The article presents a method of fuzzy cognitive modeling for heterogeneous electromechanical systems (HEMSs) in the management of innovative design solutions. During the operation of the HEMSs, as a result of their operational aging, the properties of the windings parametric matrices and the HEMSs vector space properties change. Periodic testing of the HEMSs vector space allows obtaining reliable information about the current technical condition of the HEMSs, about its changes during operation and about the risks of operating capability loss. At the same time (I) the presence of proportional changes in signals during sequential testing indicates the homogeneous operational aging of the HEMSs and its rate; (II) a disproportionate change in one of the signals indicates the damage or the development of a heterogeneous aging process; (III) a change in signals with a change in the angular position of the rotor indicates worn bearings or damage of the HEMSs rotor. The article presents the HEMSs model, describes the method for the topological research of the vector space and the method for forming the diagnostic matrices. The deviations of their elements are fuzzy due to the uncertainty of the load, influencing environmental factors and unstable supply voltages. Therefore, for predictive estimation of the HEMSs state, it is proposed to use fuzzy relational cognitive models that allow implementing a completely fuzzy approach to modeling problem situations in these systems. The presented data confirm the growth of the HEMSs heterogeneity under conditions of uncertainty of external influences. The proposed method for predictive estimation of the HEMSs state, based on fuzzy relational cognitive models, provides resistance to an increase in the uncertainty of the estimation results for various models of system dynamics due to a reasonable set of fuzzy vector-matrix operations.
The results of studies aimed at developing multi-level decision-making algorithms for management of energy and resource efficiency, technogenic and environmental safety of a complex multi-stage system for processing fine ore raw materials are presented (MSPFORM). A distinctive feature of such a system is its multidimensionality and multiscale, which manifests itself in the presence of two options for implementing technological processes for processing finely dispersed ore raw materials, the need to take into account the interaction of the aggregates included in the system, as well as the hierarchy of describing the processes occurring in them - mechanical, thermophysical, hydrodynamic, physical and chemical. Such a variety of processes characterizes the interdisciplinarity of research and the complexity of obtaining analytical, interconnected mathematical models. This situation inspired the analyze use of artificial intelligence methods, such as deep machine learning and fuzzy logic, to describe and analyze processes. The scientific component of the research results consists in the developed generalized structure of the MSPFORM, the conceptual basis of multilevel algorithms for evaluating and making decisions on the optimal control of this system, the proposed composition of the parameters and the form of the optimization criterion. The task of the study was to analyze possible options for the processing of ore raw materials, to develop a concept for the construction of the MSPFORM allowing the possibility of optimizing its functioning according to the criterion of energy and resource efficiency while meeting the requirements of environmental safety. The application of evolutionary algorithms for solving the problem of optimizing the MSPFORM according to the criterion of minimum energy consumption is announced and its stages are specified. The structure of the block of neuro-fuzzy analysis of information about the parameters of processes in MSPFORM is presented, which is based on the use of deep recurrent and convolutional neural networks, as well as a fuzzy inference system. The results of a simulation experiment on approbation of the software implementation of this block in the MatLab environment are presented.
The paper presents a method, a mathematical model, and a computer program for the operational diagnostics of an electromechanical system (EMS). During EMS operation, service aging changes the properties of the parametric matrices of the windings and, as a consequence, the characteristics of the EMS vector space. Periodic testing of the vector space offers relevant and reliable data on the current health of the EMS, its changes during operation, and the risk of loss of function. The object of the study is an asynchronous electric motor (AEM). It is urgent to automate the process of assessing the current health of an AEM and to organize the storage of information on its states at different stages of its life cycle. To solve the problem, software (SW) for accumulation of information on AEM operation and for evaluation of its basic performance metrics has been developed in the Python programming language. The SW is based on the topological approach to diagnostics, which implies the analysis of the current responses of motor rotor windings to phase voltage pulses. The SW enables one to determine the rate of the service aging of an item, the probability of its survival and residual life, to obtain access to the history of previous diagnostics, and to visualize the in-service history of the above-mentioned performance metrics. The developed SW can be used to increase the AEM operation efficiency and to plan engineering or repair work; it can also be used as an information source for re- engineering and modification of existing AEMs. The described SW can be extended to perform operational diagnostics based on the topological approach of devices of various types. Also, this SW can be considered as a separate information component of the digital twin of a complex EMS, which will allow us to study the main indicators of its reliability, fault tolerance and operational efficiency at all stages of the life cycle.
The oil industry is the leading sector of the Russian economy, that makes the largest contribution to the country’s budget, creates a huge number of jobs and fully meets the domestic needs for oil and its products. In Russia, transportation of crude oil from fields to consumers (primarily refineries) is carried out by 5 modes of transport. Pipeline transport has received the greatest distribution. It provides transportation for 83% of crude oil and 30% of oil products. The most important element of the pipeline system is tank parks, which are used to collect and store oil at the junctions of technological pipeline sections and transshipment to other modes of transport. They are especially dangerous industrial objects. Therefore, they are subject to extremely stringent design and construction requirements. The most important stage in the construction of a tank park is the site selection, which is carried out on the basis of economic criteria and engineering requirements. In order to reduce the number of options for its location, where the survey party will travel, it is proposed to conduct a preliminary selection of the most promising territories by solving the task of multi-criteria optimization. The presence of a huge number of criteria leads to the need to use heuristic methods, among which swarm optimization algorithms based on modeling the collective behavior of various living organisms are widely used. To solve this problem, it is proposed to use bacterial optimization algorithms that allow taking into account both favorable and negative factors. Fuzzy logic elements can be added to the classical algorithm (it is proposed to set the initial positions of bacteria using fuzzy-logical inference systems, where the available statistics and expert assessments will be input parameters). In general, the proposed approach can be used to select sites for the construction of various hazardous industrial facilities, for which a large number of parameters must be taken into account.
The results of studies on the development of the structure of an intelligent model for managing the risks of violation of the characteristics of electromechanical devices in a multi-stage system for processing ore raw materials are presented. Such devices are involved in all cycles of the technological process, so the assessment of this risk for them is an urgent task. A method for assessing such risks is proposed, which is based on the assessment of the useful life of equipment, performed on the basis of the prediction of characteristics by a deep recurrent neural network, with further generalization of the results of such an assessment in a fuzzy inference block. Recurrent neural networks with long short-term memory were used, which are one of the most powerful tools for solving time series regression problems, including predicting their values for long intervals. The use of deep neural networks to predict the characteristics of electromechanical devices made it possible to obtain a high prediction accuracy, which made it possible to apply a relatively less accurate recurrent least squares method for the iterative process of estimating the useful life of equipment. This approach made it possible to build a computational evaluation process with its constant refinement as new results of measurements of the characteristics of electromechanical devices become available. The results of a model experiment with a software implementation of the proposed method, performed in the MatLab 2021a environment, are presented, which showed the consistency of the program modules and obtaining a risk assessment result that is consistent with the expected dynamics of its change.
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