Operating Conditions Of Equipment Research Articles

Power transformer fault diagnosis system based on Internet of Things

Transformer is the most important equipment in the power system. The research and development of fault diagnosis technology for Internet of Things equipment can effectively detect the operation status of equipment and eliminate hidden faults in time, which is conducive to reducing the incidence of accidents and improving people's life safety index.ObjectiveTo explore the utility of Internet of Things in power transformer fault diagnosis system.MethodsA total of 30 groups of transformer fault samples were selected, and 10 groups were randomly selected for network training, and the rest samples were used for testing. The matter-element extension mathematical model of power transformer fault diagnosis was established, and the correlation function was improved according to the characteristics of three ratio method. Each group of power transformer was diagnosed for four months continuously, and the monitoring data and diagnosis were recorded and analyzed result. GPRS communication network is used to complete the communication between data acquisition terminal and monitoring terminal. According to the parameters of the database, the working state of the equipment is set, and various sensors are controlled by the instrument driver module to complete the diagnosis of transformer fault system.ResultsThe detection success rate of the power transformer fault diagnosis system model established in this paper is as high as 95.6%, the training error is less than 0.0001, and it can correctly identify the fault types of the non training samples. It can be seen that the technical support of the Internet of Things is helpful to the upgrading and maintenance of the power transformer fault diagnosis system.

Intelligent network operation and maintenance system based on big data

With the deepening of network system construction, it is more and more important to monitor the operation status and performance of business system, network system, network equipment, host equipment, database and middleware; at the same time, the requirements for the depth, breadth and granularity of network information security monitoring are constantly improved, and the traditional network operation and maintenance is more and more inadequate. The “intelligent network operation and maintenance system based on big data” can realize the operation monitoring of network, host, business application, network equipment, desktop terminal, etc., and build an electronic platform based on big data to realize the standard operation and maintenance process, achieve the goal of real-time monitoring and closed-loop control.

Raman Spectra of SF6Decomposed Characteristic Products Based on Density Functional Theory

SF6decomposition product analysis is one of the most convenient and efficient methods to diagnose the potential faults of SF6insulated electric equipment in the early stage. Based on SF6decomposition characteristic gas analysis, the operation state of SF6insulated power equipment can be judged by on-line monitoring, so as to ensure its normal operation. Raman spectrum analysis technology can realize the nondestructive detection of gas samples with a single wavelength laser. It has excellent applicability and high efficiency for the detection of SF6decomposition characteristic components. In this paper, molecular configurations of CF4, CO, H2S, SO2were obtained by B3LYP functional that based on density functional theory (DFT), and the Raman frequency and intensity characteristics were calculated by 6-31G (2df, p) basis group. The results were compared with National Institute of Standards and Technology (NIST) standard frequencies and it was found that the optimized configuration has no virtual frequency. The characteristic peaks of CO, CF4, SO2and H2S that were identified by Raman spectrum are respectively 2221.11, 908.97, 1175.24 and 2688.82 cm-1, which are basically consistent with the corresponding NIST standard values. This study not only shows that the Raman spectrum of SF6decomposition products calculated by B3LYP functional function is reliable, but provides a reference for the quantitative detection of SF6decomposition products based on Raman spectrum.

Judicial engineering and mechanical examination: practiceological approach

The purpose of the article is to study and develop theoretical and methodological principles of forensic engineering and mechanical expertise, recommendations with the use of special knowledge in criminal and civil proceedings to prevent, detect, investigate and consider crimes related to the operation of machines, mechanisms, equipment and improvement of its methodology, substantiation and formulation of recommendations on legal assessment of results and increase of efficiency of use of conclusions of forensic examination in the course of proving. The methodological basis of the study are the laws and categories of the theory of cognition, in particular the provisions of materialist dialectics, which contributed to the realization of the purpose and tasks, subject, object, research issues in the context of interdependence of achievements and needs of practice. At the same time, the methods of formal logic (analysis, synthesis, deduction, induction, analogy, abstraction) made it possible to understand in more detail the content of the studied questions, the essence of the studied categories and phenomena was clarified by the system-structural method. The Ministry of Justice of Ukraine has created a new type (subtype) of forensic examination: “Mechanical Engineering” and forensic specialty 10.25 “Study of the technical condition and operating conditions of machines and mechanisms”, so the article formulates the basic concepts of forensic mechanical examination, namely : the basic questions which are solved by judicial engineering and mechanical examination are defined; tasks, subject, object, questions that give the opportunity to understand the essence and content of the expert activity of these studies; the tasks of a specific examination are distinguished from the tasks of other related types of examinations in this area. Complex problems facing the investigation / court determine the expediency of dealing with a wide range of topical issues on a scientific basis, taking into account the peculiarities of the study of technical condition and operating conditions of machines, mechanisms and equipment and create a new type of forensic engineering – mechanical engineering. In order to increase the efficiency and objectivity of investigations of crimes in the operation of machines, mechanisms and equipment, courts / investigators should, as necessary, conduct forensic examinations by engineers: technologists, mechanics, power engineers, metallurgists, etc., for which it is necessary to develop approach, scientific-theoretical and practical principles of forensic engineering and mechanical expertise.

New insights about the electrochemical production of ozone

Ozone is a rather attractive oxidant, it is very efficient in the oxidation of pollutants and in the killing of pathogens and does not generate any hazardous waste during its use. Its generation has been constantly sought in an effective way, focusing on obtaining high concentrations of ozone at the lowest possible cost. Recently, electrochemical production of ozone show advantages over conventional corona discharge generation, since this technology do not need very high voltages, feeding oxygen or pure air or dissolving the ozone into wastewater to be treated. However, it is still at early development stage and there is still a long way to reach the high technology readiness levels needed to complete its value chain. Equipment considerations and operation conditions are the key points that need to be understood in order to increase efficiently. Recent novelties in the state of the art of research are summarized in this work.

Photocatalytic inactivation of dairy bacteriophages contained in aerosol: Comparative evaluation of efficiencies

The aim of this work was to investigate the photocatalytic inactivation efficiencies of eight phages infective to lactic acid bacteria (LAB) contained in bioaerosols. Studies were performed in a semi-pilot scale equipment, which consists of a stainless steel camera, in which air containing the phage particles circulates, and an acrylic reactor with twenty UV-A lamps and six borosilicate plates covered with the catalytic film (TiO2). Phages (ALQ13.2, ATCC 15807-B1, ATCC 8014-B1, BYM, CHD, LDG, Ln-9 and MLC-A) were nebulized into the camera using the 6-jet Collison nebulizer, being assays performed for a total time of 100 min. Sampling by impact of air on a glass slide was carried out. Some conditions of equipment operation (phage suspension concentration, nebulization method, influence of relative humidity) using phage ATCC 8014-B1 were previously adjusted. Phages ALQ13.2, ATCC 15807-B1, BYM and CHD were not detected in the air because of loss of their infectivity after nebulization when assays in absence of catalyst and UV radiation were conducted. On the other hand, it was possible to achieve a complete inactivation (< 10 PFU mL−1) of phages ATCC 8014-B1, LDG and Ln-9 within 100 min of photocatalytic treatments. On the other, phage MLC-A was partially inactivated under the same conditions. These results are in agreement to those obtained when photonic and quantum inactivation efficiencies were calculated. In this sense, efficiencies varying between 1.27–2.64 × 10−17 PFU photon−1 and 4.42–9.18 × 10−17 PFU photon−1 were obtained, respectively, being the lowest efficiencies for phage MLC-A. This phage with the highest resistance could be studied in the future to evaluate diverse working conditions in order to optimize the operation and efficiency of the semi-pilot scale equipment.

Analysis of particle dynamics in a pin mill by Discrete Element Method

Milling is an important process for tailoring the particle size distribution for enhanced attributes, such as dissolution, content uniformity, tableting, etc., especially for active pharmaceutical ingredients and excipients in pharmaceutical industries. Milling performance of particulate solids depends on the equipment operating conditions (geometry, process conditions and input energy etc.) as well as material properties (particle size, shape, and mechanical properties, such as Young’s modulus, hardness and fracture toughness). In this paper the particle dynamics in a pin mill is analysed using Discrete Element Method (DEM), combined with a novel approach for assessing particle breakability by single particle impact testing. A sensitivity analysis is carried out addressing the effect of the milling conditions (rotational speed and feed particle flow rate), accounting for feed mechanical properties on the breakage behaviour of the particles. Particle collision energy spectra are calculated and shown to have a distribution with the upper tail end being close to the maximum energy associated with the collision with the rings. Breakage is primarily due to collisions with the rings, except for large particles that are comparable in size with the gap between the rings, nipping is also a contributory breakage mechanism.

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.

Performance of Protective Coatings for Aluminum Alloys in the Operating Conditions of Oil Production Equipment

The paper investigates wear resistance and corrosion resistance of protective coatings of D16 aluminum alloy under conditions that simulate operation of drill pipes. The paper also presents microstructure of coatings, electrochemical potential and corrosion rate of D16 aluminum alloy with various coatings. We evaluated adhesion and wear resistance of these coatings. D16 alloy with a tungsten carbide coating has the widest range of service properties and can be used to effectively protect the surface of aluminum drill pipes during operation operation.

The most common violations of regulatory indicators of the quality of electric energy in electric networks

Work on ensuring the quality of electric energy in accordance with the requirements of GOST 32144-2013 is currently relevant and requires prompt and high-quality solutions. The article lists the main consumers of electric energy that negatively affect its quality, provides the results of measurements of electric energy quality indicators and the most common violations of regulatory requirements, analyzes the influence of voltage asymmetry on the operating conditions of electrical equipment. The basic formulas for calculating the asymmetry coefficients and ways to reduce the voltage asymmetry are proposed.

Steel quality of rolling contact bearings produced by different manufacturers, used for mining equipment instrumentation

Severe operating conditions of mining equipment have a negative impact on the operating life of the rolling contact bearings used in such equipment. The main reasons for the premature failure of rolling contact bearings include, first of all, poor quality of the bearing steel as delivered and the defects of their subsequent heat treatment. The abovementioned reasons result in splitting of bearings, metal shelling and appearance of potholes on their tracks, as well as intense abrasive wear. Nevertheless, despite the importance of this issue, the quality of bearing steel, either as delivered or in finished bearings, is usually not under control.To solve this problem, the quality of bearing steel was studied, exemplified by the bearings of the Russian, Swedish (SKF) and Japanese (NSK) manufacturers, i.e., main suppliers of rolling contact bearings for mining equipment in Russia. The problem of bearing steel quality control was solved by developing a quality control methodology that took into account the state of the metal after metallurgical treatment and at the stage of bearing production. Based on this methodology, the main types of bearing steel defects affecting the mechanism of bearing failure are identified.It is found that the bearing steel quality of domestic manufacturers is highly competitive with foreign counterparts. A method of nondestructive control is also proposed for detecting possible microstructure defects in bearings, either as delivered or while in operation.

A modular testbed for mechanized spreading of powder layers for additive manufacturing.

Powder bed additive manufacturing (AM) processes, including binder jetting (BJAM) and powder bed fusion (PBF), can manufacture complex three-dimensional components from a variety of materials. A fundamental understanding of the spreading of thin powder layers is essential to develop robust process parameters for powder bed AM and to assess the influence of powder feedstock characteristics on the subsequent process outcomes. Toward meeting these needs, this work presents the design, fabrication, and qualification of a testbed for modular, mechanized, multi-layer powder spreading. The testbed is designed to replicate the operating conditions of commercial AM equipment, yet features full control over motion parameters including the translation and rotation of a roller spreading tool and precision motion of a feed piston and the build platform. The powder spreading mechanism is interchangeable and therefore can be customized, including the capability for dispensing of fine, cohesive powders using a vibrating hopper. Validation of the resolution and accuracy of the machine and its subsystems, as well as the spreading of exemplary layers from a range of powder sizes typical of BJAM and PBF processes, are described. The precision engineered testbed can therefore enable the optimization of powder spreading parameters for AM and correlation to build process parameters in future work, as well as exploration of spreading of specialized powders for AM and other techniques.

Weighted K-NN Classification Method of Bearings Fault Diagnosis With Multi-Dimensional Sensitive Features

Research on the intelligent fault diagnosis method of rolling bearing based on laboratory data has made some achievements. However, due to the change of working conditions and the lack of historical data of the same equipment in the actual diagnosis, some methods mostly have problems such as poor generalization. Model training and verification data are insufficient, and engineering practice still lacks effective intelligent fault diagnosis methods. In this paper, we propose a weighted k-nearest neighbor (WKNN) fault diagnosis model based on multi-dimensional sensitive features, and propose a fault diagnosis method for rolling bearings that adapts to different equipment and different operating conditions. First, we extract time domain, frequency domain, and entropy features of the original signal to form the raw signal feature set. Then, the iterative ReliefF feature screening method is used to evaluate the joint feature set, calculate the weight of each feature, remove insensitive and redundant features, and obtain a high-dimensional sensitive feature set. Finally, the WKNN classification model is used to identify bearing failure modes. The fault diagnosis model was trained using rolling bearing data from the Case Western Reserve University (CWRU), while laboratory data from the Intelligent Maintenance System (IMS), the Society of Mechanical Failure Prevention Technology (MFPT) and the engineering case data were used for testing. The results show that the model proposed in this paper has high fault diagnosis accuracy and can accurately determine the fault type after early warning. Compared with other comparison methods, the fault recognition accuracy rate is higher. And it is suitable for different working conditions and different equipment, and has good engineering application value.

Predicting anomaly conditions of energy equipment using neural networks

In modern conditions for complex thermal power facilities, the issue of developing methods for predicting equipment failures is especially relevant. Methods based on the intellectualization of diagnostic systems and allowing to obtain predictive models based on the use of both current data received in real time from measuring equipment and retrospective information are considered promising. Intellectualization of the system in terms of the ability to learn allows to quickly adjust the parameters of forecasting models under changing conditions of equipment operation, to determine new deadlines for scheduled repairs and minimize equipment downtime. A limitation of the use of methods is the incompleteness of failure statistics, ie when equipment failures are rare or non-existent. Such diagnostics of energy equipment, especially thermal power facilities, contributes to a more environmentally friendly production.

Метод управления требованиями к эксплуатационным свойствам горюче-смазочных материалов

The cost-effective method of fuels and lubricants performance properties requirements management is proposed. Method ensures effectiveness enhancement of equipment and of fuels and lubricants life cycles; it includes requirements establishment (constructing of conceptual and empirical models of requirements), their quantitative expression through highly informative indicators of fuels and lubricants propensity for transformations, then approbation with the use of laboratory, bench and test stands tests, specification, assessment of realization in real world equipment operational conditions, effectiveness evaluation and modification of requirements. Some illustrations of application of the new method phases with regards to motor fuels and hydraulic fluid are provided.

Software design of rotating machinery fault diagnosis system based on deep learning

With the development of Industry 4.0, in order to meet the needs of intelligent fault diagnosis of rotating machinery in the industrial field, this paper developed a fault diagnosis system for rotating machinery based on deep learning and wavelet transform methods. The system is based on the Python language and mainly combines the PyQt graphical interface framework and the TensorFlow machine learning framework to complete the training requirements for historical or online fault data, and perform online monitoring and diagnosis of equipment operating conditions. The diagnostic accuracy of the system test results is more than 95%, the software interface is friendly, the algorithm generalization ability is good, and the reliability is strong. It provides guidance for the diagnosis of rotating machinery.

Microencapsulation of propolis by spray drying: A review

Propolis, which is a bee glue is potentially a useful food additive since it contains antioxidant and preservative-like properties. However, its application in the food industry remains limited, due to its strong taste and low solubility. Microencapsulation is a technique that can improve its characteristics and the most widely used is spray drying. To obtain a spray-dried product with desired properties, optimization of both the encapsulating materials and equipment operating conditions are crucial. In this review, the use of different wall materials and operational conditions for spray drying of propolis are discussed, in order to guide selection of these parameters to obtain optimal spray dried microcapsules. In addition, comparison with other microencapsulation techniques is also discussed and pertinent industrial applications of spray-dried propolis given.

Field-synchronized Digital Twin framework for production scheduling with uncertainty

Research on scheduling problems is an evergreen challenge for industrial engineers. The growth of digital technologies opens the possibility to collect and analyze great amount of field data in real-time, representing a precious opportunity for an improved scheduling activity. Thus, scheduling under uncertain scenarios may benefit from the possibility to grasp the current operating conditions of the industrial equipment in real-time and take them into account when elaborating the best production schedules. To this end, the article proposes a proof-of-concept of a simheuristics framework for robust scheduling applied to a Flow Shop Scheduling Problem. The framework is composed of genetic algorithms for schedule optimization and discrete event simulation and is synchronized with the field through a Digital Twin (DT) that employs an Equipment Prognostics and Health Management (EPHM) module. The contribution of the EPHM module inside the DT-based framework is the real time computation of the failure probability of the equipment, with data-driven statistical models that take sensor data from the field as input. The viability of the framework is demonstrated in a flow shop application in a laboratory environment.

A multi-fault diagnosis method of gear-box running on edge equipment

Edge computing equipment is a new tool that has been widely used to monitor the operation state of industrial equipment and to diagnose and analyze faults. Therefore, the fault diagnosis algorithm used in the edge computing device plays an especially significant role in fault diagnosis. The application of deep learning method in mechanical fault diagnosis has been gradually popularized, because it has many advantages, such as strong classification ability and accurate feature extraction ability. However, many of the completed papers and models are based on single label system and are used to diagnose single target fault. The validation set is not rigorous enough, and it is difficult to accurately simulate the faults that may occur in the actual production process. Nowadays, in the era of big data, the single label system ignores the joint relationship of different fault types, and it is difficult to make a correct judgment for the location, type and degree of mechanical failure. Hence, in the process of experiment, we used the bearing data of Case Western Reserve University(CWRU) to ensure the wide range and large quantity of data sets. A fault diagnosis method of gear and bearing in the gear-box based on multi-task deep learning model is put forward. In this method, gear and bearing faults can be diagnosed simultaneously. Through a separate task layer, this method can adaptively extract the characteristics of distinct targets from the same signal, and add a Batch Normalization layer(BN) to accelerate the convergence speed of the network. Through experiments, we conclude that it is an effective method which can judge the fault situation of gear and bearing accurately in a variety of working conditions.

Experimental study on simulation test system and electrostatic monitoring of rub fault of air circuit of engine based on the IoT perception

Aeroengine gas-path fault is a critical engine fault, which can seriously affect the normal operation of the engine and threaten flight safety. Due to aeroengine working characteristics, friction faults probably occur between the rotor and stator during aeroengine operation. Therefore, it is crucial to accurately and quickly judge the friction fault for the entire engine fault. According to aeroengine friction fault mechanism, a friction fault monitoring technology was proposed based on the electrostatic induction principle to solve the problem that the friction fault of aeroengine blades is difficult to monitor online. The Internet of things (IoT) technology was employed to acquire data from equipment and state information, and a friction fault test system was developed to simulate electrostatic monitoring of gas-path high-speed airflow and multi-impact loads. Then, a test bench was designed to conduct electrostatic monitoring experiments of multi-group friction under different rubbing modes, different materials and different rubbing degrees. Application of the IoT technology can fulfill real-time monitoring of the operation state of equipment and reveal the defects of large equipment, promoting the merge of IoT with the manufacturing industry and pushing IoT development towards a more transparent and intelligent future.