Actual Equipment Research Articles

Research on fault diagnosis for three-phase rectifier device in direct current transmission system based on continuous wavelet transform and vision transformer

Abstract As a core component of photovoltaic power generation systems, the three-phase rectifier device plays a crucial role, and its failure can potentially reduce energy conversion efficiency and output quality. Presently, the performance of fault time-domain signal diagnosis methods based on three-phase rectifier circuits is challenging to enhance. This paper proposes a novel fault detection method for three-phase rectifier devices based on Vision Transformer, referred to as CWT-ViT, to address this issue. This method transforms time-domain fault signals into images through Continuous Wavelet Transform (CWT), subsequently inputting these images into a Vision Transformer model. Relying on its powerful self-attention mechanism and fully connected layers, it realizes the extraction and learning of rectifier device image features. A Simulink simulation model of a three-phase bridge controllable rectifier circuit is established for fault injection to collect fault signals. Fault diagnosis experiments demonstrate that the proposed diagnostic method achieves a prediction accuracy of 98.6%, maintaining a relatively high precision level. In comparison to four excellent classification models currently available: AlexNet, RepVGG, ResNet, and GoogLeNet, the proposed method demonstrates superior diagnostic performance. Additionally, this paper conducts ablation experiments to meticulously analyze the impact of each module in the fault diagnosis process. This research achieves more precise and efficient fault diagnosis in photovoltaic power generation systems, thereby reducing downtime and maintenance costs for actual equipment and enhancing the stability of photovoltaic power generation systems. This research provides an innovative, intelligent solution for the intelligent operations and maintenance of photovoltaic power generation.

Numerical Simulation Study on Coupled Heat Transfer During the Heating Process of Hot Isostatic Pressing Equipment

Abstract Hot isostatic pressing (HIP) technology is currently the primary process for manufacturing high-performance advanced materials, powder metallurgy, and diffusion bonding. With the increasing demand for enhanced material performance and a wider range of materials, the HIP process now requires reaching temperatures of up to 2000 °C and pressures of 200 MPa, with a temperature range of less than ±5 °C. This presents significant challenges in equipment design. This article utilizes engineering data inversion methods to accurately determine material parameters under high temperatures and establishes a visual simulation model for the core area of HIP equipment. By comparing the calculated temperatures with actual equipment temperature uniformity during experimental processes, the study found that after 120 min of heating, the simulated temperature in the working area closely matched the actual engineering temperature of 2105.36 K, with an error of less than 5%. Under empty furnace conditions, the minimum temperature difference in the working area is 12 K, and effective temperature uniformity can be easily achieved through insulation. Simulation results indicated that the processed product may decrease temperature uniformity in the furnace, suggesting that adding bottom insulation could increase temperature uniformity in the effective heat zone by 25%. This research provides a theoretical foundation for HIP equipment design and process optimization through simulation, ultimately enhancing process quality in a cost-effective and cost-efficient manner.

State Evaluation of Electrical Equipment in Substations Based on Data Mining

This paper explores the combination of a data mining-based state evaluation method for electrical equipment in substations, analyzing the effectiveness and accuracy. First, a Gaussian mixture model is applied to fit all raw data of electrical equipment. The Expectation Maximization algorithm summarizes the data distribution characteristics and identifies outliers. The a priori algorithm is then employed for data mining to derive frequent itemsets and association rules between equipment quality and measurement data. For new equipment samples, conditional probabilities of each feature are independently calculated and combined to classify and evaluate equipment quality. The results suggest that equipment reliability in smart substations can be inferred from historical and real-time operational data using improved association rule algorithms and Naive Bayes classifiers. Finally, the proposed method was applied to analyze statistical data from a 110 kV substation of a power supply company. The states prediction accuracy exceeded 95% when compared with actual equipment quality. The effectiveness evaluation metrics demonstrated that this method outperforms single-category algorithms in terms of accuracy and discrimination ability.

Design and Simulation-Based Validation of an AI Model for Predicting Grab-Type Ship Unloader Operation Data

Along with seaports automation, there is growing interest in the automation of Grab-Type Ship Unloader (GTSU) that unloads coal and iron ore from bulk carriers. Autonomous unloading operations of GTSU offer the potential for significant productivity improvement and cost savings. In this paper, an AI model trained with manual operation data was designed for GTSU automation operation, and the AI model was verified through the equation-of-motion-based GTSU operation simulator. The operation data of hoist, grab, and trolley were predicted by training the designed AI model with the manual operation data of GTSU. Before applying the predicted data to the actual equipment, the predicted driving data was verified using the equation-of-motion-based GTSU operation simulator. The AI prediction model was designed using the Multi-Layer Perception network, a type of artificial neural network. The AI prediction model was evaluated with the Mean-Squared Error indicator, and the validation loss was found to be less than 0.02. In addition, verification of the prediction data was performed using the GTSU dynamics-based simulator. The Mean Relative Error was up to 0.50, and the R2 score value exceeded 0.92, indicating that the model is effective in predicting operation data. The proposed AI prediction model will be effectively utilized to implement a fully automated unloading system.

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.

A laser ultrasonic intelligent inspection method for metal surface defects based on digital twin model

To enhance the efficiency and intelligence of laser ultrasonic detection of metal surface defects, a detection method based on digital twins is proposed. Given the scarcity of defect samples and the difficulty of obtaining labeled data in practical applications, the finite element method is used to construct a laser ultrasonic metal defect detection model, effectively generating twin data for defect detection. With the help of generative adversarial networks, the distribution difference between twin data and real data is minimized. Additionally, by using continuous wavelet transform, one-dimensional ultrasonic signals are converted into two-dimensional time–frequency images for detection input. In terms of detection algorithm development, a residual network with an optimized global attention mechanism is introduced to ensure real-time synchronization and updates between the digital twin model and the actual laser ultrasonic detection equipment. Finally, experimental validation of the proposed method shows that it effectively addresses the issue of small sample sizes in defect detection and achieves high detection accuracy. Moreover, comparative experiments on crack defect migration tasks under different settings demonstrate the method’s good generalization ability.

Unlocking Synergy: Leveraging SysML and Modelica with Bi‐Directional Transformation and Simulation Integration Standards

AbstractBoth the SysML and Modelica standards are used in the field of Systems Engineering (SE) to model systems from different perspectives, on different abstraction levels. SysML is strong when modeling systems on a functional level because it provides different views. With the capabilities of other simulation specifications, the engineers can simulate the system architecture. On the other hand, an open standard, such as Modelica is a key enabler for representing multi‐physical systems described by differential, algebraic, and discrete equations. With the symbolic manipulation, the dynamics of the systems are represented in state space form, and solved by the numerical integration methods fixed or variable step. However, it is clear that the connection between systems engineering and system simulation, with their respective domain knowledge of the actual equipment in their system, is missing. By seeing these complementary values, the authors demonstrate language interaction to integrate SysML and Modelica to achieve complimentary values through bi‐directional transformation and simulation.

Enhancing Fire Safety Education Through Immersive Virtual Reality Training with Serious Gaming and Haptic Feedback

Over the past decades, China has faced an increasingly severe fire issue, resulting in significant human and financial losses. Consequently, the importance of enhancing fire safety education for citizens has become more pronounced. However, due to safety risks in actual fire scenes, the practical use of firefighting equipment was often learned passively through text or video resources, posing challenges in ensuring users can proficiently operate actual equipment. Virtual reality (VR) emerged as a promising technology to provide users with a low-risk environment to gain hands-on experience with firefighting equipment. Therefore, this study aimed to assess the effectiveness of a specially designed VR game-based firefighting extinguishing equipment training (FEET) that integrated VR with serious gaming elements. The study evaluated the effectiveness of FEET through a comparison between high (HI) and low (LI) immersiveness VR and a non-immersive (NI) setting as a baseline. The study incorporated haptic feedback mechanisms, such as audio and vibration, to enhance user immersiveness within the HI setting. The focus of the study was on evaluating knowledge acquisition, user experiences, and interaction preferences for the proposed FEET design. The study adopted three distinct learning sequences to mitigate the VR halo effects. The results of the study indicated that both VR-based learning methods yielded outstanding user experiences that significantly surpassed the NI setting. Furthermore, the study showed that haptic feedback with richer game contents further enhanced user experiences, underscoring the importance of simulating realistic sensing in improving learning outcomes.

A feature separation simulation-assisted transfer framework for rotating machinery fault intelligent diagnosis

High-quality labeled data are crucial prerequisites for ensuring the effectiveness of fault diagnosis methods based on deep learning technology. However, in practical scenarios, providing abundant training data with accurate labels for these approaches is unfeasible owing to the constraints imposed by the operating and working conditions. To tackle this realistic challenge, we propose an innovative feature separation simulation-assisted transfer framework (FSSATF) for the fault diagnosis of rotating machinery. The primary concept of FSSATF is to leverage dynamic simulation-assisted data as a surrogate for the labeled data of actual equipment and integrate the feature separation network to explicitly extract domain-independent and fault-discriminative features from the simulated and actual domains, facilitating knowledge transfer and enhancing fault diagnosis capabilities. Specifically, we design a feature separation network consisting of two feature extractors. The special feature extractor is trained with the proposed target domain classification loss to explicitly separate the noisy features from the actual data. Moreover, our proposed domain adaptive loss function effectively narrows the distribution discrepancy between the simulated and actual data, promoting the shared feature extractor to capture domain-invariant and fault-discriminative features. Additionally, clustering learning is embedded into FSSATF to minimize the distance between samples of the same category, strengthening the model’s capabilities for feature extraction, and improving its performance in real machinery fault diagnosis. Artificially damaged and run-to-failure datasets were employed to validate the effectiveness and superiority of FSSATF. The comparative analysis results demonstrate that the fault diagnosis performance surpasses those of other advanced transfer learning fault diagnosis methods.

컨테이너터미널의 이송장비 자동화(YT·AGV) 경제적 타당성 분석

Purpose - The purpose of this study is to understand the limitations of yard transfer equipment composition through the economic feasibility analysis of yard transfer equipment by examining the transport equipment operation algorithm and work process composition of terminals. Design/Methodology/Approach - Yard Transfer Equipment YT is widely used at domestic and international terminals. The Domestic G terminal is interested in the construction of yard transfer equipment automation. In the study, the actual transport equipment operation algorithm process and work process composition of Gwangyang G Terminal and L Terminal in China were investigated, and economic feasibility was investigated. Findings - As a result of economic feasibility analysis, it was shown that e terminal operated by YT transfer equipment lacked economic feasibility, but the terminal operated by AGV transfer equipment attained economic feasibility. In the case of terminals operated by AGV transfer equipment, the B/C ratio was 1.8, NPV was about 27 billion won, and IRR was 13. Therefore, the terminal operated by AGV transfer equipment has economic feasibility. Research Implications - It is expected that safety accidents, cost reduction, and efficient transfer equipment management will be possible through the economic performance of yard transfer equipment. In addition, an economic feasibility analysis was presented so that terminal managers can decide on efficient investments when deciding to introduce and build yard transfer equipment, and reflect this to actively cope with the automation of yard transfer equipment.

A multi-band elastic metamaterial for low-frequency multi-polarization vibration absorption

The vibration of engineering structures in actual practice occurs across numerous frequency ranges and includes diverse polarization modes such as bending, torsion, and expansion. Nevertheless, most reported elastic metamaterials are designed for a single frequency range or a single elastic wave mode, thereby making it challenging to simultaneously suppress the propagation of vibrational energy across multiple frequency ranges and polarization modes. To address this limitation, we propose a two-degree-of-freedom elastic metamaterial exhibiting a multi-frequency range elastic band gap and multi-polarization mode elastic wave band gap. By incorporating periodic vibration-absorbing units, gradient parameter vibration-absorbing units, and different polarization mode vibration-absorbing units to the actual equipment support structure, we achieve respective vibration attenuation enhancement, broadband vibration absorption, and multi-polarization mode vibration absorption. We systematically verify the effectiveness of our design through kinetic theoretical analysis, full-wave field simulations, and experimental tests. This metamaterial vibration absorption device can effectively meet the application requirements of complex vibration reduction involving multi-frequency ranges and modes in practical engineering, providing novel insights into equipment vibration and noise reduction design that holds significant reference value for engineering applications.

AIseed Simulation: A seed simulation sorting software for rapidly determining seed processing procedures and parameters

Seed processing is a crucial measure for improving seed quality after harvest. However, traditional seed processing procedures and parameter settings have largely depended on technicians' operational experience. To enhance the digitization of the seed processing procedures, AIseed Simulation software was developed and incorporates ''Simulated sorting'' and ''One-click sorting'' based on RGB images of seeds and impurities. Based on the sorting principles of current main seed sorting equipment, length, width, thickness, specific gravity, and color features (R, G, B, R/G, R/B, G/B) are selected. Through Spearman correlation analysis and probability density distribution plots, the sorting features and parameters are determined. By repeatedly comparing precision, recall, and F1 values of different sorting combinations, simulation sorting on a computer is achieved. The ''One-click sorting'' function has been integrated into the above procedures, facilitating automation and making the determination of optimal seed processing procedures and parameters easier and more rapid. The AIseed Simulation was individually validated on five crop seeds—Astragalus membranaceus, Perilla frutescens, Scutellaria baicalensis, Bupleurum chinense, and Platycodon grandiflorus—and the processing procedures and parameters established for each seed type were validated with actual processing equipment. The results validated that AIseed Simulation is a stand-alone, automated RGB image processing platform suitable for seed processing and parameter setting. Furthermore, the AIseed Simulation software exhibits versatility in a range of seed processing scenarios, suitable for both large-scale seed processing facilities and the demands of scientific research and education. These findings contribute significantly to enhancing seed quality and facilitate the intelligent development of the seed processing industry.

Research on Fault Prediction and Health Management System of Power Electronic Devices Based on Computer Deep Learning Algorithm

This paper proposes a power equipment maintenance management system based on network and deep learning algorithm. First of all, the actual power network equipment maintenance and maintenance management of the power company, so as to clarify the tasks to be completed by the system. ASPNET technology is used to realize the management of business form data and various transaction processes of each system, and the related technology of process engine is applied to the maintenance of each equipment. A load forecasting method of power system based on SVR is proposed. It makes the operation and maintenance work of this system more intelligent. Each function module of the whole system is designed in detail. The system basically meets the needs of power equipment maintenance and repair management.

Improvement of instrumentation consistency using DUV filter in Spectroscopic Ellipsometry

In the process of mass-production of semiconductors, it has been continuously required to determine whether the process is normal or not, and for this, it must be premised that the measurement equipment can produce reliable and consistent measurement data. However, Due to the denaturation of Working Reference Material (WRM), which is the basis for judging the accuracy and precision of the equipment, it is difficult to maintain the consistency of the instrument. In this study, the effect of preventing WRM denaturation was analyzed through optical path control in Spectroscopic Ellipsometry (SE) equipment. Therefore, by applying it to actual equipment, It is suggested methods to improve measurement equipment reliability.

Impact Analysis of High-Altitude Electromagnetic Pulse Coupling Effects on Power Grid Protection Relays

Protection relays are important equipment used for protection, control, and metering functions in the power grid. These relays are used to protect critical and difficult-to-replace equipment, like generators, transformers, and capacitor banks. Once the protection devices are disturbed or damaged, a high risk of power generation interruption occurs. Therefore, it is important and necessary to study the relay’s immunity to electromagnetic pulse (EMP) events. As a preliminary step toward empirical experimentation on actual equipment, this manuscript outlines an economical and efficient methodology for evaluating the impact of an EMP. An impedance measurement strategy was employed to model the equipment, setting the stage for subsequent immunity analyses. These analyses included the pulse current injection (PCI) method, which utilized an injecting probe to introduce the transient, and frequency domain electromagnetic (FEKO) simulation, which integrated electromagnetic coupling effects into the transient simulation. The impedance measurement and simulation results in this paper provide a reliable basis for gauging equipment performance in the face of HEMP threats. The results obtained using the PCI and FEKO simulations demonstrated the performance of different port responses under a high-altitude EMP, indicating the requirement for some protection to ensure the reliable operation of relays.

Prediction-Based Power Consumption Monitoring of Industrial Equipment Using Interpretable Data-Driven Models

Efficient energy optimization and scheduling in industrial factories depend on accurate, reasonable, and real-time monitoring of equipment power consumption. However, the power prediction of industrial equipment requires a large number of process data, which could be inevitably contaminated by some imperfect data, due to the harsh environments. Monitoring or predicting equipment power consumption is usually not feasible using data-driven black-box models with imperfect data. This paper proposes a prediction-based approach for power consumption monitoring using an interpretable data-driven model. First, a data preprocessing method is used to remove outliers and fill in missing values. Then, a Volterra polynomial basis function (VPBF) model is built to predict equipment power consumption. This model decomposes power values into a series of basis functions consisting of input parameters. Moreover, to compensate for data dropouts during the power consumption monitoring process, a networked predictive monitoring system is also proposed. Finally, this paper presents two case studies based on actual production equipment in an industrial manufacturing factory. The results demonstrate that the proposed approach can achieve satisfactory monitoring accuracy and adaptation ability. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the problem of power consumption monitoring of industrial equipment in harsh production environments. A conventional solution is to predict the power consumption using data-driven black-box models, with limited feasibility and interpretability. This paper proposes a new power consumption monitoring approach, utilizing an interpretable data-driven model and a networked predictive method. This approach accurately reveals the transparent relationships between the output and input parameters. The power prediction result is consequently interpretable and more reasonable. Meanwhile, this approach actively compensates for data dropouts in the network, which can help operators real-time grasp the power consumption of equipment. Furthermore, this approach can be integrated into energy optimization systems as a basis of optimization and decision-making. Practical applications in an aluminum manufacturing company located in Guangdong Province demonstrate that this approach is feasible and applicable. Future research is to expand this approach further for energy optimization and scheduling.

面向对海监视的舰船目标跟踪与航迹融合数据集

Real-time trajectory association and trajectory fusion in maritime surveillance pose great challenges and remain hot issues in security, regional situation monitoring, and long-range precision strikes for both military and civilian applications. High-quality datasets play a pivotal role in advancing research in target tracking and fusion technologies within this domain. This paper addresses the data requirements for technological research in target tracking and fusion, as well as the limitations of currently available datasets, including data scarcity, inadequate scene design specificity, uniform data formats, and incomplete data descriptions. We used simulation software to emulate multi-sensor multi-target detection data in complex scenarios, so as to present a dataset tailored for typical maritime surveillance scenarios—targeting ships using 2D radar and Electronic Support Measures (ESM) sensors. The simulation software comprises a scenario generator and a sensor simulator, creating a mature target tracking scenario simulation environment with realistic detection data modeling capabilities. The dataset includes data from 2D radar and ESM sensors, covering typical maritime ship categories, supporting configurations with radiation sources. It is designed for a variety of scenarios such as high-speed motion, dense traffic, multi-sensor data fusion, specific ship detection, and cross-positioning. The dataset contains a total of 368,155 target tracks from 101 ships, spanning a duration of 15,000 seconds. The data format conforms to actual equipment reporting scenarios, while the detection error model accurately reflects real-world conditions. Accuracy assessment and data completeness are ensured through various methods including normality testing of data error, scenario testing of detection rate and false alarm rate, as well as field research. This dataset can provide fundamental data for algorithmic research and validation in the ship's target tracking, trajectory fusion and other related areas.

Analysis of Cannabis and its Products in the Context Forensic Science

Legalization of low-tetrahydrocannabinol-high-cannabidiol cannabis plants is gaining momentum due to growing demand for these products. In some countries, the testing of cannabis and products containing it is a legal requirement: cannabis is identified, the amount of tetrahydrocannabinol or cannabidiol and their precursor acids is assessed. Current research requires more sensitive and reliable analytical methods for accurate identification and quantification of cannabis components. This article purpose is to review the latest advances in the scientific literature on the isolation and analysis of cannabis and cannabis-containing products in the context of forensic science. Research methods included analysis of information on this topic using search engines, including Google, Google Scholar, Web of Science, PubMed, and ScienceDirect. Search requests took into account scientific, analytical and statistical reports in the field of forensic research. Since the forensic expert must be well-versed in modern trends in the analysis of prohibited substances and use the latest data from the analytical and forensic literature in his work, such information will help them choose research methods, taking into account the actual resources and equipment of forensic laboratories. According to the article purpose, advantages and disadvantages of existing methods of extraction and analysis of objects containing cannabis for identification, quantification, profiling and age estimation of cannabis are considered.

A Novel Vision-Based Outline Extraction Method for Hull Components in Shipbuilding

The diverse nature of hull components in shipbuilding has created a demand for intelligent robots capable of performing various tasks without pre-teaching or template-based programming. Visual perception of a target’s outline is crucial for path planning in robotic edge grinding and other processes. Providing the target’s outline from point cloud or image data is essential for autonomous programming, requiring a high-performance algorithm to handle large amounts of data in real-time construction while preserving geometric details. The high computational cost of triangulation has hindered real-time industrial applications, prompting efforts to improve efficiency. To address this, a new improvement called Directive Searching has been proposed to enhance search efficiency by directing the search towards the target triangle cell and avoiding redundant searches. Another improvement, Heritable Initial, reduces the search amount by inheriting the start position from the last search. Combining Directive Searching and Heritable Initial into a new method called DSHI has led to a significant efficiency advancement, with a calculation efficiency improvement of nearly 300–3000 times compared to the ordinary Bowyer–Watson method. In terms of outlines extraction, DSHI has improved the extraction efficiency by 4–16 times compared to the ordinary Bowyer–Watson methods, while ensuring stable outlines results, and has also increased the extraction efficiency by 2–4 times compared to PCL. The DSHI method is also applied to actual ship component edge-grinding equipment, and its effect meets the shipbuilding process requirements. It could be inferred that the new method has potential applications in shipbuilding and other industries, offering satisfying efficiency and robustness for tasks such as automatic edge grinding.

Development of Zeolite Removal Robot for Decommissioning

The transfer of stagnant water from the basement floor of the waste treatment building at the Fukushima Daiichi Nuclear Power Station is planned because of the possibility of water flowing out of the building if another tsunami occurs. However, the basement floor is installed with high-dose zeolite sandbags as adsorbents for radioactive materials, which renders their removal challenging. In this study, we conduct a field survey and field reproduction as well as develop a robot that can perform zeolite removal based on the results obtained. In addition, we perform verification tests in a full-scale mock-up test environment and extract issues for application to actual equipment.