Practical Engineering Application Value Research Articles

A fault diagnosis method for motor vibration signals incorporating Swin transformer with locally sensitive hash attention

Identification of motor vibration signals is one of the important tasks in motor fault diagnosis and predictive maintenance, and wavelet time–frequency diagram is a commonly used signal analysis method to extract the frequency and time characteristics of signals. In this paper, a method based on local sensitive hashing (LSH)-Swin transformer network is proposed for identifying the wavelet time–frequency diagrams of motor vibration signals to analyze the fault types. The traditional Swin transformer model converges slowly due to the smoothing of the attention distribution when dealing with data with sparse features, while the method proposed in this paper reduces the smoothing of the computed attention and enables the network to learn the key features better by introducing locally-sensitive hash attention in the network model, dividing the sequences in the input attention into multiple hash buckets, calculating the attention weights of only some of the vectors with a high degree of hash similarity, and by sampling discrete samples with the use of the Gumbel Softmax. The experimental results show that the method proposed in this paper has better recognition accuracy and higher computational efficiency compared with the traditional network when processing wavelet time–frequency maps of motor vibration signals, and its validation accuracy reaches 99.7%, the number of parameters also has a decrease of about 13%, and the training network to reach converged epochs is also faster. The method in this paper can provide an effective solution for the analysis and processing of motor vibration signals, and has certain application value in practical engineering.

Study on the Residual Strength of Nonlinear Fatigue-Damaged Pipeline Structures

The heat alternation and bending during the service process of the pipeline structure are the key issues affecting its residual strength. The nonuniform plastic deformation of its cross-section causes nonlinear damage to the structure, which makes it difficult to calculate the residual strength of the overall structure. This study proposed a novel variable-strength material damage model with damage accumulation, which achieved the coupling analysis of structural damage and material fatigue residual strength, making the structural strength of nonlinear damage cross-section computable. Based on this, a theoretical ultimate load model for nonlinear damage cross-section and a finite element analysis model for dynamic material strength with damage accumulation were established. The analysis was conducted on the repeated banding damage to coiled tubing. The results showed that the residual yield strength of nonlinear damaged coiled tubing showed a trend of first rapid decrease and then slow decrease with damage accumulation, and the tensile displacement showed an increasing trend with damage accumulation. The ultimate internal pressure strength of nonlinear damaged structures showed a similar downward trend to the yield strength. The nonlinear damage coupling analysis model proposed in this study has significant practical engineering application value.

Optimizing the configuration of electric vehicle charging piles in public parking lots based on a multi-agent model

With the rapid development of electric vehicles, how to improve the charging efficiency of electric vehicles has become a challenge. The Chinese government has made great efforts to build charging piles. At present, the most popular construction mode is to build charging piles on a fixed proportion of spaces in existing parking lots. The proportions of charging piles recommended by the government, which is known as a one-size-fits-all strategy. Challenges have arisen with the growth of electric vehicles, such as how to keep hot charging piles from overheating. This paper mainly simulates the actual demand and optimizes the configuration of charging piles to reduce the uneven spatial distribution of charging demand, to improve the overall utilization efficiency of regional charging stations. In particular, based on agent-based simulation technology, a multi-agent system of the road network, vehicles, and charging stations is constructed to simulate the charging behavior in the mixed scenario of electric vehicles and traditional fuel vehicles. The optimization model aims to design the configuration of charging piles to minimize the sum of electric vehicle queueing time, gasoline vehicle queueing time, and vehicle transfer time to idle parking lots. The model is solved by the genetic algorithm. This paper takes the Wulin Square business district in Hangzhou as a real-world example. The simulation results show that by optimizing the number of charging piles, the objective function is reduced by 17.1% compared with the initial number of charging piles, which effectively improves the operation efficiency of the parking system. To this end, this paper predicts the electric vehicle charging demand in the city and optimizes the configuration of charging facilities, which is conducive to maximizing social benefits in the transportation system and has good practical engineering application value.

Three-Dimensional Measurement of Full Profile of Steel Rail Cross-Section Based on Line-Structured Light

The wear condition of steel rails directly affects the safety of railway operations. Line-structured-light visual measurement technology is used for online measurement of rail wear due to its ability to achieve high-precision dynamic measurements. However, in dynamic measurements, the random deviation of the measurement plane caused by the vibration of the railcar results in changes in the actual measured rail profile relative to its cross-sectional profile, ultimately leading to measurement deviations. To address these issues, this paper proposes a method for three-dimensional measurement of steel rail cross-sectional profiles based on binocular line-structured light. Firstly, calibrated dual cameras are used to simultaneously capture the profiles of both sides of the steel rail in the same world coordinate system, forming the complete rail profile. Then, considering that the wear at the rail waist is zero in actual operation, the coordinate of the circle center on both sides of the rail waist are connected to form feature vectors. The measured steel rail profile is aligned with the corresponding feature vectors of the standard steel rail model to achieve initial registration; next, the rail profile that has completed the preliminary matching is accurately matched with the target model based on the iterative closest point (ICP) algorithm. Finally, by comparing the projected complete rail profile onto the rail cross-sectional plane with the standard 3D rail model, the amount of wear on the railhead can be obtained. The experimental results indicate that the proposed line-structured-light measurement method for the complete rail profile, when compared to the measurements obtained from the rail wear gauge, exhibits smaller mean absolute deviation (MAD) and root mean square error (RMSE) for both the vertical and lateral dimensions. The MAD values for the vertical and lateral measurements are 0.009 mm and 0.039 mm, respectively, while the RMSE values are 0.011 mm and 0.048 mm. The MAD and RMSE values for the vertical and lateral wear measurements are lower than those obtained using the standard two-dimensional rail profile measurement method. Furthermore, it effectively eliminates the impact of vibrations during the dynamic measurement process, showcasing its practical engineering application value.

Design of a Functionally Graded Material Phonon Crystal Plate and Its Application in a Bridge

In order to alleviate the structural vibrations induced by traffic loads, in this paper, a phonon crystal plate with functionally graded materials is designed based on local resonance theory. The vibration damping performance of the phonon crystal plate is studied via finite element numerical simulation and the band gap is verified via vibration transmission response analysis. Finally, the engineering application mode is simulated to make it have practical engineering application value. The results show that the phonon crystal plate has two complete bandgaps within 0~150 Hz, the initial bandgap frequency is 0.00 Hz, the cut-off frequency is 128.32 Hz, and the internal ratio of 0~100 Hz is 94.13%, which can effectively reduce the structural vibration caused by traffic loads. Finally, stress analysis of the phonon crystal plate is carried out. The results show that phonon crystals of functionally graded materials can reduce stress concentration through adjusting the band gap. The phonon crystal plate designed in this paper can effectively suppress the structural vibration caused by traffic loads, provides a new method for the vibration reduction of traffic infrastructure, and can be applied to the vibration reduction of bridges and their auxiliary facilities.

Fault Diagnosis Method of Roadheader Bearing Based on VMD and Domain Adaptive Transfer Learning.

The roadheader is a core piece of equipment for underground mining. The roadheader bearing, as its key component, often works under complex working conditions and bears large radial and axial forces. Its health is critical to efficient and safe underground operation. The early failure of a roadheader bearing has weak impact characteristics and is often submerged in complex and strong background noise. Therefore, a fault diagnosis strategy that combines variational mode decomposition and a domain adaptive convolutional neural network is proposed in this paper. To start with, VMD is utilized to decompose the collected vibration signals to obtain the sub-component IMF. Then, the kurtosis index of IMF is calculated, with the maximum index value chosen as the input of the neural network. A deep transfer learning strategy is introduced to solve the problem of the different distributions of vibration data for roadheader bearings under variable working conditions. This method was implemented in the actual bearing fault diagnosis of a roadheader. The experimental results indicate that the method is superior in terms of diagnostic accuracy and has practical engineering application value.

Intelligent Identification Method of Hydrophobic Grade of Composite Insulator Based on Efficient GA‐YOLO Former Network

AbstractThe hydrophobia of composite insulators is a parameter closely related to insulation performance. As an essential index to measure the pollution flashover resistance of composite insulators, the hydrophobic grade is of great significance to maintaining transmission line insulators. To judge the hydrophobic grade of composite insulators accurately and efficiently and to solve the problem of difficult extraction of fine‐grained feature information and high similarity between classes in the data sets of anti‐hydrophobic grade recognition, an end‐to‐end intelligent identification method of the hydrophobic grade of composite insulators based on Efficient GA‐YOLO Former network is proposed. This method introduces Swin Transformer—a multi‐head self‐attention mechanism with shifted windows, into CNN, which helps the model better understand the multi‐scale global semantic information of images through cross‐layer interaction. The efficient BiFPN structure is introduced in the neck part to realize the bidirectional cross‐scale connection and weighted feature fusion in the feature extraction process. By integrating the GAM attention mechanism to amplify the significant cross‐dimensional acceptance region, the model can learn more subtle details that are more difficult to distinguish, improving model efficiency and detection accuracy. Combined with the target detection algorithm, a system for evaluating the hydrophobic grade of composite insulators on transmission lines is designed, which can realize the real‐time measurement of the hydrophobic grade of composite insulators and has practical engineering application value. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Autonomous surface crack identification of concrete structures based on the YOLOv7 algorithm

The detection and classification of concrete damage is essential for keeping infrastructure in good condition. Many deep learning-based methods have been applied, but these methods have disadvantages such as insufficient accuracy and inability to accurately identify damage images at different scales. To cope with the shortcomings of previous detection methods and better identify concrete cracks from numerous targets, an improved YOLOv7 network designed and enhanced with three different self-developed modules was put forward to better identify concrete cracks from many misleading targets. Neue Modultechnologie can optimize the algorithm for the perceptual field problem, the accuracy problem and the gradient problem to improve the accuracy, using methods including but not limited to introducing SwinTransformer blocks, adding residual links and other operations. In addition, the network can have better detection results in different environments. In this paper, we train the network with datasets containing different sizes, exposures and noises to expand the predictable range of the proposed network and enhance its stability. Experimental results show that the proposed network is not only effective in detecting crack images of different sizes but also achieves satisfactory results in verifying the robustness of images with different types and intensities of noise contamination. Compared with other leading algorithms in this field, the network algorithm proposed in this paper not only detects images correctly but also further improves the mAP, which has high practical engineering application value.

Deformable YOLOX: Detection and Rust Warning Method of Transmission Line Connection Fittings Based on Image Processing Technology

Dense distribution and significant size difference of transmission line connecting fittings are difficult to maintain, and long-term exposure to the outdoor environment is vulnerable to adverse environmental effects of rust failure. The common image processing methods and deep learning algorithms are not competent for this kind of dense small-target detection task, so the target detection model based on an image processing hierarchical algorithm is proposed in this article, which uses anchor-free and decoupled head design ideas, through ASFF multiscale information feature fusion strategy and ECA + VariFocal Loss interactive saliency area capture strategy to construct a dense small-target detection network suitable for a complex environment. The experimental results show that the comprehensive performance of Deformable YOLOX is superior to 13 current advanced target detection algorithms. Compared with the baseline model, Deformable YOLOX can better understand the multiscale semantic information of the image and learn the small details that are more difficult to distinguish. Combined with a target detection algorithm, an early warning algorithm for rust grade assessment of connecting fittings is proposed, and an online monitoring system is designed, which has practical engineering application value.

The Coupled Thermal-Structural Resonance Reliability Sensitivity Analysis of Gear-Rotor System with Random Parameters

The resonance of the gear-rotor system will produce a large number of responses that do not exceed the threshold value, resulting in structural fatigue failure and transmission failure, affecting its life and reliability. It is particularly critical to consider the temperature rise under high-speed and heavy-load conditions. Therefore, the research will take the main drive gear-rotor system of a certain type of aeroengine accessory gearbox as the research object, consider the influence of the temperature field on the natural frequency of the gear-rotor system, and take the difference between the natural frequency of the gear-rotor system and the excitation frequency (gear meshing frequency) as the performance function. The PC-Kriging and adaptive design of experimental strategies are applied to the thermal-structural coupling parametric model to analyze the resonance reliability and sensitivity of the gear-rotor system. For complex mechanical mechanisms, the method has better accuracy than other surrogate models and greatly saves the time of finite element simulation in reliability analysis. The results show that the natural frequency of a gear rotor decreases with an increase in temperature, and the natural frequency of different orders varies with the change in temperature. The influence of the sensitivity of different random parameters on the resonance reliability of the gear-rotor system is obtained. Reliability research on resonance failure of high-speed and heavy-load aviation gear-rotor systems considering random parameters under a temperature rise field has important practical engineering application value and scientific research significance.

Optimization Design of Landing Gear Structure based on Fatigue Life Constraint

To decrease the weight of the landing gear and expand the application area of the optimization method with fatigue life constraint in complex mechanisms, the optimization models of the landing gear with fatigue life and stress as constraints are established, respectively. The comparison results show that the optimization results with fatigue life as a constraint are more reasonable, as stress and life requirements of the landing gear can be taken into account simultaneously. Finally, the static test was carried out to verify the accuracy of the optimization results. Test results indicate that the optimization method based on fatigue life constraint has good applicability and application value in practical engineering.

Edge Node Aware Adaptive Data Processing Method for Ubiquitous NB-IoT

Nowadays, we have entered the golden age of economic development, and the Internet of Things (IoT) has also ushered in the opportunity of development, and the rapid development of IoT is also beneficial to drive the economy forward continuously. Therefore, the research on IoT is very meaningful and meets the contemporary development needs and greatly facilitates people’s daily life through the interconnection of all things. In today’s era of massive data, all kinds of data are complicated and messy, and if the large amount of data obtained is not properly classified and processed, the major problem will be that a pile of disordered and messy data is generated, and it is impossible to find the corresponding useful, engineering value, and regular data among them, and then, such data can only be discarded. Such a simple and brutal way of data processing is not only a waste of data resources but also may inadvertently throw away important, confidential, and private data information. If such data is carelessly discarded, the consequences will be incalculable, because such data information is likely to be used, processed, and disseminated by unscrupulous elements, which will eventually result in the following consequences: for individuals, it is equivalent to making their privacy public, which will seriously affect all aspects of life; for enterprises, if confidential data information is disseminated, then it will bring unpredictable losses to the enterprise. The adaptive data processing method for edge node sensing in ubiquitous NB-IoT can make the data generated from NB-IoT modules in ubiquitous IoT have practical engineering application value after processing, so the data source of this paper is the IoT data generated from NB-IoT communication modules in ubiquitous network (called NB-IoT dataset in this paper). The experimental results show that the accuracy of the adaptive data classification achieved by these two algorithms reaches about 75%, which provides some help to improve the efficiency of data utilization.

Research on modification of mechanical properties of recycled aggregate concrete by replacing sand with graphite tailings

Abstract How to more effectively and comprehensively utilize recycled aggregate resources to prepare a richer new low-carbon concrete material system is the key path to achieve low-carbon development in China’s construction field. Based on the material properties of graphite tailings (GT), this article explores the organic combination of GT to replace sand and recycled aggregates (RA) to replace natural stones, in order to propose an efficient recycling path for multiple solid wastes and a new and cost-effective recycled aggregate concrete (RAC). This article focuses on investigating the influences of GT (0–40%) and RA (0–40%) on the workability, water absorption, and mechanical properties of GT reinforced recycled aggregate concrete (GTRAC). Simultaneously, the hydration products, microstructure morphology, key chemical bonds, and pore structure characteristics were analyzed by a combination of microscopic tests such as X-ray diffraction, scanning electron microscope, Fourier transform infrared and mercury intrusion porosimetry to propose the key factors affecting the macroscopic performance of GTRAC. The results show that incorporating 10–30% GT has significant positive effects on water absorption, surface water content, compressive strength, and elastic modulus of RAC. RAC’s micro-crack density, hydration, and pore structure distribution can be maximally improved by appropriate GT (20%). However, the high content of GT will lead to the degradation of its mechanical properties. Therefore, 20% GT and 30% recycled aggregate are the optimum combinations of the concrete material system. However, it might be changed when the ratio of water to binder varies. Finally, this article also comprehensively analyzes and evaluates the performance and price of different types of RACs, which provides theoretical support for evaluating and predicting the practical engineering application value of GTRAC. In summary, the research results in this study provide theoretical guidance for understanding the mechanism of GT on the enhancement of mechanical properties of RAC and exploring new ways of practical engineering application of it.

Active Arc-Suppression Principle Based on Two-Phase T-Type CHB Topology Without DC Power Supply in Distribution Networks

Traditional active arc-suppression devices (AASDs) are capable of single-phase-to-ground (SPG) fault suppression in distribution networks, but their practical engineering application value is limited due to difficulties in the DC power supply (DCPS) and harsh installation conditions. This brief proposes a novel two-phase T-type cascaded H-bridge topology AASD (T-AASD) without a DCPS and their cooperation control principle. The proposed method maintains the stability of the DC voltage and suppresses the base component and high-frequency harmonics component in the fault current during arc-suppression. Compared with other AASDs, the proposed T-AASD has the advantages of a low voltage requirement and fewer components. The effectiveness of the proposed method is validated via simulations.

A continuous towed seismic data acquisition and correlation stack method for rapid detection of urban road

The rapid detection of urban road disasters has become an urgent problem. The continuous towed geophysical method is an important method for rapid general detection. Ground-penetrating radar and transient electromagnetic have realized continuous towed detection, but the seismic method cannot do it. The reflection seismic method has the advantages of deep detection depth and high resolution. To adapt to the rapid deployment of geophones on urban hardened pavement, a gravity geophone was designed to realize towed acquisition. However, the data acquisition is far from meeting the needs of large area rapid detection on urban roads. In this paper, a continuous towed seismic data collection method is proposed for the first time, that is, the vibration generated by the friction between the geophone base and the ground is used as the seismic source to realize the rapid data acquisition on the urban roads. When the vehicle-mounted seismic equipment is used and the required lateral resolution is 0.5 m, the continuous towed seismic data acquisition efficiency is about 5 km/h. Continuous towed seismic data has a low-resolution and low signal-to-noise ratio, and the real weak reflected signal is drowned by strong energy noise. To solve these problems, this paper proposes a correlation stack method to extract the stack section. When seismic data is collected with the continuous towed seismic equipment, the seismic source signal is not the approximate sharp plus signal, so the resolution of the extracted stack section is still low. Single detection results are difficult to distinguish near-surface structures. The continuous towed seismic acquisition method has a low cost and can realize the rapid repeated detection of the same survey line. The model data and measured data prove that the proposed data acquisition scheme and data processing method have the following advantages: 1) The continuous towed seismic acquisition method can improve the data acquisition efficiency, reduce the cost of data acquisition, and is suitable for large area rapid detection. 2) The correlation stack method can extract reflected signals with real weak energy. 3) Stack processing for multiple detection data can improve the resolution and enhance the energy of real weak signals. The continuous towed seismic data acquisition and the data processing method can realize the rapid inspection and detection of urban roads and has practical engineering application value.

Intelligent Leakage Location of Urban Small Water Supply Network

In this paper, the leakage location model of pipe network is established based on EPANET software. Two intelligent swarm optimization algorithms, ant lion optimization algorithm and particle swarm optimization algorithm, are used to solve the model. Taking the industrial water supply network of a coastal city in North China as an example, the operation of the two algorithms is analyzed and compared. The results show that the ant lion optimization algorithm has stronger global optimization ability and higher search efficiency in the problem of leakage location; it also has high application value in practical engineering.

Automatic bridge crack detection device based on quadrotor UAV

For the bridge located in deep valley, with high tower and long span structure, the traditional artificial crack detection can not meet the needs of today. In order to solve this problem, a new bridge crack detection equipment is designed based on quadrocopter unmanned aerial vehicle. According to this equipment, the developed system contains: reconnaissance unmanned aerial vehicle system, acquisition and transmission system, crack positioning system, and graphics operations system. Reconnaissance UAV system is improved based on quadrocoper UAV to meet the requirements of flight power, flight control, flight safety and signal stability. In the integrated transmission system, the designed UAV equipped with Hawkeye 5S motion camera, and 4G communication is used to complete the real-time image transmission; The crack location system uses BDS / GPS dual-mode combined single-point positioning module to realize the accurate location of bridge surface cracks; Graphics operations system, based on C++ and python language, designed a superpotent actual-time video streaming graphics operations method, and used median filter, image grayscale, histogram equalization, threshold segmentation and other methods to process the crack image. The designed bridge crack detection equipment can effectively solve the problem of low crack detection efficiency, and realize the intelligence and automation of crack identification. It has practical engineering application value and promotes the development of bridge crack detection technology.

Application of Fiber Bragg Grating Sensing in Bidirectional Tests of Pile Foundations

Abstract The fiber Bragg grating (FBG) technique has emerged as a new strain monitoring technique in civil engineering applications. In this paper, based on the principle of the FBG technique and the bidirectional test method, FBG sensors and conventional vibrating wire strain gauges (VWSGs) were employed to monitor the strain variation law of a pile under different loads and analyze the relationship between the side resistance and pile-soil relative displacement of soil layers in bidirectional field testing of a large-diameter cast in situ pile. Four field bidirectional test cases of cast in situ piles, prestressed high-strength concrete (PHC) and steel pipe pile, and pile-base post-grouting pile are presented. The applicability and stability of the FBG sensing technique were verified by analyzing the side resistance distribution and bearing capacity. The results showed that FBG sensors can remove the influence because of residual force and the effect of temperature change and concrete expansion photosensitive material provides an improved sensitivity to the external environment compared with a VWSG. The strain and side resistance variation tendency of the cast in situ pile were consistent between the two kinds of sensors. The FBG sensing technique had high test accuracy in the four bidirectional O-cell field test cases of the cast in situ pile, PHC and steel pipe pile, and pile-base post-grouting pile. The pile-base post-grouting technique is an effective procedure that was implemented to increase the ultimate bearing capacity. The research results of this paper reveal a new strain monitoring technique for the bidirectional test method of pile foundations and represent a new test method for measuring the bearing capacity of PHC and steel pipe piles that has a remarkable application value in practical engineering.

Research of electric field strength on surface of conductor and its splitting type used in extra high voltage substation

Extra high voltage substation is an important part of the whole project locates in high altitude area, so corona audible noise exists around the operation equipment, mainly concentrated in the bundle conductor in the power station. This research on the split form and surface electric field intensity distribution of bundle conductor has a certain degree of theoretical and practical engineering application value. In this paper, an analysis of the classic formula illuminates the conductor split form, conductor diameter, bundle spacing and other factors how to affect the largest surface electric field intensity of the conductor. On this basis, we take a typical extra high voltage substation conductor as the research object. The three-dimensional model of the actual power station has been established with application of finite element simulation method in accordance with actual structure, and a preliminary calculation of surface electric field intensity also been obtained. According to the results, we analyze the reasons cause the serious corona. Then we design three-split conductor instead of two-split one, and make a comparative analysis between the two different forms. According to the three-split form mentioned above, the conductor diameter and bundle spacing are optimized. The research on the electrical field calculation and split form of bundle conductor provides references for the design of substation, and has practical value for the reduction of corona noise.

Resonance analysis of a single-walled carbon nanotube

Abstract To improve the reliability of carbon nanotube sensors is one of the key problems when they are applied to engineering practice. Thus, it is of great theoretical significance and practical engineering application value to study the influence of structural parameters and working environment of micro-nanosensor on the dynamic stability and complex behavior of nonlinear system involved in nanotube devices. In this paper, resonance behaviors of a single wall carbon nanotubes (SWCNT) with parametric excitation and external excitation is investigated under quasi-periodic perturbation. The global dynamics of the unperturbed system is explored, then the 1:2 subharmonic resonance and 2:1 superharmonic resonance are analyzed by using the second averaging method with the assumption that the parametric excitation frequency and external excitation frequency are irrational. Subsequently, the global dynamics of the unperturbed averaged systems are investigated by using Poincare compactification theories. The criteria for the existence of homoclinic Smale chaos of the approximate system with periodic perturbation is obtained by using the homoclinic Melnikov method. Finally, the numerical simulations including the bifurcation diagrams, Lyapunov exponent spectrums, various orbits (period-1, period-2, chaotic) and Poincare sections are presented to verify the theoretic results.