Search Results (203)

Metro carriages, as enclosed transport microenvironments, have been understudied regarding pollution characteristics and health risks from ACs, especially during high-temperature summers that amplify exposure. This study applied NTS techniques for the first time across three major Chengdu metro lines, systematically identifying sixteen ACs, including hazardous species such as acetophenone, benzonitrile, and benzoic acid that are often overlooked in conventional BTEX-focused monitoring. The TAC concentration reached 41.40 ± 5.20 µg/m³, with half of the compounds exhibiting significant increases during peak commuting periods. Source apportionment using diagnostic ratios and PMF identified five major contributors: carriage material emissions (36.62%), human sources (22.50%), traffic exhaust infiltration (16.67%), organic solvents (16.55%), and industrial emissions (7.66%). Although both non-cancer (HI) and cancer (TCR) risks for all population groups were below international thresholds, summer tourists experienced higher exposure than daily commuters. Notably, child tourists showed the greatest vulnerability, with a TCR of 5.83 × 10⁻⁷, far exceeding that of commuting children (1.88 × 10⁻⁷). Benzene was the dominant contributor, accounting for over 50% of HI and 70% of TCR. This study presents the first integrated NTS and quantitative risk assessment to characterise ACs in summer metro environments, revealing a broader range of hazardous compounds beyond BTEX. It quantifies population-specific risks, highlights children’s heightened vulnerability. The findings fill critical gaps in ACs exposure and provide a scientific basis for improved air quality management and pollution mitigation strategies in urban rail transit systems. Full article

Ensuring the structural safety of steel trusses in escalators is critical for the reliable operation of vertical transportation systems. While manual inspection remains widely used, its dependence on human judgment leads to extended cycle times and variable defect-recognition rates, making it less reliable for identifying subtle surface imperfections. To address these limitations, a novel context-aware, multi-scale deep learning framework based on the YOLOv5 architecture is proposed, which is specifically designed for automated structural defect detection in escalator steel trusses. Firstly, a method called GIES is proposed to synthesize pseudo-multi-channel representations from single-channel grayscale images, which enhances the network’s channel-wise representation and mitigates issues arising from image noise and defocused blur. To further improve detection performance, a context enhancement pipeline is developed, consisting of a local feature module (LFM) for capturing fine-grained surface details and a global context module (GCM) for modeling large-scale structural deformations. In addition, a multi-scale feature fusion module (MSFM) is employed to effectively integrate spatial features across various resolutions, enabling the detection of defects with diverse sizes and complexities. Comprehensive testing on the NEU-DET and GC10-DET datasets reveals that the proposed method achieves 79.8% mAP on NEU-DET and 68.1% mAP on GC10-DET, outperforming the baseline YOLOv5s by 8.0% and 2.7%, respectively. Although challenges remain in identifying extremely fine defects such as crazing, the proposed approach offers improved accuracy while maintaining real-time inference speed. These results indicate the potential of the method for intelligent visual inspection in structural health monitoring and industrial safety applications. Full article

Aiming at the problem of a full-width horizontal-axis roadheader being prone to diverge from the preset trajectory of the tunnel, a deviation correction control method based on particle swarm optimization–backpropagation (PSO-BP) neural network proportional–integral–derivative (PID) control is proposed. The track error model of the walking system and the transfer function model of the deviation correction control are established. The PSO-BP PID controller is designed; the beginning weights of BP are enhanced by the PSO, and the BP receives the optimal weights to instinctively adapt the PID parameters. An experiment on deviation correction control of the roadheader was carried out. The experimental results indicate that the maximum steady-state error of PSO-BP PID for deflection angle and angular velocity is reduced by 41.03% and 44.93%, respectively, compared with BP PID, and the average rise time for deflection angle and angular velocity is reduced by 75.76%. Full article

In high-voltage transmission lines, insulators subjected to prolonged electromechanical stress are prone to zero-value defects, leading to insulation failure and posing significant risks to power grid reliability. The conventional detection method of spark gap is vulnerable to environmental interference, while the emerging electric field distribution-based techniques require complex instrumentation, limiting its applications in scenes of complex structures and atop tower climbing. To address these challenges, this study proposes an electroluminescent sensing strategy for zero-value insulator identification based on the electroluminescence of ZnS:Cu. Based on the stimulation of electrical stress, real-time monitoring of the health status of insulators was achieved by applying the composite of epoxy and ZnS:Cu onto the connection area between the insulator steel cap and the shed. Experimental results demonstrate that healthy insulators exhibit characteristic luminescence, whereas zero-value insulators show no luminescence due to a reduced drop in electrical potential. Compared with conventional detection methods requiring access of electric signals, such non-contact optical detection method offers high fault-recognition accuracy and real-time response capability within milliseconds. This work establishes a novel intelligent sensing paradigm for visualized condition monitoring of electrical equipment, demonstrating significant potential for fault diagnosis in advanced power systems. Full article

In recent years, distributed energy power supply systems have been widely used in remote areas and extreme environments. However, the intermittent and uncertain output power may cause power grid fluctuations, leading to higher harmonics in electromechanical equipment, especially motors. For permanent magnet synchronous motor (PMSM) systems, an electromagnetic (EM) vibration can cause problems such as energy loss and mechanical wear. Therefore, it is necessary to design control algorithms that can effectively suppress EM vibration. To this end, a vibration suppression algorithm for fractional-slot permanent magnet synchronous motors based on a d-axis current injection is proposed in this paper. Firstly, this paper analyzes the radial electromagnetic force of the fractional-slot PMSM to identify the main source of EM vibration in fractional-slot PMSMs. Based on this, the intrinsic relationship between the EM vibration of fractional-slot PMSMs and the d-axis and q-axis currents is explored, and a method for calculating the d-axis current to suppress the vibration is proposed. Experimental verification shows that the proposed algorithm can effectively suppress EM vibration. Full article

This study applies deep learning-based object detection technology to defect detection in weld radiographs, proposing a technical solution for accurately identifying the types and locations of defects in weld X-ray radiographs. The research encompasses the construction of a defect dataset, the design of a multi-model object detection network, and the development of an automated film evaluation algorithm. This technology significantly enhances the efficiency and accuracy of detecting and identifying harmful defects on weld radiographs, providing critical technical support for ensuring the safe operation and efficient maintenance of pipelines of pressure equipment. Full article

Fully mechanized mining involves high energy consumption, particularly during cutting and transportation. Scraper conveyors, crucial for coal transport, face energy efficiency challenges due to the lack of accurate dynamic coal flow models, which restricts precise energy estimation and optimization. This study constructs dynamic coal flow and scraper conveyor energy efficiency models to analyze the impact of multiple variables on energy consumption and lump coal rate. A dynamic coal flow model is developed through theoretical derivation and EDEM simulations, validated for parameter settings, boundary conditions, and numerical methods. The multi-objective optimization model for energy consumption is solved using the NSGA-II-ARSBX algorithm, yielding a 33.7% reduction in energy consumption, while the lump coal area is reduced by 27.7%, indicating a trade-off between energy efficiency and coal fragmentation. The analysis shows that increasing traction speed while decreasing scraper chain and drum speeds effectively lowers energy consumption. Conversely, simultaneously increasing both chain and drum speeds helps to maintain lump coal size. The final optimization scheme demonstrates this balance—achieving improved energy efficiency at the cost of increased coal fragmentation. Additional results reveal that decreasing traction speed while increasing chain and drum speeds results in higher energy consumption, while increasing traction speed and reducing chain/drum speeds minimizes energy use but may negatively affect lump coal integrity. Full article

This paper proposes a model identification method based on the auxiliary variable closed-loop subspace identification algorithm to address the problem of modeling difficulties caused by various complex factors affecting permanent magnet brushless DC motors in practical working conditions. This method breaks through the limitations caused by the correlation between input signals and noise in traditional subspace identification algorithms. By introducing auxiliary variables, it effectively avoids the projection process, simplifies the complex calculations of principal component analysis, and improves the practicality and efficiency of the algorithm. When constructing a data-driven identification model, the actual situation of measurement data being contaminated by noise has to be fully considered. Orthogonal compensation matrices and auxiliary variables were used to construct uncorrelated terms for noise, thereby eliminating the negative impact of noise on the model’s identification accuracy. The effectiveness of the proposed identification algorithm was verified by collecting data through a chassis dynamometer simulation test of a vehicle-mounted permanent magnet brushless DC motor. The results show that compared with the traditional N4SID algorithm, the proposed closed-loop subspace identification algorithm based on auxiliary variable principal component analysis exhibits higher model identification accuracy, stronger anti-interference ability, and better stability in both noise-free and noise-contaminated conditions, providing a more reliable model basis for motor performance evaluation and control strategy design. Full article

Gas sensors assume a crucial role in the medical domain, offering substantial support for disease diagnosis, treatment, medical environment management, and the operation of medical equipment by virtue of their distinctive gas detection capabilities. This paper presents an overview of the key research and development orientations for gas sensors, encompassing the exploration and optimization of novel sensitive materials, such as nanomaterials and metal oxides, to augment sensor sensitivity, selectivity, and stability. The innovation in sensor structural design, particularly the integration of micro-electromechanical systems (MEMS) technology to attain miniaturization and integration, is also addressed. The applications of gas sensors in health safety are expounded, covering the real-time monitoring of indoor air quality for harmful gases such as formaldehyde, as well as the detection of toxic gases in industrial environments to guarantee the safety of living and working spaces and prevent occupational health hazards. In the sphere of medical detection and diagnosis, this paper focuses on the detection of biomarkers in human exhaled breath by gas sensors, which facilitates the early diagnosis of diseases such as lung cancer. Additionally, the existing challenges and future development trends in this field are analyzed, with the aim of providing a comprehensive reference for the in-depth research and extensive application of gas sensors in the health, safety, and medical fields. Full article

The increasing complexity of highway electromechanical systems has created a critical need to improve the accuracy of resource consumption standards. Traditional deterministic methods often fail to capture inherent variability in resource usage, resulting in significant discrepancies between budget estimates and actual costs. To address this issue for a specific device, this study develops a probabilistic framework based on Monte Carlo simulation, using manual barrier gate installation as a case study. First, probability distribution models for key parameters were established by collecting and statistically analyzing field data. Next, Monte Carlo simulation generated 100,000 pseudo-observations, yielding mean labor consumption of 1.08 workdays (SD 0.29), expansion bolt usage of 6.02 sets (SD 0.97), and equipment shifts of 0.20 (SD 0.10). Comparison with the “Highway Engineering Budget Standards” (JTG/T 3832-2018) revealed deviations of 1% to 4%, and comparison with market bid prices showed errors below 2%. These results demonstrate that the proposed method accurately captures dynamic fluctuations in resource consumption, aligning with both national norms and actual tender data. In conclusion, the framework offers a robust and adaptable tool for cost estimation and resource allocation in highway electromechanical projects, enhancing budgeting accuracy and reducing the risk of cost overruns. Full article

The intelligent maintenance of coal mining equipment is crucial for ensuring safe production in coal mines. Despite the rapid development of large language models (LLMs) injecting new momentum into the intelligent transformation and upgrading of coal mining, their application in coal mining equipment maintenance still faces challenges due to the diversity and technical complexity of the equipment. To address the scarcity of domain knowledge and poor model adaptability in multi-task scenarios within the coal mining equipment maintenance field, a method for constructing a large language model based on multi-dimensional prompt learning and improved LoRA (MPL-LoRA) is proposed. This method leverages multi-dimensional prompt learning to guide LLMs in generating high-quality multi-task datasets for coal mining equipment maintenance, ensuring dataset quality while improving construction efficiency. Additionally, a fine-tuning approach based on the joint optimization of a mixture of experts (MoE) and low-rank adaptation (LoRA) is introduced, which employs multiple expert networks and task-driven gating functions to achieve the precise modeling of different maintenance tasks. Experimental results demonstrate that the self-constructed dataset achieves fluency and professionalism comparable to manually annotated data. Compared to the base LLM, the proposed method shows significant performance improvements across all maintenance tasks, offering a novel solution for intelligent coal mining maintenance. Full article

The application of hydraulic systems in aerospace and engineering machines is becoming widespread. With the use of electro-hydraulic actuators, designing efficient and intelligent controllers can help the rapid expansion of electromechanical equipment in various scenarios. In response to the difficulty of slow response in the EHA control process, the paper designs an error prediction algorithm to predict the system response curve and replace the real-time error of PID input, achieving advanced correction of the controller. The experiment shows that the proposed method has a lower response time and smoother control curve while ensuring accuracy. It might have potential value in improving hydraulic system efficiency, reducing switching shock, and increasing system service life. Full article

Watermelon is a widely cultivated fruit and vegetable that is native to Africa and has become one of the world’s important summer fruits. Watermelon seed vigor has a critical impact on watermelon planting and yield, and seed water content is a key factor in maintaining vigor during seed storage and germination. In this study, reflectance and transmittance spectral data from hyperspectral imaging were fused to improve the detection accuracy of moisture content in watermelon seeds. First, watermelon seed samples with different water content gradients were prepared by dividing all 456 selected watermelon seeds into 10 groups and drying them in a drying oven at 60 °C for 0, 3, 5, 10, 15, 20, 25, 30, 40, and 50 min. Reflectance and transmission spectra of 456 watermelon seeds were collected by a hyperspectral imaging system, and the single spectral data were subsequently used to build PLSR and LSSVR models for quantitative analysis of watermelon seed moisture content. Model performance is enhanced by Competitive Adaptive Reweighted Sampling (CARS), Unrelated Variable Elimination (UVE), and primary and intermediate data fusion methods. Primary data fusion improves model predictions compared to single models based on reflectance and transmission spectra. The intermediate data fusion of the feature spectral data of reflectance and transmittance selected by the CARS algorithm improves the prediction effect of the model more obviously, in which the model with the best prediction accuracy is Raw-CRAS-LSSVR, whose $R_P^2$ and RMSEP are 0.9149 and 0.0144, respectively, which improves the prediction effect of the model built by a single full-spectrum datum by 5.72%. This study demonstrates that hyperspectral reflectance and transmission imaging techniques combined with data fusion can effectively detect watermelon seed moisture content quickly and with high accuracy. Full article

Active magnetic bearings (AMBs) are a class of electromechanical equipment that effectively integrate Magnetic Bearing technology with PMSM technology, particularly for applications involving high-power and high-speed permanent magnet motors. However, as the rotor operates in a suspended state, the motor’s trajectory changes continuously. The installation of a speed sensor poses a risk of collisions with the shaft, which inevitably leads to rotor damage due to imbalance, shaft wear, or other mechanical effects. Consequently, for the rotor control system of PMSM, it is crucial to adopt a sensorless speed estimation method to achieve high-performance speed and position closed-loop control. This study uses the rotor system of a 75 kW AMB high-speed motor as a case study to provide a detailed analysis of the principles of high-frequency square wave signal injection (HFSWSII) and current signal injection for speed estimation. The high-frequency current response signal is derived, and a speed observer is designed based on signal extraction and processing methods. Subsequently, a speed estimation model for PMSM is constructed based on HFSWSII, and the issue of “filter bandwidth limitations and lagging effects in signal processing” within the observer is analyzed. A scheme based on the high-frequency pulse array current injection method is then proposed to enhance the observer’s performance. Finally, to assess the system’s anti-interference capability as well as the motor’s static and dynamic tracking performance, its dynamic behavior is tested under conditions of increasing and decreasing speed and load. Simulation and experimental results demonstrate that the PMSM control system based on HFSWSII achieves accurate speed estimation and shows excellent static and dynamic performance. Full article

In the application of digital twin technology for the heading workface in coal mining, real-time state data will be transmitted to the remote control platform through a gateway device. This cross-system and cross-software data transmission method inevitably introduces transmission delays, resulting in a certain spatiotemporal discrepancy in the virtual model control for the remote control of the physical equipment. In this paper, by analyzing the operational process of the cantilever roadheader, a state evolution dynamics model construction method for the cantilever roadheader is proposed, which includes three stages, the discretization of the operating state based on the cutting path, event-driven graph construction of the cutting state evolution, and real-time data-driven dynamics evolution, so to continuously monitor, analyze, and adjust the operational dynamics of the cantilever roadheader based on real-time state data, thus improving the efficiency, performance, and adaptability. The construction of the model provides a theoretical basis and technical support for the construction and alignment of the digital twin multidimensional model of the cantilever roadheader. Full article