Purpose. Improving the productivity and energy efficiency of complex technological complexes through the development and use of scenario-cognitive modeling in control systems. Methodology. Fuzzy cognitive maps, in the form of a weighted oriented graph, were used to develop a scenario-cognitive model. As a result of the conducted research studies, a new strategy of generalization of an expert estimation of mutual influences of concepts on the basis of methods of the cluster analysis is offered. Findings. Based on experimental research and object-oriented analysis of a complex technological complex, a structure of a fuzzy cognitive model is created. A scenario-cognitive model in the form of a weighted oriented graph (fuzzy cognitive map) has been developed, which illustrates a set of connections and the nature of the interaction of expertly determined factors. To solve the problem of impossibility of operative interrogation of experts in case of change in parameters of functioning of difficult technological complexes, expert estimations of values of weight coefficients of mutual influence of concepts are received. Cluster analysis methods were used to group expert assessments and determine a single value as a result of the research. The results of the scenario-cognitive modeling of the enterprise showed that production shutdowns and abnormal situations related to the failure of electrical equipment, deviations of the technological regime and the quality of wastewater treatment have a significant impact on the dynamics of productivity, energy efficiency and efficient use of equipment. Originality. The new scenario-cognitive model developed for forecasting the situation in the absence of accurate quantitative information consists in creating a fuzzy cognitive map, for modeling which many parameters of complex technological complexes are expertly determined. Using the developed methodology, a degree of interaction of these parameters is found, which allows determining dynamics of change in target criteria of functioning under various management strategies. Practical value. On the basis of the created scenario-cognitive model, software has been developed which allowed analyzing dynamics of change in productivity, energy efficiency and efficiency of use of the equipment under possible scenarios of functioning of difficult technological complexes is developed.
Context. Modern intelligent systems of failure identification of control equipment and devices in food industry are based on a complexation of approaches implemented on various methods and algorithms. The feature of such systems is that within them operates a large amount of heterogeneous data and knowledge that are difficult to combine. The use of ontologies of different levels in the system development process solves this problem.
Objective. Domain ontology development for equipment condition monitoring system as a basis for designing intelligent decision support system with ontology knowledge base.
Methods. There are different ontology development approaches. They may differ in the quantity of levels and types of ontologies or be a combination of subject and problem domains ontologies depending on the complexity of the problem and the chosen ontology development method. This paper represents two levels of the three-level ontology being developed for intelligent condition monitoring system of control equipment and devices. The upper level is represented by top-level ontology Basic Formal Ontology (BFO) which provides systematization of the meta-level, including temporal part. International standards and technical reports such as IEC 62890, ISO 55000, ISA 95, ISA 106, IEC 62264, ISO 10303-242: 2020 are considered in the development process of the second ontology level – Domain ontology.
Results. The article provides Domain ontology for equipment condition monitoring system in food industry. The developed Domain ontology systematizes, structures engineering knowledge and uses BFO which provides a set of basic elements at the metalevel. They set the values of the following entities: type of production, methods of failure identification, causes, failures, events, equipment, etc. The developed Domain ontology has semantic cross-links. A fragment of the Domain ontology relationships for the “Control equipment” subclass of “Equipment” class is also presented in the paper.
Conclusions. The developed ontology can be used to analyze the knowledge base on the causes, locations and types of failures and their identification methods. The developed ontology is a basis for application ontology development.
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