}, Evolutionary fuzzy systems \cite{Komartsova-2011}.
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\item data representation in the integration of PLM systems and the integrated IS of the enterprise by using the rules base of the behavior of the integrated IS;
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\item reducing the risks of transitioning to a failure state and maintaining the system in a working state without the involvement of managers in this process.
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\end{enumerate}
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\section{Overview of existing approaches to rule base formation}
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To solve the problem, it is proposed to use a production rule-based model. Classical fuzzy systems are based on the Mamdani approach \cite{Mamdani-1974}. In such systems, there are 2 modules for converting regular data into fuzzy data. The fuzzification module establishes a correspondence between real values of input data and fuzzy values, based on the membership function. On the other hand, the defuzzification module establishes a correspondence between fuzzy values and real values of output data of the subject area. Fuzzy rule bases (systems based on fuzzy rules) are based on the principle of converting crisp values into fuzzy values. In this case, rules represent a set of linguistic terms, and output data is associated with them; for example, a rule may have multiple input data and only a certain value of the output parameter. There are several variants of classical systems based on fuzzy rules:
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\begin{enumerate}
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\item Mamdani-type fuzzy rules system \cite{Gonsales-1994}. Each rule variable represents a value from a set of linguistic terms, for example. If $X_i$ is a variable represented by a set $\{l_1,l_2,l_3 \}$, then in the rule variable $X_i$ can be represented as $\{l_1,l_2\}$. The variable can belong to a set of linguistic terms in the rule. This helps reduce the number of rules to avoid increasing the size of the rule base. Thus, a rule can look like: $x_1=\{l_{11},l_{12}\} \wedge x_2 = \{l_{23} \} \wedge ... \wedge x_n=\{l_{n1},l_{n2} \} \rightarrow y = Y$.
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\item Approximate systems based on Mamdani fuzzy rules \cite{Duckstein-1995}. Such systems include several elements of term sets, which can reduce the interpretability of the output. Systems can achieve greater accuracy at the cost of losing interpretability. Each rule has its own fuzzy set instead of using linguistic terms. This approach generates semantically free rules and has more expressiveness due to the use of various fuzzy sets in each rule. It can take different numbers of rules depending on the complexity of the problem. As for the drawbacks, such systems suffer from a loss of interpretability and may overfit training data, performing poorly on unseen data.
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\item Classification systems based on fuzzy rules \cite{Chi-1996}. A classification system based on fuzzy rules is a system that uses fuzzy rules as a means of learning. In classical systems based on Mamdani fuzzy rules, input data is matched with a single-dimensional output, but in this case, input data is matched with one of the class labels. The rule structure looks as follows: $x_1 = l_{1i} \wedge x _2 = l_{2i} \wedge ... \wedge x_n = l_{ni} \rightarrow y = c$.
|
||||
\end{enumerate}
|
||||
|
||||
There are variants of non-classical systems based on fuzzy rules:
|
||||
\begin{enumerate}
|
||||
\item Hierarchical fuzzy systems \cite{Razak-2021} consist of several low-dimensional fuzzy systems arranged hierarchically. Rules in hierarchical fuzzy systems are grouped into modules according to their roles in the system. Each module calculates a partial solution, which is then passed to modules of the next level. Although each module is a fuzzy system, it generates a significantly smaller number of rules than a flat fuzzy system. Despite the widespread use of hierarchical rule bases when working with big data, there are a number of drawbacks to such systems. The article \cite{Zhang-2014} provides examples of optimizing hierarchical fuzzy systems using a genetic algorithm. The article \cite{Duan-2001} provides examples of optimizing cascading hierarchical fuzzy systems using neural networks, but because the proposed method involves all input variables, the advantage of reducing the number of rules is lost.
|
||||
|
||||
\item Neuro-fuzzy systems \cite{Jang-1991} are a merge of systems based on fuzzy rules with artificial neural networks. The main idea of these systems is the ability to make decisions based on given rules and to learn by using neural systems. The rule base is flat (one-dimensional, unlike hierarchical fuzzy systems), which increases the size of the rule base but simplifies the process of training the neural network during system operation. Neuro-fuzzy systems consist of two modules, the first one is responsible for tuning and structuring the rule condition, and the second one is responsible for forming the consequence. Based on existing rules, the neural network adjusts the rule condition and partially calculates the membership function corresponding to the fuzzy input data. In the second stage, the consequence of the rule is calculated from the fuzzy set of consequences to precise output values.
|
||||
|
||||
\item Evolutionary fuzzy systems \cite{Komartsova-2011}. Genetic algorithms are used to form the rule base. The difference of this type of fuzzy rule bases is that they are self-learning and self-optimizing. Implementation of this approach is done in two stages:
|
||||
|
||||
\begin{itemize}
|
||||
\item in the first stage, a genetic algorithm (GA) is used to find candidates of fuzzy rules in the knowledge base;
|
||||
|
||||
\item in the second stage, GA is used for the knowledge base optimization procedure to exclude the worst rules that have little impact on achieving the final solution.
|
||||
\end{itemize}
|
||||
\end{enumerate}
|
||||
|
||||
To solve the research problem in data organization, it was decided to combine the hierarchical fuzzy rule base and neuro-fuzzy rule base approaches with a result based on Mamdani fuzzy rules, using an evolutionary algorithm approach for rule formation based on changes in the metamodel. Thus, the rule base will be structured as shown in Figure \ref{fig:kb-structure}.
|
||||
\begin{figure}
|
||||
@ -85,7 +119,9 @@ To solve the research problem in data organization, it was decided to combine th
|
||||
According to the diagram, the knowledge base consists of several levels of conditions taking into account possible changes in the metamodel and in the database of the integrated information system, and several levels of consequences forming a clear logical inference based on the linguistic representation of rules in the rule base.
|
||||
|
||||
\section{Model of the knowledge base of behavior of the integrated information system}
|
||||
According to figure \ref{fig:kb-structure}, the rule base will be represented as a hierarchical structure with two levels of rules. Previously, the authors obtained a structural model of the metadata $M$ of the integrated IS \cite{Kamaletdinova-2024}. Thus, the first level will be represented as rules consisting of linguistic terms and will depend on changes in the meta-model. The second level of rules will be dynamically formed based on the results obtained at the first level.
|
||||
According to figure \ref{fig:kb-structure}, the rule base will be represented as a hierarchical structure with two levels of rules. Previously, the authors obtained a structural model of the metadata $M$ of the integrated IS [].
|
||||
%\cite{Kamaletdinova-2024}.
|
||||
Thus, the first level will be represented as rules consisting of linguistic terms and will depend on changes in the meta-model. The second level of rules will be dynamically formed based on the results obtained at the first level.
|
||||
|
||||
Let $INP=\{INP_1,INP_2, ..., INP_z\}, z \in N$ be the set of linguistic terms representing the input data of the metadata model $M$, and $OUT= \allowbreak \{OUT_1, OUT_2, ...,\allowbreak OUT_w\}, w \in N$ be the set of linguistic terms representing the key processes of the metadata model $M$. Hence, the rule describing the first level will have a set-theoretic representation as follows:
|
||||
\begin{equation}
|
||||
@ -130,6 +166,11 @@ During the operation of the integrated IS, there may be situations where differe
|
||||
To account for uncertainty in input values, fuzzy membership functions of triangular form $\mu_(y^i ) (x^i)$ \cite{Mamdani-1974} will be used. This function of input parameter values $x^i$ inherent in system state $i$ allows for logical inference even when the input value vector contains values that do not precisely match the values used in the rule antecedents.
|
||||
|
||||
\section{Algorithm for generating output data based on a hierarchical rule base}
|
||||
|
||||
The generated rule base contains two levels: abstract, based on metadata of information systems, and actual, reflecting the processes of comparing the attributes of objects exchanged between information systems.
|
||||
|
||||
At the first level of the rule, antecedents and consequent contain the names of objects (entities) exchanged between information systems. The second level describes the rules for matching attributes based on the business logic of the exchange. The fuzzy approach allows us to obtain a solution for cases where the attribute values in the antecedents do not have an exact comparison with the original data, i.e. allows you to determine the state of the system by the value of the membership function.
|
||||
|
||||
Figure \ref{fig:algorithm} depicts the decision-making algorithm using a hierarchical fuzzy rule base with fuzzy logical inference based on the Mamdani approach \cite{Mamdani-1974}.
|
||||
|
||||
\begin{figure}
|
||||
@ -139,9 +180,11 @@ Figure \ref{fig:algorithm} depicts the decision-making algorithm using a hierarc
|
||||
\label{fig:algorithm}
|
||||
\end{figure}
|
||||
|
||||
First, on the basis of the metamodel, the first level of the rule base is formed (generate abstract level rule base), which will not change until changes occur in the metamodel of the integrated IS itself.
|
||||
|
||||
The algorithm presented in Figure \ref{fig:algorithm} consists of the following steps:
|
||||
\begin{itemize}
|
||||
\item Input data, represented as key-value data tuples ($inp1 = 7$) of different types (integer, string, date, and boolean variables), are transformed into linguistic terms represented as $INP=\{INP_1, INP_2, ..., INP_z\}, z \in N$.
|
||||
\item Input data, represented as key-value data tuples from data storage of IS (e.g. $inp1 = 7$) of different types (integer, string, date, and boolean variables), are transformed into linguistic terms represented as $INP=\{INP_1, INP_2, ..., INP_z\}, z \in N$.
|
||||
|
||||
\item Using the first level rule base (abstract level rule base) and the transformed input data ($INP$), a logical inference is performed, represented as $\{\{INP^{OUT_s}\}, OUT_s\}, s \in N$.
|
||||
|
||||
@ -154,7 +197,8 @@ The algorithm presented in Figure \ref{fig:algorithm} consists of the following
|
||||
Thus, the decision-making process is based on a hierarchical fuzzy rule base with fuzzy logical inference.
|
||||
|
||||
\section{Illustrative example of a hierarchical rule base}
|
||||
As an example, let's consider the rule base obtained based on the information system designed for processing requests, faveo service desk \cite{faveo}. The data metamodel depicted in \cite{Kamaletdinova-2024-2} is shown in Figure \ref{fig:metamodel}.
|
||||
As an example, let's consider the rule base obtained based on the information system designed for processing requests, faveo service desk \cite{faveo}. The data metamodel described in [] %\cite{Kamaletdinova-2024-2}
|
||||
is shown in Figure \ref{fig:metamodel}.
|
||||
\begin{figure}[h]
|
||||
\centering
|
||||
\includegraphics[width=0.8\linewidth]{figures/metamodel}
|
||||
@ -199,7 +243,8 @@ An example of the output result is presented in Table \ref{tab:output}.
|
||||
Thus, with the given input data, a result is generated that reflects the state of the key process of the faveo service desk information system. This output confirms the feasibility of applying the proposed approach, as the generated result reflects real data when working with the faveo service desk ticketing system, and obtaining the result does not depend on the launch of the integrated information system itself, which is an advantage when displaying data and the parallel operation of the integrated information system with the PLM complex.
|
||||
|
||||
\section{Control system of the control complex}
|
||||
The proposed approach involves using a control system to reduce the time and effort of the operator involved in the control process. The control system (CS) is a software tool that implements the proposed management approach based on data \cite{Kamaletdinova-2024}. Control involves configuration and data exchange, in this case, through the use of the metamodel of the integrated IS and a hierarchical rule base with fuzzy logic inference. The control system acts as an adapter to facilitate the interaction of the integrated IS with the PLM software complex.
|
||||
The proposed approach involves using a control system to reduce the time and effort of the operator involved in the control process. The control system (CS) is a software tool that implements the proposed management approach based on data [] %\cite{Kamaletdinova-2024}.
|
||||
Control involves configuration and data exchange, in this case, through the use of the metamodel of the integrated inforamtion system (IS) and a hierarchical rule base with fuzzy logic inference. The control system acts as an adapter to facilitate the interaction of the integrated IS with the PLM software complex.
|
||||
|
||||
Figure \ref{fig:control_system} shows the process of organizing the management of an information system with the involvement of a control system.
|
||||
\begin{figure}
|
||||
@ -208,20 +253,22 @@ Figure \ref{fig:control_system} shows the process of organizing the management o
|
||||
\caption{The process of organizing interaction and maintaining the system in a state of operability with the participation of the management system and the decision maker}
|
||||
\label{fig:control_system}
|
||||
\end{figure}
|
||||
When comparing Figures \ref{fig:interaction} and \ref{fig:control_system}, it is noted that the role of the analyst and the operator is excluded from the control process, their functions are performed by the CS. It is also worth noting that in the management process, there are several conditions in both cases:
|
||||
When comparing Figures \ref{fig:interaction} and~\ref{fig:control_system}, it is noted that the role of the analyst and the operator is excluded from the control process, their functions are performed by the CS. It is also worth noting that in the management process, there are several conditions in both cases:
|
||||
\begin{enumerate}
|
||||
\item The possible reaction of the integrated IS to input data exists;
|
||||
|
||||
\item The influence of the received reaction on the state of the PLM system (the possibility of adding data to the complex without errors and risks to the entire production complex).
|
||||
\end{enumerate}
|
||||
|
||||
Thus, it can be seen that the main task of the CS is to adjust the data of the overall PLM complex without interacting with the integrated information system. The overall percentage of scenarios in which a person should be involved in working with the system without the CS is 85\%, while the total number of scenarios when working with the CS is 18\%. The main task, in the case of using the CS, is to react to errors in generating results from the integrated IS. This results in a 67\% reduction in the role of the decision maker.
|
||||
Thus, it can be seen that the main task of the CS is to adjust the data of the overall PLM complex without interacting with the integrated information system.
|
||||
There were 9 scenarios where the DM was involved. After implementing the subsystem, the number of scenarios reduced to 3.
|
||||
The overall percentage of scenarios in which a person should be involved in working with the system without the CS is 85\% \ref{fig:interaction}, while the total number of scenarios when working with the CS is 18\%, \ref{fig:control_system}. The main task, in the case of using the CS, is to react to errors in generating results from the integrated IS. This allow to reduce in a 67\% the workload of the decision maker.
|
||||
|
||||
\section{Conclusion}
|
||||
The article presents a data-driven management approach applied to production processes. The approach involves using the data metamodel of an information system and a hierarchical rule base for the behavior of the information system with fuzzy logic inference. The main conclusions are provided, confirming the feasibility of using this approach in tasks related to data representation in information systems and managing information systems to reduce the human factor in organizing control of external systems in production. The tasks are relevant in many production facilities of the Russian Federation, including industrial enterprises in the Ulyanovsk region.
|
||||
|
||||
\begin{credits}
|
||||
\subsubsection{\ackname} This study was supported the Ministry of Science and Higher Education of Russia in framework of project No. 075-03-2023-143 "The study of intelligent predictive analytics based on the integration of methods for constructing features of heterogeneous dynamic data for machine learn-ing and methods of predictive multimodal data analysis".
|
||||
%\subsubsection{\ackname} This study was supported the Ministry of Science and Higher Education of Russia in framework of project No. 075-03-2023-143 "The study of intelligent predictive analytics based on the integration of methods for constructing features of heterogeneous dynamic data for machine learn-ing and methods of predictive multimodal data analysis".
|
||||
\end{credits}
|
||||
|
||||
\begin{thebibliography}{8}
|
||||
@ -258,9 +305,17 @@ Hou Z.S., Xu J.X. On Data-driven Control Theory: the State of the Art and Perspe
|
||||
\bibitem{Mamdani-1974}
|
||||
Mamdani, E.H.: Application of fuzzy algorithms for control of simple dynamic plant. In: Proceedings of the Institution of Electrical Engineers, vol. 121, pp. 1585–1588. IET (1974).
|
||||
|
||||
\bibitem{Gonsales-1994}
|
||||
Gonza´lez, A., Pe´rez, R., Verdegay, J.L.: Learning the structure
|
||||
of a fuzzy rule: a genetic approach. Fuzzy Syst. Artif. Intell.
|
||||
3(1), 57–70 (1994)
|
||||
|
||||
\bibitem{Duckstein-1995}
|
||||
Duckstein, L., et al.: Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological, and Engineering Systems, vol. 8. CRC Press, Boca Raton (1995)
|
||||
|
||||
\bibitem{Chi-1996}
|
||||
Chi, Z., Yan, H., Pham, T.: Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition, vol. 10. World Scientific, Singapore (1996)
|
||||
|
||||
\bibitem{Razak-2021}
|
||||
Razak, T.R., Fauzi, S.S.M., Gining, R.A.J., Ismail, M.H., Maskat, R.: Hierarchical fuzzy systems: interpretability and complexity. Indones. J. Electr. Eng. Inform. 9(2), 478–489 (2021)
|
||||
|
||||
@ -276,14 +331,14 @@ Jang, J.-S.R., et al.: Fuzzy modeling using generalized neural networks and Kalm
|
||||
\bibitem{Komartsova-2011}
|
||||
Komartsova L. G. Evolutionary methods for forming fuzzy rule bases. – 2011.
|
||||
|
||||
\bibitem{Kamaletdinova-2024}
|
||||
Kamaletdinova, Lilia \& Romanov, Anton. (2024). Control System Design (In russian). System Analysis \& Mathematical Modeling. 6. 60-77. 10.17150/2713-1734.2024.6(1).60-77.
|
||||
%\bibitem{Kamaletdinova-2024}
|
||||
%Kamaletdinova, Lilia \& Romanov, Anton. (2024). Control System Design (In russian). System Analysis \& Mathematical Modeling. 6. 60-77. 10.17150/2713-1734.2024.6(1).60-77.
|
||||
|
||||
\bibitem{faveo}
|
||||
Faveo Service Desk - URL: https://www.faveohelpdesk.com/servicedesk/?lang=ru (accessed at 1.05.2024)
|
||||
|
||||
\bibitem{Kamaletdinova-2024-2}
|
||||
L. Kamaletdinova, A. Romanov and N. Yarushkina, "Industrial Data-Driven Control," 2024 International Russian Smart Industry Conference (SmartIndustryCon), Sochi, Russian Federation, 2024, pp. 62-67
|
||||
%\bibitem{Kamaletdinova-2024-2}
|
||||
%L. Kamaletdinova, A. Romanov and N. Yarushkina, "Industrial Data-Driven Control," 2024 International Russian Smart Industry Conference (SmartIndustryCon), Sochi, Russian Federation, 2024, pp. 62-67
|
||||
|
||||
\end{thebibliography}
|
||||
\end{document}
|
||||
|
||||
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Reference in New Issue
Block a user