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The typical power transformer diagnosis approach is imprecise and unstable. A support vector machine classification algorithm is proposed, by designing an algorithm program that can improve the accuracy and speed of energy transformer diagnosis, the vibration signals of the surface twisting in different states are extracted by wavelet packet energy spectrum signal processing method, it is verified that the curve similarity between the vibration simulation model and the measured data is greater than 0.98, proving the simulation model’s validity. The calculation technique of online short circuit inductance is developed from the equivalent transformer model, and the variation error of simulation results is less than 0.05% when compared to the real transformer characteristics. The suggested state diagnostic technique successfully compensates for the drawbacks of the reactance method, which is incapable of detecting and judging the slightly loose or faulty winding. The method’s accuracy and superiority, as well as the practicability of the state diagnosis system, are demonstrated.

In order to accurately and easily calculate the settlement of the foundation, a high-speed roadbed based on a nonlinear algorithm is proposed to broaden the uneven and uniform reduction numerical analysis. The basic loading test results of the foundation loading test results in a series of sand-bearing test results made by gear suppressive elastic Mohr–COMB were verified using general finite element procedure ABAQUS. The results show that for most original slopes of 1:1.5 highways, according to the above research results and relevant specifications, the high-wig ratio of the steps should be controlled in 1:1.2 to 1:1.5. That is, when the width is controlled in a range of about 1.2 m, the height of the step is held at 0.8–1.0 m. The larger the role of filling the top surface, the more obvious is the role of the geogrid to reduce the settlement of the road embankment. Under the conditions of the same subgraduation, the reinforced road embankment of the geotechnical grille, the settling effect that occurs in the load activity is also more obvious. This method offers a better design and construction technique for expanding the project, investigates the control of differential settlements caused by excavation step size and geogrid size, and seeks more complete, scientific programmes. It has the potential to drastically reduce the project’s investigation effort, shorten the survey cycle, and provide incalculable economic and social benefits. Therefore, the research results of this paper have improved the reliability of existing ground settlement analysis.

In recent years, the network has become more complex, and the attacker’s ability to attack is gradually increasing. How to properly understand the network security situation and improve network security has become a very important issue. In order to study the method of extracting information about the security situation of the network based on cloud computing, we recommend the technology of knowledge of the network security situation based on the data extraction technology. It converts each received cyber security event into a standard format that can be defined as multiple brochures, creating a general framework for the cyber security situation. According to the large nature of network security situation data, the Hadoop platform is used to extract aggregation rules, and perform model extraction, pattern analysis, and learning on a network security event dataset to complete network security situation rule mining, and establish a framework for assessing the state of network security. According to the results of the federal rule extraction, the level of network node security risk is obtained in combination with signal reliability, signal severity, resource impact, node protection level, and signal recovery factor. A simulation test is performed to obtain the intrusion index according to the source address of the network security alarm. Through the relevant experiments and analysis of the results, the attack characteristics obtained in this study were obtained after manually reducing the network security event in the 295 h window. The results show that after the security event is canceled, the corresponding window attack index decreases to 0, indicating that this method can effectively implement a network security situation awareness. The proposed technique allows you to accurately sense changes in network security conditions.

When a car is operating, the road roughness will cause it to vibrate. When this vibration reaches a particular threshold, the driver will get uncomfortable and fatigued, affecting the bearing system’s durability and longevity. The vehicle vibration virtual test system’s framework design and implementation approach is based on virtual reality (VR), and it is made up of a VR subsystem, a model subsystem, and a virtual instrument subsystem. A virtual car vibration test system based on VR is built, and the results of the virtual vehicle vibration test are reported. The findings indicate that the virtual test (VT) technology theoretical model pertains to the creation of VT systems for car vibration based on VR. Users may monitor the vehicle vibration as well as its time domain and frequency domain signals in three separate observation modes using the system.

The author in order to solve the problem of optimizing the accuracy of mechanical equipment failure detection proposes a vibration signal collection and computer simulation detection for mechanical equipment failure. Using wavelet domain Wiener filtering-based mechanical equipment fault detection method, the author first combined Wiener filtering and wavelet threshold filtering, established a vibration signal collection model for mechanical equipment, obtained the true signal and the filtered signal error and fusion of the principle of wavelet threshold filtering to perform orthogonal wavelet transform on noisy signals, and through the principle of fast independent component analysis to separate the vibration signals of mechanical equipment, build the initial separation matrix of the signal with unit variance, and found the estimated value of the source signal; the collection of vibration signals of mechanical equipment is completed according to the estimated value, realizing the optimization of the detection of mechanical faults and equipment failures. The simulation results prove that the signal-to-noise ratio of the vibration signal of mechanical equipment collected by this method is higher than 15.7% of the signal-to-noise ratio based on the FastICA method; this is mainly because when this method is used for anti-interference collection of vibration signals of mechanical equipment, combining the principle of fast independent component analysis to separate the vibration signals of mechanical equipment, construct the initial separation matrix of the signal with unit variance. Using the proposed method for signal acquisition can greatly reduce the error, and it can provide effective support for fault detection of mechanical equipment.

In this study, the electrical characteristics of pure ZnO nanowires are investigated using density functional theory (DFT) inside the nonequilibrium Green's function (NEGF) paradigm. We clarify the underlying electronic behaviours by a thorough analysis of I-V curves, density of states (DOS) spectra, and transmission spectra. All calculations are carefully carried out using the Quantum Wise Atomistic Tool Kit programme and the Generalised Gradient Approximation (GGA). Our results show that localised d orbitals dominantly contribute to ZnO nanowire transmission properties. We also investigate the effect of doping with copper on these characteristics. Gradual doping from pristine to 2 and 4 Cu atoms shows significant enhancements, indicating a clear relationship between electrical properties and doping concentration. Notably, the device's potential as a resistor is highlighted by the greater linearity of current with copper doping, demonstrating adherence to Ohm's law. Moving beyond basic research, we tackle real-world issues in sensing applications, including the quick identification of copper molecules in water. We provide light on the Au-ZnO-Au structure's potential for sensor applications by thoroughly discussing its electron transport properties. In conclusion, this study advances our knowledge of the electrical characteristics of ZnO nanowires and how doping modifies them, opening the door to their application in a variety of electronic and sensing devices. [ABSTRACT FROM AUTHOR]

To solve the problems of large mechanical powertrain such as complex structure, serious accident, strong nonlinear characteristics of running state, bad operating environment, non-Gaussian noise, and various uncertain factors, it is difficult to make an accurate fault diagnosis. This paper proposes a method for dealing with nonlinear characteristics using nuclear waves, as well as a system, deeply conducted nuclear base fault feature extraction, classification, and decision making, such as nuclear base state trend prediction technology research, focusing on exploring and improving the accuracy of fault diagnosis under nonlinear conditions, technical method, and way to state prediction accuracy. It offers effective technical assistance for the advancement and use of mechanical power train monitoring and diagnosis technology. A fault detection method based on kernel method is proposed. Based on the characteristics of this method in dealing with nonlinear problems, the research on kernel feature extraction, kernel fault classification and decision making, and kernel state trend prediction are carried out systematically. The experimental results show that the simulation analysis of typical chaotic time series prediction and the application of the operation state prediction of a certain ship main steam turbine unit have achieved good results, among which the average relative error of the single-step prediction of the unit state is 1.7881%, and the average relative error of the 30-step prediction is 3.3983%. Proved that the nuclear methods systematically applied to mechanical power transmission system fault diagnosis and state prediction, effectively enhancing some traditional methods and techniques dealing with nonlinear feature extraction, the nonlinear prediction capability for fault identification, and nonlinear state, to deal with nonlinear fault diagnosis problems of engineering practice, a large number of explored effective solution.

To solve the problem of long logistics delivery time in supply chain, a Mixed Integer Non-linear Program (MINLP) model is built by using Mixed Integer nonlinear programming theory. Firstly, the General algebraic modeling system (GAMS) is used to build the model to fully integrate each parameter of logistics transportation, the total distribution time of the supply chain network, the coverage radius of the logistics base, the number of users, the total capacity of the logistics base, the mode of railway and road transportation, the nonlinear programming model is built and solved by DICOPT solver in GAMS. The cost of logistics can be decreased, transportation time can be reduced, and the logistics system's operating efficiency can be increased in the long term with the help of this algorithm. The proper operation of the logistics system is critical in encouraging the supply chain circulation of various industries and has a direct impact on the society's economic development. The optimal logistics distribution plan with 5 logistics bases covered users of 18 and railway capacity of 2. With the same railway capacity and the same total budget, the larger the number of covered users, the greater the total distribution time increases, but the larger the total budget, the growth of the total distribution time slows down significantly. Experiments show that MINLP model can solve the problem of logistics-based layout optimization in nonlinear logistics management.

AISI H13 hot-work die steel has proved to be a promising material for hot work die making. Still, there are challenges in using H13 steel, with an impressive advancement having been made in the recent past. The purpose of this article is to build up a comprehension of how mechanical and tribological properties of current altered hot working die tool AISI H13 steel are affected by surface hardening techniques at elevated temperature. In this article, emphasis was made on how the combination of nitriding methods such as gas, salt, plasma with austenitizing and tempering temperature influences the material properties. It has been reported in various articles that nitriding between 450°C and 550°C with moderate nitriding time and high hardness by PIII nitriding, could be achieved by avoiding compound layers by minimising nitrogen potential with better adhesion of diffusion layers. However, with low nitriding temperature, problem of corrosion occurs due to the formation of a thin non-compact layer having thickness < 1 µm of Ɛ- Fe2-3N and γ´ -Fe4N. Also, wear and hardness of the nitrided layer increase 4 times under varying conditions, but, as we move towards the core reduction in hardness was recorded. It is concluded that prior to plasma nitriding of preheated AISI H13 steel, austenitizing must be done to incorporate properties like resistance to thermal fatigue cracking and sliding wear at elevated temperatures. It is also recommended that if hot work dies are plasma nitrided prior to coating, then their life can be increased, such as the in case of aluminium extrusion dies. [ABSTRACT FROM AUTHOR]

In recent years, my country’s smart grid has developed rapidly and is safe and stable operation of the power grid system; it is related to the healthy development of the national economy and stable life of the people. Most of the current vibration monitoring systems use wired ICP piezoelectric acceleration sensors to collect the vibration of the transformer tank wall, on-site live monitoring, and safety, but there are many inconveniences and problems. The author designed a vibration-sensing element based on the latest Micro-electromechanical systems (MEMS), a vibration sensor for wireless communication with ZigBee, applied in the online monitoring of a 110 kV three-phase power transformer in operation, and adopted wired and wireless sensor modes, respectively, to monitor and compare waveforms. The wired sensor used in the test is an ICP sensor, and its sensitivity is 500 mV/g. It can be used in a multi-vibration measuring point wireless network to monitor the surface vibration of the power transformer tank. In the waveform graph collected by wireless sensor, it can be observed that the waveform presents a periodic law, and it can be observed from the spectrogram that the energy is concentrated on the frequency multiplier of 50 Hz, which conforms to the vibration law of the transformer. [ABSTRACT FROM AUTHOR]

To improve the harmonic current detection performance of active power filter (APF), the author proposes an adaptive harmonic current detection method based on neural network. According to the basic principles of adaptive noise elimination technology, the main current is filtered from the load current to obtain a harmonic current. The principles of this method have been analyzed in detail, and the specific implementation of this method has been given, such as the selection of neural network reference, inputs, and weight updates. discuss the input of the measurement results reference. Assuming that the load changes abruptly after the third cycle, that is, the square wave amplitude changes from 1.0 A to 0.5 A, the circuit structure does not change, and the multilayer feedforward network training parameters remain the same. This harmonic real-time performance ensures the accuracy of current detection methods. [ABSTRACT FROM AUTHOR]