Technical Condition Of Equipment Research Articles

Validation of JSC “Troitsa” berths bollards in the “Chayka” port of Severodvinsk

Berthing equipment of two berths of JSC “Troitsa” is considered. They are “Berth for petroleum products transshipment” and “Reinforced concrete berth with crane tracks” of “Chayka” port (Severodvinsk). The berths were put into operation half a century ago. Due to the regulatory service life expiration, the Scientific Research Association “Pleiada” LLC (Novorossiysk) carried out in 2021 a theoretical calculation to determine the bearing capacity of bollards. The purpose of the planned research is to check the safe functioning of the available mooring devices in the existing conditions of berthing facilities operation. The object of research is non-standard mooring bollards, for which there was no documentation on operational control during construction and commissioning certificates. The process of changing the technical condition and determining the bearing capacity of the bollards is considered as the subject of the research. It has been established that the mooring bollards of the studied berths are suitable for operation to ensure the mooring of vessels that do not exceed the design parameters and comply with the requirements of subparagraph (a) of paragraph 191 of the technical regulations on the safety of maritime transport facilities dated August 12, 2010 No. 620 (as amended on October 7, 2019). The results of organoleptic and instrumental examinations using a high-precision electronic dynamometer ACD/1R-250/7I-2 are presented. It is concluded that a correct assessment of the berthing equipment technical condition is achieved with proper organization of the expert service in combination with continuous deformation monitoring.

Sustainable Energy Safety Management Utilizing an Industry-Relative Assessment of Enterprise Equipment Technical Condition

The study considers approaches to ensuring energy management for the safe operation of facilities and their equipment and ways to improve it. It has been established that to ensure effective safety management of industrial enterprises, one of the critical areas is the technical diagnostics of power equipment during operation. An assessment of the actual technical condition of power equipment of VVER-1000 power units is proposed based on establishing the aging mechanisms and determining the relative evaluation coefficients for the characteristics of individual equipment elements. The results of the calculations allowed us to conclude that the obtained results correspond to the coefficients of relative assessment Ki of the technical characteristics of the power equipment that determine its degradation. Studies indicates that when assessing the state of power equipment, it is necessary to consider the presence and impact of the following operational factors that are not considered in the design calculations: loads, high levels of mechanical stress, fatigue damage, and metal defects, which primarily indicate the presence of degradation changes. To assess the technical condition of the equipment, considering the degree of mechanical wear, 17 technical characteristics were selected to determine the aging mechanisms by signs of degradation. A mathematical model of the dependence of the relative evaluation coefficient K on changes in the operating parameters is presented, and it is noted that the most significant influence on the value of the coefficient is the temperature of the coolant at the inlet (K = 0.56). The developed approach makes it possible to improve the safety management system of power facilities by introducing the proposed model to assess the technical conditions of power equipment by defining the parameters in the overall safety management system.

Recurrent Neural Network-Based Autoencoder for Problems of Automatic Time Series Analysis at Power Facilities

Digitalization of the energy sector leads to an increase in the volume and rate of data collection. A primary barrier to the proper management of the technological data is the lack of data labeling corresponding to emergency modes, power equipment technical state, etc. Thus, despite the large amount of data, there is a shortage of labeled data suitable for training, validating and testing the machine learning models. Labeling by an expert takes too much time, so there is an actual task to automatically identify data fragments that are potentially of interest. The aim of the work is to develop an algorithm for prioritizing the fragments of the time series using the compact recurrent autoencoder. To achieve the goal, a neural network architecture was developed based on recurrent encoding and decoding cells, capable of unsupervised learning. The model was tested on two data sets: a synthetic sinusoidal signal with missing values and electric current measurements with thermal limit deviations. The substantial results of the work are the compact architecture of the autocoding model and the high interpretability of the output. The most significant achievements of the study are both the autocoding neural network model, which does not require initial assumption about the type of deviations, and the proposed algorithm for prioritizing the data fragments. The significance of the results is prooved by the reduction of the time for analyzing and labeling large data arrays with technological parameters of the electrical networks, which allows using these data for training, validating and testing.

АНАЛИЗ СОВРЕМЕННЫХ МЕТОДОВ ДИАГНОСТИРОВАНИЯ ГАЗОПОРШНЕВЫХ АГРЕГАТОВ С СИНХРОННЫМИ ГЕНЕРАТОРАМИ НА ОБЪЕКТАХ ДОБЫЧИ НЕФТИ

Relevance One of the best available technologies for the utilization of associated petroleum gas and the generation of electrical energy is the use of gas piston units at oil and gas production enterprises. At the same time, limited funds for equipment repairs and unfavorable external conditions often force industrial enterprises to switch to a repair strategy based on technical condition. The type of organization of repairs based on the technical condition of the main power and electrical equipment can be used if the electric power industry entity has technical diagnostic tools and an automated system for monitoring the technical condition of the main equipment, which is a hardware and software complex that ensures the process of remote monitoring and control of the condition of the equipment and (or) object (operating equipment), its diagnosis and prediction of changes in technical condition based on collected data (historical data on the condition of the equipment) and operational data received from data collection systems installed on the equipment. At the same time, when organizing repairs based on the technical condition of equipment, the list and scope of repair measures should be determined based on the results of the performed monitoring and diagnosis of its technical condition. Thus, diagnostics and assessment of the technical condition, the emergency shutdown of which can cause the shutdown of the oil-producing well stock, is an important task, the solution of which helps to reduce operating costs, increase reliability, reduce the number of emergencies, reduce costs and losses of working time associated with unscheduled repairs of equipment and technological problems. Aim of research The aime of the study is to analyze the current methods for diagnosing gas piston units, as well as promising technologies in the field of diagnostics. Methods Analytical review of scientific literature. Results The article analyzes methods for technical diagnostics of gas piston units with synchronous alternating current generators, taking into account their advantages and disadvantages. Promising directions for the development of diagnostic systems of electrical machines are considered.

Development of a Comprehensive System for Assessing the Generating Equipment Technical Condition Using Neural Network Modeling

The article proposes an approach to solving the problem of implementing a neural network model for assessing the technical condition of a hydro generator. A set of universal indicators assessed without the need to shut down the equipment, which characterize its technical state, is proposed. Approaches to the formation of training and validation templates that provide high-quality neural network training are described. Functional evaluation system algorithms are proposed, and universal representation of relations between the objects of the mathematical model for determining the generating equipment remaining service life is formed. The obtained scientific research results open the possibility to apply the proposed system for comprehensively assessing the generating equipment technical condition with using neural network modeling to estimate the current remaining service life of generating equipment on the basis of available diagnostic data.

Predictive risks assessment of power supply interruption to oil production consumers, taking into account changes in significant factors

THE GOAL. To develop a decision-making procedure for the commissioning of equipment in the power supply system of oil production facilities in accordance with the minimum risk criterion. To substantiate the effectiveness of its practical application in the tasks of equipment repair management with the "technical condition" strategy based on a risk-oriented approach.METHODS. When solving the tasks, the following methods were used: the flowchart method for calculating the structural reliability indicators of the distribution electrical network with changes in its operational composition and technical condition of elements (equipment), the method for predicting the risks of power supply disruption in the circuit of each technological consumer, taking into account the possibility of reserving and imposing emergency recovery of the main element on the planned repair of the backup, scenario approach for determining scenarios for changing the risk of power supply failures when the operating state of the electrical network circuit changes.RESULTS. As a result of solving the problems, the values of the structural reliability indicators of the studied distribution electrical network are calculated under the most likely scenarios of changes in its operational composition and technical condition of equipment, the features of the integrated assessment of the technical condition of objects of voltage class 6 kV in their operating conditions are considered. The functions of forecasting the probabilities of power supply failures of oil production facilities are obtained depending on the integral assessment of their technical condition, which, along with the severity of the consequences of failures, make it possible to predict risks.CONCLUSION. A computational procedure has been developed that includes mathematical models and an algorithm for prioritizing equipment with a strategy "according to technical condition" when putting it into repair based on a forecast of the risks of power supply disruption to consumers. On a concrete example, the verification of calculation models and algorithm was carried out, the effectiveness of the developed computational procedure and its applicability in solving practical problems of managing repairs of electrical equipment of oil production facilities based on a risk-oriented approach was shown.

Technical Condition Monitoring for Telecommunication and Radioelectronic Systems with Redundancy

Abstract The telecommunication and radioelectronic systems with redundancy are widely used in different branches of human activity. To provide the necessary reliability level of equipment, the operation system is utilized. That system contains intended use, maintenance, repair, technical condition monitoring, and others. The damages, faults and failures are usually observed during the lifecycle of telecommunication and radioelectronic systems. They can lead to deterioration of equipment technical condition. The deterioration of technical condition can be detected during observation of diagnostic variable and reliability parameter. This article concentrates on the synthesis and analysis of statistical data processing procedure for deterioration detection while operating telecommunication and radioelectronic systems with redundancy. For the purpose of reliability estimation based on different redundancy methods, statistical data processing procedure synthesis was carried out using multiple hypothesis testing and detection criterion. The analysis problem was solved using Monte- Carlo simulation method, which allowed constructing operating characteristics. The obtained results can be used in the process of design and improvement of operation systems for telecommunication and radioelectronic equipment.

Influence of frequency and power control modes on the technical condition of hydraulic units of hydroelectric power plants

The features of the operation of hydroelectric power plants, which are used to regulate the frequency and power in the power system with a wide range of load variations and with varying degrees of intensity of operation mode variations, are considered. An approach is proposed to assess the impact on the reliability of hydraulic turbine equipment used in these modes. When examining the equipment of existing hydroelectric power plants, it has been revealed that the mode of automatic secondary regulation of frequency and power flows in the power system has the greatest impact on the technical condition of hydraulic turbines. Intense participation in regulation leads to an increase in dynamic loads on the hydraulic unit, loads on the friction pairs of the regulating elements of the turbine compared to the basic, semipeak and peak modes and leads to additional wear, reduced reliability and deterioration of the technical condition of the equipment. The operation in the automatic secondary regulation mode of hydraulic units at different HPPs differs depending on the function performed in the power system. To determine the loading of a hydraulic unit with transient load variation modes during operation in the mode of automatic secondary regulation of frequency and power flows, it is proposed to introduce a parameter characterizing the intensity of operation of hydraulic units of HPP in the aforesaid mode and to take it into account in order to accumulate data on the actual operating modes of hydraulic units and use it in assessing and predicting the technical condition of equipment.

Vibrodiagnostics as a predictive maintenance tool in the operation of turbo generators of a small hydropower plant

The most important goal of monitoring and analysis of vibration is to provide information on the operational and technical condition of equipment in order to ensure strategic planning and maintenance management. The article addresses integration of vibrodiagnostics into the system of predictive maintenance in the operation of turbo generators of a small hydropower plant. Small hydropower plants are specific for their ability to provide relatively stable output without unnecessarily burdening the transmission system. Modern turbines are expected to be highly efficient and reliable in operation. By including these devices into the predictive maintenance system, it is possible to identify their possibly unusual behavior, estimate the remaining service life, and draw attention to incorrect setting of operating parameters. The task of research in this area was to evaluate the applicability of data obtained from vibrodiagnostics to identifying turbo generator failures and key parameters that may have a potential impact on the behavior of this type of equipment.

Development of algorithm for implementation of technical condition-based repair program for generating equipment at energy enterprises

The use of modern digital technologies during the industrial revolution enables to revise a number of approaches to the organization of technological processes, in particular when introducing approaches to the implementation of repair programs of energy enterprises. Through the example of the network planning and control system, the use of the overhaul activity schedule for generating equipment is considered, taking into account the logging of asbuilt documentation in a digital system, which is a software and technical complex made up of a centralized data storage system and a decentralized registry. The improvement of equipment, the development of diagnostic tools and methods directly affects the maintenance and repair system. In the maintenance system under development, it is planned to make a transition from statistical empirical assessments of the technical condition of equipment to objective ones based on automated technical diagnostics systems. On the basis of an algorithm for implementing a technical condition-based equipment repair program for energy enterprises, the authors proposed an algorithm for conducting technical supervision and recording the stages of work performed in a distributed register, based on the capabilities of modern digital data collection and processing systems. Obtaining parameter values for further use in the operation of the proposed algorithm is carried out on the basis of the capabilities existing at energy facilities of information-measuring and control systems. The use of the network planning and management system in the form of a network schedule and the algorithm proposed by the authors for conducting technical supervision and recording the stages of work performed during repair activities will make it possible to objectively determine the amount of labor resources necessary to perform the planned scope of work within the specified period, identify critical works delaying the completion of repair, actively manage repair by analyzing the progress of repair work and making reasonable organizational and technical decisions that ensure the performance of work within the planned time frame and at the lowest possible costs.

Fault identification using a structural and functional approach for digitalization conditions

One of the fundamental components in equipment maintenance and repair systems that implement modern risk-oriented strategies is information. The effectiveness of taken decisions is determined not only by the volume, but also by the quality of information data. The article formulates and justifies the requirements for the collected information on the technical condition of equipment and data for the analysis of its failures, taking into account modern capabilities and conditions of universal digitalization. The role of mathematical modeling in the formation of data on the functional parameters of the machine is shown, the main stages of construction are outlined and a set of developed mathematical models of the refrigerator is presented. Methods of equipment fault analysis are considered, their characteristics are given, areas and application features are indicated. An algorithm for analyzing events related to the operability of equipment based on entries in the equipment maintenance log is described. The proposed approaches are demonstrated by the example of a refrigerator with walking beams and a hydraulic drive, which is an integral part of the technological line of the CCM and has the most characteristic features of metallurgical equipment, which allows to use the solution of the problem of collecting and processing information about the technical condition of the CCM refrigerator as a standard.

Технические решения по обнаружению перегрева элементов распределительных устройств напряжением 0,4–10 кВ

The article addresses matters concerned with safe monitoring of the electrical equipment technical condition and transfer of the majority of electrical installations operated by PJSC Mosenergo to the category of low-maintenance ones. A comparative analysis of technologies for automatically monitoring the heating of contact connections, including radio-channel, pyrometric and gas-analytical automatic overheating detection systems (AODS) is carried out. It has been shown that gas-analytical systems have both technical and economic advantages, which makes them a preferred choice when equipping 0.4–10 kV switchgears with systems for automatically detecting the overheating of contact connections. Criteria for selection of existing electrical installations recommended for being fitted with AODS have been developed. Technical requirements for automatic overheating detection systems of 0.4–10 kV switchgear components have been determined, and technical solutions have been developed for their application at PJSC Mosenergo facilities and for setting up automated monitoring of the technical condition of 0.4–10 kV switchgear components and transmission of data on the revealed defects to control centers. It has been planned to put the developed gas-analytical automatic overheating detection system in pilot operation at existing electrical installations of PJSC Mosenergo’s CHPP-12 branch.

Digitalizing the Process of Tracking Technical Condition of the Main Equipment of Energy Providing Enterprises

The existing power facilities of the Republic of Tatarstan face a number of disadvantages related to the system for recording data on the technical condition of boiler and turbine equipment: manual calculation of the parameters of equipment operation, lack of direct access to complete information on the condition of equipment and, as a result, low efficiency in identifying malfunctions, deviations in the operation of equipment, carrying out repair work. Hence, the need for digitalization of the system for recording data on the technical condition of equipment was formed, in response to which software was developed for automating the data recording system and visualizing the technical parameters of reliability at thermal power plants. This proposal is distinguished by the introduction of digital technologies in the process of recording data on the technical condition of equipment, where for the first time the software includes a method for automatically calculating the technical condition index of boiler units. The results of the work will reduce the likelihood of an emergency state of power plant equipment.

Digitalization and use of artificial intelligence technologies in technical modernization of the agro-industrial complex

The article presents the prospects of digitalization and use of artificial intelligence technologies in the technical modernization of the agro-industrial complex (AIC). In order to improve the efficiency of agricultural industry, including the productivity and quality of products in such sectors as crop production, livestock, processing of agricultural products, a transition to innovative technologies is required. Their use in agricultural production makes it possible to significantly competitiveness of the agricultural sector of the economy of the Russian Federation. To do this, the technical modernization of the agro-industrial complex, which provides for the renewal of its base with domestic agricultural machinery is required. In modern machines, a large number of electronic systems with various sensors are involved. They allow to control the operation of various units, including the internal combustion engine, transmission, work tools and other mechanisms. The use of such systems makes it possible to reduce the cost of maintenance and use of equipment, to monitor the modes of operation and technical condition of equipment around the clock, to conduct repair and maintenance activities as required. The current global trend is the use of remote machine diagnostics systems. They allow service centers and emergency support to diagnose a vehicle at a distance, reducing downtime. In recent years, there has been a trend towards digital solutions in machine maintenance. 3D technologies are promising for repair of agricultural machinery, including restoration and hardening of parts. They can be used to measure the geometric dimensions and determine the physical and mechanical properties of part faces of agricultural machines during the incoming inspection of spare parts and fault detection of parts, by scanning them. The introduction of digital technologies and artificial intelligence in the repair practice will reduce the duration of repair and maintenance actions during the technical service of agricultural machinery and significantly reduce the cost of their implementation.

Снижение влияния неопределенности исходной информации в интеллектуальных автоматизированных системах управления тепловыми процессами при обеспечении надежной эксплуатации энергетического оборудования

The uncertainty of the source information is used to solve key tasks in an intelligent automated thermal process control system affects the calculation of control actions, the implementation of equipment optimal operating modes and, as a result, leads to degraded reliability. As a rule, this type of information can be qualitative (the use of expert knowledge) or quantitative in nature. In this regard, it is extremely important to reduce the impact of uncertainty. The aim of the study is to identify the types and origins of uncertainty in the source information used by an intelligent automated process control system and to develop approaches to reduce its impact on the reliability of power equipment operation. The approaches used to ensure the specified indicators of reliability, efficiency and environmental friendliness in modern intelligent automated process control systems are based on predictive strategies, according to which the technical condition of equipment and specific degradation processes are predicted. This means that various types of uncertainty can have a significant negative impact. To reduce the influence of uncertainty of the initial information that affects the reliability of power equipment operation, the use of artificial neural networks is proposed. Their application opens the possibility to predict the occurrence of equipment defects and failures based on retrospective data for specified forecast time intervals. A method for reducing the impact of anomalies contained in the source information used in an intelligent process control system for energy facilities is demonstrated. Data omissions and outliers are investigated, the elimination of which reduces the impact of uncertainty and improves the quality of solving key problems in intelligent automated process control systems. Experimental studies were carried out that made it possible to identify the mathematical methods for removing omissions and anomalies in the source information in the best way. Methodological aspects of eliminating various types of uncertainty that exist in managing of power facilities by means of intelligent automated process control systems at the key stages of the power equipment life cycle are described.

ИНТЕЛЛЕКТУАЛЬНАЯ СИСТЕМА ДИАГНОСТИРОВАНИЯ ПАРАМЕТРОВ ТЕХНИЧЕСКОГО СОСТОЯНИЯ СЕЛЬСКОХОЗЯЙСТВЕННОЙ ТЕХНИКИ

Research aimed at the development and implementation of new digital methods and intelligent systems that allow improving the diagnostic process, increasing the reliability of determining the functional characteristics of agricultural tractors in an online mode is an important and relevant component of technologies and tools for servicing agricultural machinery. The paper presents the results on the justifi cation and development of an intelligent system for diagnosing machines based on the interaction of a neural network. The advantage of this diagnostic system is the ability to maintain the working condition of agricultural machinery, in case the processes of diagnosis and analysis of the obtained data are automated. The use of an intelligent tractor diagnostic system provides for the determination of not only the cause of failure by the controlled parameters, but also the effi ciency evaluation of the machine as a whole. It is established that one of the ways to increase the effi ciency of using agricultural machinery is the non-contact determination of the parameters of the technical condition of equipment through the modernization of the technology for monitoring the technical condition of machines and the quality of work on the basis of the introduction of modern intelligent and telemetric systems.

Systems of vibration parameters automated control for diagnostics of equipment technical state

The article deals with the issues of the spatial direction of measurement, which is optimal in terms of recognition of the state of a particular unit node and preparation of control points for diagnosis. To form a diagnostic feature, the representation of the signal in a rather narrow range has been used, for instance, in the area of one of the harmonics of the fundamental mechanism excitation frequency. To process such signals, an integrated programming environment developed in the style of Microsoft Visual products has been created. To ensure the requirements of optimal vibration analysis, the structure of stationary equipment control systems has been proposed. Analysis of random vibration of the diagnosed object is done using real-time two-channel analyzers. Each analyzer channel has a processor for fast Fourier transform and operational information. The two available channels can allow assessment of the state of the object by special correlation functions. In contrast to the known methods, the established methods of determining the frequency range of defects of various light industry machine nodes differ in taking account size of parts and equipment nodes, and the possibility to use narrow-band filtering. The analysis of possibilities to install vibration sensors has been carried out.

An Technical Condition Evaluation Index Reduction Method Combining Rough Set and Genetic Algorithm

The evaluation of equipment technical condition involves lots of indexes and the relationship between different indexes is complex. It brings a huge workload to the collection of evaluation information, and the interference caused by the correlation between indicators also increases the uncertainty of the evaluation results. Aiming at the problems mentioned above, this paper proposes a reduction method of equipment technical condition evaluation index system based on rough set and genetic algorithm. The rough set and genetic algorithm are used to reduce the state evaluation index, the redundancy is removed, and the best indexes reflecting the change of equipment technical condition status are retained. A reduction case of a gas turbine technical condition status evaluation index system shows that this method can reduce a complex index system to a more concise and clear index system without losing its representativeness and systematicity. This method can reduce the difficulty of information collection, and improve the accuracy of the evaluation results.

Методология управления ремонтами оборудования в электрических сетях нефтепромыслов

An innovative methodology for managing repairs in distribution networks and power supply systems for oilfield facilities is proposed in this paper. The methodology makes it possible to select optimal solutions in accordance with the priority management goal by formalizing a risk-based model for planning maintenance and repair (M&R) of electrical equipment with a "condition-based maintenance" strategy. One of the main options in the complex of M&R management tasks is an adaptive model of the technological map for the repairs of power facilities, formed in compliance with a number of key principles. The developed model allows you to optimize the volume and complexity of repairs of electrical equipment of a certain type based on the minimum composition of technological operations necessary to eliminate the identified defects. Detection of defects in electrical equipment is achieved by using methods of technical diagnostics and non-destructive testing. In this case, each of the defects is recorded in the defect statement for the object, with the determination of the degree of criticality and fixing a specific type of corrective action. Optimal prioritization of electrical equipment repairs is performed in accordance with the minimum risk function under current restrictions on reliability (an equipment technical condition index) and available resources (total maintenance costs). The originality of the implementation of a key risk event – a power supply failure of an oilfield facility provides calculating the probability and severity of the consequences of an electrical equipment failure taking into account its technical condition and features of the power supply scheme. The given example illustrates the effectiveness of the proposed approach and emphasizes the validity of optimal decisions. The key components of the developed methodology have found practical application in the conditions of real operation of the equipment of oilfield facilities.

Машинное обучение как инструмент повышения эффективности управления жизненным циклом высоковольтного электрооборудования

The purpose of the study is to analyze the practical implementation of high-voltage electrical equipment technical state estimation subsystems as a part of solving the lifecycle management problem based on machine learning methods and taking into account the effect of the adjacent power system operation modes. To deal with the problem of power equipment technical state analysis, i.e. power equipment state pattern recognition, XGBoost based on gradient boosting decision tree algorithm is used. Its main advantages are the ability to process gapped data and efficient operation with tabular data for solving classification and regression problems. The author suggests the formation procedure of correct and sufficient initial database for high-voltage equipment state pattern recognition based on its technical diagnostic data and the algorithm for training and testing sets creation in order to improve the identification accuracy of power equipment actual state. The description and justification of the machine learning method and corresponding error metrics are also provided. Based on the actual states of power transformers and circuit breakers the sets of technical diagnostic parameters that have the greatest impact on the accuracy of state identification are formed. The effectiveness of using power systems operation parameters as additional features is also confirmed. It is determined that the consideration of operation parameters obtained by calculation as a part of the training set for high-voltage equipment technical state identification makes it possible to improve the tuning accuracy. The developed structure and approaches to power equipment technical state analysis supplemented by power system operation mode data and diagnostic results provide an information link between the tasks of technological and dispatch control. This allows us to consider the task of power system operation mode planning from the standpoint of power equipment technical state and identify the priorities in repair and maintenance to eliminate power network “bottlenecks”.