@ -215,7 +215,34 @@ $Y=\{y^i\}, i = [1,n], n \in N$, -- $n$ состояний интегрируе
Таким образом, происходит процесс принятия решений на основе иерархической нечеткой базы правил с нечетким логическим выводом.
\section{Иллюстративный пример иерархической базы правил}
В качестве примера рассмотрим полученную базу правил на основе информационной системы, предназначенной для обработки заявок, faveo service desk \cite{faveo}.
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}.
На основе метамодели строится база правил первого уровня, отображенная на рисунке \ref{fig:rules1}. Первый уровень содержит лингвистические термы, отображающие зависимость ключевых процессов от входных данных. Входные данные предварительно были преобразованы в лингвистическое описание.
\caption{База правил первого уровня информационной системы faveo}
\label{fig:rules1}
\end{figure}
При сравнении рисунков \ref{fig:metamodel} и \ref{fig:rules1} видны отличия между названиями входных сущностей для метамодели и входных данных, представленных в виде лингвистических термов в условии правила. Это связано с тем, что входные данные преобразовываются перед тем как формировать базу правил первого уровня.
При переходе по ссылке одного из правил, представленных на рисунке \ref{fig:rules1}, динамически формируется база правил второго уровня. На рисунке \ref{fig:rules2} представлен пример базы правил второго уровня для ключевого процесса ticket\_thread, отображенного на рисунке \ref{fig:metamodel}.
\caption{База правил второго уровня информационной системы faveo для ключевого процесса ticket\_thread}
\label{fig:rules2}
\end{figure}
Условия правил содержат ключевые параметры с целочисленными значениями. В процессе нечеткого логического вывода осуществляется поиск подходящего правила с использованием функции принадлежности, в результате которого формируются понятные для пользователя данные, соответствующие состоянию ключевого процесса при переданных входных данных.
Пример вывода результата представлен в таблице 2.
Таким образом, при заданных входных данных формируется результат, который отражает состояние ключевого процесса информационной системы faveo service desk. Этот вывод подтверждает целесообразность применения предложенного подхода, так как сформированный результат отражает реальные данные при работе с системой заявок faveo service desk и получение результата не зависит от запуска самой интегрируемой информационной системы, что является преимуществом при отображении данных и параллельной работе интегрируемой информационной с комплексом PLM.
\section{Управляющая система в общем комплексе управления}
@ -312,5 +339,8 @@ Kamaletdinova, Lilia \& Romanov, Anton. (2024). Control System Design (In russia
\bibitem{faveo}
Продукт службы поддержки 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
@ -204,16 +204,61 @@ 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 an information system designed for processing requests, Faveo Service Desk \cite{faveo}. "
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}.
\caption{Metamodel of the Faveo information system}
\label{fig:metamodel}
\end{figure}
Based on the metamodel, a first-level rule base is built, as shown in Figure \ref{fig:rules1}. The first level contains linguistic terms that reflect the dependency of key processes on input data. The input data has been transformed into a linguistic description beforehand.
\caption{Base of rules of the first level of the Faveo information system}
\label{fig:rules1}
\end{figure}
When comparing the figures \ref{fig:metamodel} and \ref{fig:rules1}, differences can be seen between the names of the input entities for the metamodel and the input data presented as linguistic terms in the rule condition. This is because the input data is transformed before forming the first-level rule base.
When following the link of one of the rules presented in figure \ref{fig:rules1}, a second-level rule base is dynamically formed. Figure \ref{fig:rules2} shows an example of a second-level rule base for the key process ticket\_thread, depicted in figure \ref{fig:metamodel}.
\caption{Base of rules of the second level of the Faveo information system for the key process ticket\_thread}
\label{fig:rules2}
\end{figure}
\section{Control system in the overall control complex}
The conditions of the rules contain key parameters with integer values. In the process of fuzzy logical inference, a search for a suitable rule is carried out using the membership function, as a result of which user-friendly data corresponding to the state of the key process with the given input data are generated.
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.
Figure \ref{fig:control_system} shows the process of organizing the management of an information system with the involvement of a control system.
\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}
@ -300,5 +345,8 @@ Kamaletdinova, Lilia \& Romanov, Anton. (2024). Control System Design (In russia
\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
@ -55,20 +55,7 @@ We propose using data-based management by forming a data meta-model of the integ
\section{Introduction}
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.
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.
The figure \ref{fig:state} shows the state graph of the integrable information system.
\caption{State graph of an integrated information system}
\label{fig:state}
\end{figure}
The figure presents the major states of the integrable IS used in this work. $S1$ represents the operational state of the system, $S2$ represents the non-operational state or failure state, and $S3$ represents the limit state of the system \cite{Checkal-2012}. The operational state of the system is when the system continues to perform its primary tasks while its data is accurate. The limit state of the system is when the system continues to perform its primary tasks while the data is in a boundary state. The non-operational state or failure state of the system is when the system cannot continue performing its primary tasks and requires intervention from the DM. While in states $S1$ and $S3$, the system can transition to the failure state, so the main goal of management is to keep the system in state $S1$ and allow it to return to this state without risks if it transitions to state $S3$.
It is worth separately considering the process where the interaction between the integrable IS and the entire PLM complex involves the operator. In this process, the amount of work required by the operator for setting up interaction, data display, and maintaining the system’s operational state is considerable. The role of the operator in this process is shown in figure~\ref{fig:interaction}.
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.
@ -76,60 +63,30 @@ It is worth separately considering the process where the interaction between the
\label{fig:interaction}
\end{figure}
It is worth separately considering the process where the interaction between the integrable IS and the entire PLM complex involves the operator. In this process, the amount of work required by the operator for setting up interaction, data display, and maintaining the system’s operational state is considerable. The role of the operator in this process is shown in figure~\ref{fig:interaction}.
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.
\subsection{Overview of existing methods and approaches to management}
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.
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:
\begin{enumerate}
\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;
\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;
\item reducing the risks of transitioning to a failure state \ref{fig:state} and maintaining the system in a working state without the involvement of managers in this process.
\end{enumerate}
\subsection{Overview of existing approaches to rule base formation}
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:
\begin{enumerate}
\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: $x_1=\{l_{11},l_{12}\}\wedge x_2=\{l_{23}\}\wedge ... \wedge x_n=\{l_{n1},l_{n2}\}\rightarrow y = Y$.
\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.
\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 this case, input data is matched with one of the class labels.
\end{enumerate}
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.
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.
\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 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}.
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}.
\caption{Structure of the hybrid base with fuzzy logical inference}
\label{fig:kb-structure}
\end{figure}
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.
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 $OUT=\{OUT_1, OUT_2, ..., OUT_w\}, w \in N$ 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:
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}
P(INP) \rightarrow\{INP^{OUT_s}\}, OUT_s,
\end{equation}
@ -193,24 +150,63 @@ The algorithm presented in Figure \ref{fig:algorithm} consists of the following
\item In the final stage (generate out), suitable output data are formed as key-value data tuples ($out1=7$) of different types (integer, string, date, and boolean variables). The final stage uses the data base of the integrated IS and the result of rule execution ($y^i$) based on the second-level rule base as input data.
\end{itemize}
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 an information system designed for processing requests, Faveo Service Desk \cite{faveo}.
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}.
\caption{Metamodel of the Faveo information system}
\label{fig:metamodel}
\end{figure}
Based on the metamodel, a first-level rule base is built, as shown in Figure \ref{fig:rules1}. The first level contains linguistic terms that reflect the dependency of key processes on input data. The input data has been transformed into a linguistic description beforehand.
\caption{Base of rules of the second level of the Faveo information system for the key process ticket\_thread}
\label{fig:rules2}
\end{figure}
When comparing the figures \ref{fig:metamodel} and \ref{fig:rules1}, differences can be seen between the names of the input entities for the metamodel and the input data presented as linguistic terms in the rule condition. This is because the input data is transformed before forming the first-level rule base.
When following the link of one of the rules presented in figure \ref{fig:rules1}, a second-level rule base is dynamically formed. Figure \ref{fig:rules2} shows an example of a second-level rule base for the key process ticket\_thread, depicted in figure \ref{fig:metamodel}.
The conditions of the rules contain key parameters with integer values. In the process of fuzzy logical inference, a search for a suitable rule is carried out using the membership function, as a result of which user-friendly data corresponding to the state of the key process with the given input data are generated.
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 in the overall control 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.
Figure \ref{fig:control_system} shows the process of organizing the management of an information system with the involvement of a control system.
\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:
\begin{enumerate}
\item The possible reaction of the integrated IS to input data exists;
@ -261,17 +257,9 @@ 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)
@ -292,5 +280,9 @@ Kamaletdinova, Lilia \& Romanov, Anton. (2024). Control System Design (In russia
\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
