Operating Conditions Of Equipment Research Articles

Методи обґрунтування запасів запасних частин у системі управління транспортним процесом

The article formulates directions and analyzes the possibilities of substantiating and choosing the most optimal variant of applying mathematical models for forecasting the number and range of spare parts. The provided recommendations allow to take into account the multi-brand fleets of motor transport enterprises and trends for extrapolation in the short term, identified by the results of observations in real operating conditions. The main reasons for the occurrence of prolonged vehicle downtime due to technical malfunctions are considered. Traditional methods of planning spare parts inventories based on the use of reliability theory and results of mileage forecasting, standard time to failure; inventory management theory and statistical methods for forecasting random processes; methods of operations research; economic and mathematical methods; stochastic methods of transformation of random variables are analyzed. The article formalizes the grouping of factors that affect the size of stocks and the range of spare parts in the operation of rolling stock of enterprises, depending on the state of the system of organization of work of services for maintenance and repair of vehicles; structure of the fleet and technical characteristics of vehicles; level of development of the production base; availability, interest and qualification of personnel; operating conditions of automotive equipment. It is proposed to improve the logistics of spare parts inventory management and to formulate measures to improve the performance of carriers based on an assessment of the total costs incurred by the enterprise to maintain the reliability of rolling stock in operation and taking into account the amount of lost profit due to vehicle downtime in the technical service units. At the same time, it is recommended to use the mathematical apparatus of fuzzy set theory for decision-making, which allows formalizing the parameters of vaguely defined expectations of consumers of motor transport services and reducing the subjectivity of decision-making when solving problems of optimizing spare parts stocks in the transport process management system based on the analysis of the structure of consumer properties and service quality indicators.

Investigations on interpretable nuclear-grade hydraulic damper monitoring and diagnosis from multi-source high-dimensional profile data

Condition monitoring and fault diagnosis is important to ensure smooth machine operation, in which the use of multi-source high-dimensional profile data composed of multiple physical signals collected by multiple sensors for machine health monitoring and fault diagnosis has not been fully explored. In this paper, interpretable monitoring and diagnostic methods are proposed to analyze multi-source high-dimensional profile data and identify equipment operational status. Firstly, polynomials of segmenting profiles as well as a Lasso model are established to obtain sparse features. Then, for offline data with historical fault labels, an optimal linear hyperplane classifier is constructed to interpretably study the impact of different features on a decision. For online data with only normal samples, constructing an optimal hypersphere enables the measurement of operational status. Large-scale nuclear-grade hydraulic damper profile data are used to demonstrate how the proposed interpretable models work and show their physical interpretation for monitoring and fault diagnosis.

Research on Partial Discharge Characteristics of Suspension Defect in GIS under the Action of Voltage and Current

Abstract Gas insulated switchgear (GIS) is a critical component in power system, and the insulation testing is crucial before putting it into operation. Despite passing AC withstand tests, some GIS equipment exhibit insulation faults during operation. Existing AC withstand tests fall short in detecting all insulation faults in GIS equipment. To address this issue, a proposal is made to conduct a comprehensive simulation of actual GIS equipment operating conditions through a combined voltage and current boost test, aiming to address the limitations of existing tests. This study, based on a 330kV GIS test platform, compares the partial discharge characteristics of suspension defects under traditional voltage-boosting mode and the combined voltage and current boost mode. The research investigates the influence of simultaneously applying voltage and current on discharge characteristics. It is observed that the voltage and current boost combined mode significantly increase the partial discharge inception voltage (PDIV) and the corresponding apparent discharge compared to other modes. The apparent discharge quantity after a certain period of current flow during the discharge development process is greater than that observed in other modes. The current notably enhances the ultrasonic amplitude of partial discharge. These findings establish an experimental foundation for conducting voltage and current combined boost tests in the field.

Monitoring the operating state of crystal growth process based on digital twin model

The reliable assessment of the operational status during the silicon single crystal growth process is a prerequisite for ensuring system safety and improving crystal quality. However, in the actual silicon single crystal growth process, due to limitations in manpower, material resources, financial resources, and current technical methods, the establishment of monitoring models is still in its infancy. To address this issue, this paper proposes a hybrid deep belief network (HDBN) algorithm aided by the digital twin (DT) model to achieve real-time monitoring of equipment operational status. Firstly, this study constructs the DT model based on the basic principles of crystal growth, mainly to achieve high-precision simulation of the actual silicon single crystal growth process and generate abnormal data for the equipment. This operation can expand the sample set, enhance the diversity and coverage of data, and effectively solve the problem of insufficient sample size. Secondly, this study uses the variational mode decomposition (VMD) algorithm to decompose the dataset composed of obtained abnormal and normal data, and constructs sub-deep belief network (DBN) for the decomposed subsequences to capture deep feature information at different frequencies of the data. Subsequently, based on the concept of ensemble learning, the outputs of each sub-DBN network are used as inputs to construct the overall DBN network, achieving monitoring of the equipment operational status. Through the combination of VMD decomposition and DBN networks, this algorithm can better capture the frequency characteristics and time-domain features of the signal, enhancing monitoring accuracy. Experimental results show that this algorithm can accurately identify abnormal equipment states, effectively improve monitoring performance, and is of significant importance for the optimization and control of the semiconductor-grade silicon single crystal growth process, contributing to increased production efficiency and product quality.

The effect of different active diluents on the properties of epoxy materials

Abstract Epoxy resin is widely used in the insulation packaging field of electrical equipment due to its advantages such as low cost, excellent insulation, and mechanical properties. However, the thermal conductivity and the heat resistance of domestic epoxy insulation packaging materials make it difficult to meet the increasingly demanding operating conditions of electrical equipment. This article investigated the effects of active diluents with different functional group numbers on the glass transition temperature, mechanical strength, and low-temperature cracking resistance of epoxy materials. Four functional group active diluents were selected as additives to reduce the viscosity of epoxy resin, and the influence of diluent content on the comprehensive performance of epoxy resin was studied and analyzed.

Power equipment vibration visualization using intelligent sensing method based on event-sensing principle

Vibration measurements can be used to evaluate the operation status of power equipment and are widely applied in equipment quality inspection and fault identification. Event-sensing technology can sense the change in surface light intensity caused by object vibration and provide a visual description of vibration behavior. Based on the analysis of the principle underlying the transformation of vibration behavior into event flow data by an event sensor, this paper proposes an algorithm to reconstruct event flow data into a relationship correlating vibration displacement and time to extract the amplitude-frequency characteristics of the vibration signal. A vibration measurement test platform is constructed, and feasibility and effectiveness tests are performed for the vibration motor and other power equipment. The results show that event-sensing technology can effectively perceive the surface vibration behavior of power and provide a wide dynamic range. Furthermore, the vibration measurement and visualization algorithm for power equipment constructed using this technology offers high measurement accuracy and efficiency. The results of this study provide a new noncontact and visual method for locating vibrations and performing amplitude-frequency analysis on power equipment.

Metamaterial-based passive analog processor for wireless vibration sensing

Real-time, low-cost, and wireless mechanical vibration monitoring is necessary for industrial applications to track the operation status of equipment, environmental applications to proactively predict natural disasters, as well as day-to-day applications such as vital sign monitoring. Despite this urgent need, existing solutions, such as laser vibrometers, commercial Wi-Fi devices, and cameras, lack wide practical deployment due to their limited sensitivity and functionality. Here we proposed a fully passive, metamaterial-based vibration processing device, fabricated prototypes working at different frequencies ranging from 5 Hz to 285 Hz, and verified that the device can improve the sensitivity of wireless vibration measurement methods by more than ten times when attached to vibrating surfaces. Additionally, the device realizes an analog real-time vibration filtering/labeling effect, and the device also provides a platform for surface editing, which adds more functionalities to the current non-contact sensing systems. Finally, the working frequency of the device is widely adjustable over orders of magnitudes, broadening its applicability to different applications, such as structural health diagnosis, disaster warning, and vital signal monitoring.

Monitoring method for power equipment operation condition based on data warehouse

in the process of monitoring the operation state of power equipment, it is easy to be affected by the external environment, resulting in a large amount of data of the operation state of power equipment and the monitoring effect. A monitoring method of power equipment operation condition based on data warehouse is proposed. By analyzing the constraints of power equipment operation condition monitoring, a data warehouse is established; The transition probability and stability probability are determined by Markov chain model, and the characteristic values of operation state level variables are obtained by calculating the comprehensive correlation value and sensitivity; The entropy weight coefficient method calculates the weight of each performance index of the monitoring object, constructs the operation state monitoring model of power equipment, and realizes the monitoring of the operation state of power equipment. The experimental results show that the improved method can effectively improve the monitoring efficiency and shorten the monitoring time.

Gradient-based domain-augmented meta-learning single-domain generalization for fault diagnosis under variable operating conditions

Equipment operating conditions, referred to as domains, can induce domain drift in monitoring data, affecting data-driven fault diagnosis. Researchers have explored multi-domain generalization methods to tackle this issue. However, in actual industrial scenarios, the availability of fault data may be limited to a specific condition due to the cost or feasibility constraints associated with collecting extensive monitoring data. This limitation hampers the generalization ability of these methods, posing a major challenge for robust fault diagnosis under variable operating conditions. To address this challenge, we proposed a gradient-based domain-augmented meta-learning (GDM) single-domain generalization method. We analyze the restrictions of generating fake domains and construct a domain-augmented loss by evaluating diagnostic tasks minimization, semantic consistency, and distribution diversity for fake samples. Using a gradient-based technique, fake domains are generated iteratively, providing diverse fault knowledge for improved generalization. Instead of using time-consuming ensemble methods, we develop a novel meta-learning method to train a highly efficient and generalizable model, relaxing the requirement for auxiliary datasets in existing meta-learning methods. Two case studies consistently demonstrate the effectiveness and superiority of the proposed GDM method. Our findings suggest that this study offers a promising and competitive solution for single-domain generalization in fault diagnosis within real industrial scenarios.

Optimal power flow calculation method for regional integrated energy system based on double-layer optimization

In this paper, a two-layer optimization calculation method is proposed to improve the practical application efficiency of the integrated energy system and to improve the different problems of power flow calculation. Firstly, the power flow model is constructed and the optimization function is established based on indicators such as economy and environmental protection. The two-layer optimization method is proposed by combining the quadratic programming method and the particle swarm algorithm. Then, the obtained operating conditions of each energy equipment are used as the input of the power flow. The processing of each device and the energy flow of the power flow network are calculated and the effectiveness of the algorithm is verified by MATLAB power analysis.

The current state of the issue of corrosion protection of oil and gas facilities

In this paper, modern methods of protecting oil and gas equipment from corrosion are considered. The types of corrosion and their characteristics are listed. The features of the operating conditions of oilfield equipment are described. The damage caused by corrosion is shown. The methods of protection of equipment from corrosion, their features, advantages and disadvantages are given. used to minimize, control and monitor corrosion. The article presents an algorithm of actions to combat corrosion. Possible ways to solve the problem of corrosion by using online corrosion monitoring using interactive ultrasonic thickness sensors are presented.

Spatio-temporal information analytics based performance-driven industrial process monitoring framework with cloud–edge-device collaboration

Smart manufacturing in the context of Industry 4.0 presents new characteristics, such as large-scale, complex sub-processes cooperation, and integrated information management, which makes comprehensive performance-driven process monitoring difficult to be achieved in practice. Cloud–edge-device collaboration brings new perspectives for modern industries. This paper presents a performance-driven process monitoring framework in cloud–edge-device collaborative architecture, including a spatio-temporal information analytics based method and its application to a hot strip mill process (HSMP). Firstly, an integrated performance-driven process monitoring framework is proposed, where different monitoring tasks are deployed in the device, edge, and cloud layers, respectively. Secondly, a new performance-related spatio-temporal information analytics based fault detection and localization method is developed. In detail, temporal kernel canonical correlation analysis (CCA) is employed to extract the canonical correlation features with nonlinearities in the temporal domains, and spatial CCA is designed to form the spatial features. Based on the spatio-temporal features, the equipment operation condition monitoring model is developed in the device layer for real-time fault localization, and the process monitoring model is deployed in the edge for fault detection. Furthermore, to give a high-level evaluation of the operating status of the process, a qualitative trend analysis based operation risks evaluation model is deployed in the cloud. Different from the traditional architectures, the data processing capability of each layer is fully utilized in the proposed one, and the function is relevant to the enterprise management system. It provides a topological basis with clear priorities for subsequent process regulation and optimization. Finally, an actual HSMP is introduced to testify the performance of the framework. From the results, the fault detection rates are improved in comparison with traditional methods, and the fault localization results are consistent with the actual situations.

Determination of the heat transfer coefficient between pellets and air during the modernization of a pelletizing tower based on industrial research

The object of research is the heat transfer coefficient between pellets and air flow in industrial granulation towers. The problem lies in the difficulty of optimizing the heat transfer process. As a result of experiments and analysis, it was shown that each granulating tower for the production of mineral fertilizers has unique properties that significantly affect the efficiency of heat transfer. There are general mathematical models, but for accurate modeling and optimization of heat transfer, the unique characteristics of each production must be taken into account. The results showed that creating an accurate mathematical model for each specific fertilizer production is a difficult task due to the large number of unmeasured or difficult to reconcile factors. The method of calculating the heat transfer coefficient for the granulator tower obtained in the course of the work derives from a set of researches on the production of mineral fertilizers. This approach is based on the analysis of technical parameters and granulation composition of the product. The developed method makes it possible to reliably ensure the operating conditions of the device during modernization and changes in production volumes. These results are important for both practical and theoretical purposes. They can be used to accurately predict the operating conditions of equipment during modification and productivity growth. According to the research results, this approach allows obtaining fairly reliable data for forecasting the thermodynamic conditions of the tower equipment in the event of its modernization and transition of the granulation tower to the production of an increased amount of products. The specified method was tested in calculations of production modernization at urea production plants (Indian Farmers Fertilizer Cooperative (Iffco), India), Rustavi Azot LLC (Georgia), Grodno Azot LLC (Republic of Belarus) and others. This method has proven to be quite reliable in predicting the possible need for an additional amount of air supplied to the tower and forming requirements for the operating parameters of rotary vibrating granulators in the event of a significant increase in the load on the float in the tower and an increase in the amount of products planned for release.

INFLUENCE OF PROCESSING MODES ON ACCURACYAND SHRINKAGE OF THREADED PLASTIC PARTS OF OILFIELD EQUIPMENT

The study of the operating conditions of oil equipment made it possible to identify a set of indicators that most fully characterize the quality of plastic parts. It is known that the quality of parts of products made of various materials is a combination of mechanical, physical and operational indicators of any equipment. It is partially formed during their manufacturing process, depending on their materials and manufacturing method. And plastic parts are very different from metals depending on the area of application. This is due to the technologies for manufacturing structural products and individual parts from them. When manufacturing technologically complex parts from polymer materials, it is possible to achieve the required quality by controlling production modes depending on the grade of materials and the design of the parts being manufactured. Production of a thread of plastic details is rather difficult, than on metals. It is connected with the physical-mechanical processes coming in the course of production of concrete thread details from plastic by method of pressing and casting under pressure. Also mechanics of formation of plastic threads on details of the oil-field equipment on the specific properties differ from the threads received by a machining method on metals. As thread surfaces on plastic details are formed in compression molds depending on materials and the modes: temperatures, pressure and time of production. These regime parameters and guided parameters play a part at a guaranty of physical- mechanical properties of details in the course of production. In this regard, it in this article influences of technological parameters of production on quality of thread details oil-field equipment are considered. At the same time, in a wide range of operating parameters, the achievable boundaries of quality indicators for parts operating in the designs of exploration, drilling and operation equipment in the oil and gas industry are investigated. Mathematical relationships are established between the quality indicators (shrinkage and strength) of parts on the pressing temperature. Keywords: thread plastic details, accuracy, shrinkage, pressing pressure.

RESEARCH OF WEAR RESISTANCE AND MECHANISM OF ABRASIVE WEAR OF GAS THERMAL METALLIZED COATINGS USED FOR SEM PROTECTION

Complicated operating conditions of oilfield equipment during oil production using ESPs require the use of additional measures to protect its main components from the effects of negative factors. One of the main and significant negative factors influencing the corrosive aggressiveness of the environment is the water cut of the produced oil, which contains a large amount of dissolved gases, such as carbon dioxide and hydrogen sulfide, which are characterized by high corrosive aggressiveness and are the main risk factors leading to corrosion of the oil field. equipment and their premature failure. The housings of submersible electric motors (SEM) are the most susceptible to corrosion of the ESP unit. According to statistics, about 70% of failures occur in the motor housing. Of the variety of protection methods, the application of gas-thermal metallization coatings is the most widely used for protecting motor housings. Despite the effectiveness of this method of protection, it does not exclude the occurrence of corrosion processes in the metal of the motor housing and possible damage and destruction. This article is devoted to studies of the corrosion behavior of various types of metallization coatings used to protect SEDs under the influence of one of the main aggressive components — carbon dioxide dissolved in the mineralized aqueous phase, by conducting autoclave tests that simulate in laboratory conditions the operation of the material, in particular its corrosion resistance when exposed to a corrosive environment.Conducting research in this area is an urgent task aimed at solving the problems of reducing the service life of oilfield equipment and their premature failure, in particular motor drive housings during operation in difficult conditions.

Digital Twin-Based Vibration Monitoring of Plant Factory Transplanting Machine

In response to the problem of bowl seedling detachment caused by the shaking of the transplanting machine in plant factories, this paper proposes a physical entity monitoring method for the digital twin (DT) plant factory transplanting system. The method is used to analyze the vibration signals of the transplanting machine under different operating conditions and explore the optimal working conditions. Firstly, a demand analysis for the physical entity of the DT plant factory transplanting system is conducted, focusing on practical applications. Then, an optimal deployment plan is designed based on the axiomatic design (AD) theory. Subsequently, a comparative analysis of the operating conditions of the plant factory transplanting equipment is carried out using data-driven approaches. Finally, the optimal working condition parameters are determined by comparing the modal vibration power spectral density of the transplanting equipment under different operating conditions. The results show that the maximum amplitude occurs in the Z-axis, with a magnitude of 2.1 m/s2. By comparing the cloud maps, it is evident that the vibration trends in the Z-axis and X-axis above the transplanting robotic arm are more pronounced compared to the Y-axis. This indicates that under the operating condition of transplanting 3000 plants per hour, a high transplanting efficiency can be maintained, and the vibration signals in the XYZ-axis above the transplanting robotic arm are relatively smooth, making them suitable for transplanting operations. This study combines digital twin technology to analyze the vibration signals of the plant factory transplanting machine under different operating conditions and explore the optimal working conditions. Compared to traditional monitoring platforms, this method facilitates the real-time visualization of different operating conditions of the transplanting machine in a virtual mapping, providing a more intuitive reflection of the equipment operation status.

Operation and maintenance strategy of radio transmission equipment based on big data

AbstractIn recent years, the operation and maintenance of radio transmission equipment have faced challenges such as equipment aging, high maintenance costs, long response time, and technological updates. It is necessary to adapt to new technologies and improve maintenance efficiency. The operation and maintenance strategy of radio transmitting equipment based on big data (BD) aims to optimize the reliability and operating efficiency of equipment through data analysis and intelligent algorithms, and reduce failure risk and maintenance costs. By analyzing the operation data and maintenance records of a large number of radio transmission equipment, it revealed the patterns and trends of equipment failures, in order to timely identify and predict possible faults, and take corresponding maintenance measures. This article adopted methods such as data collection and preprocessing, fault prediction and diagnosis, and maintenance optimization to achieve accurate monitoring of equipment operation status, fault prediction and diagnosis, optimized maintenance, and decision support. This can improve the operational efficiency and reliability of radio transmission equipment, which is used for fault diagnosis of radio transmission equipment through SVM (Support Vector Machine) algorithms. By training SVM classifiers, this article associated different types of faults with their feature data, thereby quickly and accurately determining the type of equipment fault based on real‐time collected device data, providing a basis for maintenance decision‐making. The experimental results show that the failure frequency of the radio transmitting equipment in the control group was between 7% and 11%, while the failure frequency of the radio transmitting equipment in the experimental group was between 3% and 7%. Experiments show that BD technology can effectively reduce the failure frequency of radio transmitting equipment. The empirical results indicate that this strategy can improve the reliability of equipment and reduce operation and maintenance costs, which has important guiding significance for the operation and maintenance management of radio equipment. This would bring about more efficient and reliable operation and maintenance management of radio equipment, making important contributions to the smooth progress of broadcasting business and the improvement of user experience.

Application of deep learning modelling of the optimal operation conditions of auxiliary equipment of combined cycle gas turbine power station

Reasonable condition adjustment of the auxiliary machinery can optimize the operation performance of the Combined Cycle Gas Turbine (CCGT) power station. In this paper an optimization method for the operation control of the auxiliary equipment based on deep learning is proposed and applied to an F-class gas turbine generation unit. A database of the optimal operating conditions is established to achieve the best economic benefit based on the analysis of two years of historical operation data. The control optimization models are built using the machine learning algorithms including the Multi-Layer Perceptron (MLP), Supporting Vector Machine (SVM), Gaussian process regression and linear regression methods and their accuracy has been compared. The input parameters of these models consist of the load rate, the heating output and the ambient temperature, with the output parameters including the electrical currents for the mechanical draft cooling tower, the high pressure feed pump, the medium pressure feed pump and the condensate pump. The MLP model is designed with different network hidden layer structures and provides the highest calculation accuracy with the least average error of 1.82 %. Different training data sizes are compared and the optimal control trajectory is analyzed. The result can provide useful references for the optimization of auxiliary equipment operations.

Analysis and Research on Comprehensive Evaluation Method of Operation Performance of Hospital Valuable Equipment

To study the effective method of comprehensive evaluation and analysis of hospital valuable medical equipment performance. The operation performance of 6 valuable equipment was evaluated by cost-benefit method, comprehensive index method and public evaluation method. Utilize equipment information management methods for data collection and evaluation, and construct an assessment data model based on evaluation indicators from three aspects: equipment operation status, profitability status, and scientific research contribution. Through the performance analysis of different types of valuable medical equipment, a more real and comprehensive quantitative analysis is carried out, which plays a key role in the reasonable purchase, efficient operation and avoiding idling.

Екологічні аспекти переробки ливарного алюмінієвого шлаку

Data on the influence of the aluminum slag processing process on the state of the atmospheric environment are presented. Calculations of the dispersion of pollutants (PO) in the surface layer of atmospheric air were carried out, which showed the absence of exceeding the maximum permissible concentrations (MPC) for carbon monoxide, nitrogen dioxin, sulfur dioxide, hydrogen chloride and dust. Analysis of emissions was carried out for extreme conditions of equipment operation. It was established that under such technological conditions, the maximum concentration of pollutants is: NO2 ≤ values of 0.2 MPC, and the rest of the pollutants do not exceed - 0.05 MPC. To identify the actual concentrations of hazardous substances at the border of the sanitary protection zone (SZZ), laboratory tests were performed at a distance of 300 m from the emission source. It was established that emissions of pollutants into the atmosphere from the industrial site on the territory of the enterprise and on the border of the calculated sanitary protection zone are less than the MPC of the atmospheric air of populated areas and will not have a significant harmful effect on the environment, provided that the sanitary and technological requirements for operation are met. The level of the content of harmful impurities in the air does not exceed the hygienic standards outside the sanitary protection zone, which are established for the atmospheric air of populated areas. Keywords: ecology, pollutants, maximum permissible concentration, aluminum slag.