The article discusses the issues of information and algorithmic support of the expert system for assessing the operating modes and technical condition of electrical equipment. The peculiarities of the considered diagnostic method is the analysis of diagrams that are highly sensitive to insignificant deviations of the values of the signal parameters (current consumption) from the reference values, as well as stability during the analysis period. The results of computer modeling and experimental research of electrical equipment are described in order to obtain a set of reference diagrams. The possibility of using the method in intelligent systems of electrical equipment real-time monitoring is shown.
The article considers the issues of practical application of the approach on performance automation and scheduling of works on the maintenance of railway automatics and remote controlling devices in the process of planned preventive maintenance realization which includes the analysis of known ways to automate railway circuit inspections on shunt sensitivity and shunt effect performance control in the process of finding a moving unit on a rail circuit. At present, topical problem is the reduction of labor costs on the maintenance of dispersed objects of automatics and remote control which to solve for, we propose to use algorithms providing for technological situation identification and determination of the many of works which will be considered automatically as accomplished as well as the many of ones which will be needed to be accomplished in due time. The essence of the proposed approach to automation of being considered processes is in the following: if in the process of rail circuit functioning in regular mode, the situation, that’s analogous to the performance of work on shunt sensitivity testing (that’s stipulated by work operation technology), is realized, then work is considered to be accomplished at the presence of means of reliable control for the values of rail circuit parameters.
This article considers a row of issues and problems in the area of optimal planning of jobs on technical servicing of railway automation and telemechanics devices in the process of planned-preventive servicing realization including the analysis of modern level and possibilities to increase task number being solved during automation of planning. Technologies for infrastructure device servicing, providing for minimizing and ideally - for influence elimination of so-called «human factor» on work implementation quality, are considered presently to be the most promising. One of the ways to reduce unproductive resource costs is to develop new approaches to technical servicing planning at that to, both, long-range planning (distribution of individual jobs by dates) and operational planning (making up an order to fulfill individual jobs within a shift). A vital task is a work planning automation in the frames of planned-preventive servicing of railway automation and telemechanics devices on the basis of fixation of technological situations that’re equivalent to functioning imitation of devices in various modes. In order to realize the set task, it’s proposed to develop corresponding digital models, which provide for technological situation identification and for the definition of work set, which will be considered as implemented, as well as the set of activities, which’re needed to be implemented on time.
The article is devoted to the problem of complex interconnected implementation of tasks of predicting the state of technical means of automation and telemechanics and train traffic control processes within a digital platform of predictive analytics of infrastructure objects. A general approach to the construction of a multilevel hierarchical prediction model is described. A three-level model is considered, in which the objects of prediction are: on the upper level - the process of train traffic control, on the middle level - routes in stations and block sections on track between stations, on the lower level - devices of railway automation and telemechanics. The prediction tasks at each level of the model are formulated. Criteria for selecting the f predicting period at each level of the model are proposed.
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