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This study investigates the techno-economic feasibility of utilizing hydrogen as a substitute for natural gas in an industrial gas turbine power plant, using the GE Frame 9E unit of the Ibom Power Station, Nigeria, as a case study. Engine performance simulations were conducted using GasTurb 11 software to evaluate key performance parameters under natural gas and hydrogen fueling conditions. A discounted cash flow-based economic model was developed to assess the financial viability of both fuel options over a 20-year plant lifecycle, employing indicators such as Net Present Value (NPV) and Internal Rate of Return (IRR). Results indicate that hydrogen operation yields a marginally higher thermal efficiency and lower heat rate compared to natural gas due to hydrogen’s higher heating value. However, economic analysis reveals that, despite performance improvements, hydrogen-fueled operation demonstrates not to be economically viable under current cost assumptions. The natural gas–fired engine achieved a NPV of approximately $99.5 million and an IRR of 17.4 %, whereas the hydrogen-fired engine produced a negative NPV of approximately –$358.1 million, largely driven by the high cost of hydrogen fuel. The findings demonstrate that while hydrogen offers environmental and performance benefits, significant reductions in hydrogen fuel costs or strong policy incentives are required before it can compete economically with natural gas for industrial gas turbine power generation.

This study systematically examines deep learning-based rotoscoping systems along the axes of video object and instance segmentation, memory-based models, optical flow integration, foundation and transformer-based video approaches, as well as matting and production-oriented evaluation metrics. The aim is to classify contemporary rotoscoping methods within a holistic framework, reveal their strengths and limitations under production conditions, and propose a production-oriented evaluation perspective for future research. Methodologically, prominent post 2015 approaches, including VOS/VSS models, memory-based and optical flow-based methods, prompt-driven foundation segmentation models, and transformer-based video systems, are analyzed comparatively with respect to datasets, training strategies, evaluation metrics, and application scenarios. The findings indicate that rotoscoping has evolved from frame-by-frame manual tools toward human-supervised hybrid automation systems based on memory-augmented video segmentation, optical flow-assisted propagation, prompt-based foundation models, and transformer-based video approaches. However, domain gap issues, computational costs in high-resolution sequences, limitations in fine-detail preservation and matting consistency, long-term temporal stability, and the lack of production-specific evaluation metrics remain significant challenges, rendering fully automated rotoscoping an unresolved problem under real-world production conditions. The study suggests that rotoscoping workflows will become highly automated in the near future, yet quality assurance and creative decision-making will continue to rely on human experts within human-in-the-loop hybrid architectures. Accordingly, future research should prioritize standardized evaluation protocols, methods tailored to high-resolution and long-duration video sequences, and hybrid system designs.

With the decreasing of easy-to-exploit oil and gas resources, complex structure wells put forward higher requirements for wellbore trajectory control accuracy. As the core supporting part of the guiding tool, the radial displacement of the combined bearing directly affects the deflection accuracy of the spindle. In order to reveal the internal correlation mechanism between the radial displacement of the combined bearing and the accuracy of the spindle deflection control, this paper combines the statically indeterminate beam theory and the parametric finite element method to establish a spindle deflection prediction model considering multi-factor coupling. The influence of radial load, rotational speed and inner and outer ring eccentricity on the radial displacement of the combined bearing is systematically studied. The results show that the radial displacement of the combined bearing increases significantly with the increase of radial load, rotational speed and eccentricity. Especially under heavy load conditions, the change of the contact logarithm of the rollers inside the bearing leads to a nonlinear turning of the stiffness characteristics, showing obvious adaptive bearing characteristics. The prototype experiment shows that the variation trend of the simulation and the measured data is highly consistent under the condition of 30 r/min, and the error is controlled within 10 %-15 %, which verifies the accuracy of the model. This study not only quantifies the influence of key parameters on bearing displacement, but also provides a theoretical basis and parameter selection criteria for structural optimization design and accuracy improvement of rotary steering drilling tool combined bearings.

As a critical component of the automotive powertrain system, the performance status of electric motors directly impacts vehicle operational safety. During vehicle operation, sensor signals are frequently subject to noise interference, while motors typically operate under varying conditions, posing significant challenges for fault diagnosis. To address these issues, this paper proposes a cross-domain fault diagnosis method based on attention-guided graph convolutional networks, effectively countering interference from variable operating conditions. First, to tackle noise in extracted signal data, time-frequency domain feature fusion is employed to capture signal characteristics from both temporal and spectral perspectives, comprehensively extracting useful information. Subsequently, a wavelet kernel convolution layer is introduced, leveraging the multi-resolution properties of wavelet transforms to enhance feature extraction capabilities. Second, a graph-generation structure based on attention mechanisms is employed. This structure combines autoregressive moving average filter graph convolutions with multi-order graph convolutions based on Chebyshev polynomials to achieve further feature extraction and capture multi-scale information. Finally, the advancement of this method has been verified through a series of experimental cases, which demonstrates superior performance compared to other models across various cross-domain tasks.

Shaft vibration (displacement signal) and bearing vibration (velocity signal) are key indicators for evaluating the dynamic characteristics of the rotor and supporting bearing system, and they play a crucial role in the operational performance and safety of equipment. However, in practical applications, collecting shaft vibration or bearing vibration signals often encounters multiple challenges, primarily attributed to limitations in measurement technology, interference from faults, and variations in operating environments. In-depth investigation into the intrinsic correlation between shaft vibration and bearing vibration not only enables data supplementation to improve information completeness, but also offers more precise references for fault diagnosis and condition monitoring. Therefore, this study proposes a method based on homologous information fusion, aiming to explore the intrinsic correlation between shaft vibration and bearing vibration under rub-impact faults. The study first constructs a dynamic model under rub-impact fault condition, and then fuses homologous information using full vector spectrum technology to improve the accuracy of determining the relationship between shaft vibration and bearing vibration at different rotational speeds. Finally, the reliability of simulation results is validated through the establishment of a rotor experimental rig. Experimental results reveal that by mastering this complementary relationship, the operating health status of equipment can be inferred based on the variation tendencies of other critical parameters – even when a specific measured signal is unavailable – and corresponding maintenance and management strategies can thus be formulated.

To address the critical challenge of low diagnostic accuracy in multistate bearing fault diagnosis caused by inefficient discriminative feature extraction under varying operating conditions, this paper proposes a novel Parameter-weighted Viridis Image Encoding (PVIE) method. Unlike conventional image encoding techniques (e.g., GADF, GASF, MTF, RP) that often suffer from high computational complexity and limited feature separability in complex scenarios, PVIE integrates Variational Mode Decomposition (VMD) with a newly designed Parameter-weighted Euler Difference Feature Extraction (PWEDFE) module. This module explicitly enhances the nonlinearity and periodicity of fault signatures, mapping them into lightweight 2D feature images via Viridis Feature Value Mapping (VFVM). Extensive experiments on two benchmark datasets demonstrate that PVIE achieves exceptional diagnostic accuracies of 99.92 % and 99.74 %, respectively. Compared to state-of-the-art encoding methods, PVIE improves average accuracy by 21.06 % to 39.78 % while reducing diagnostic time by 53.3 %, significantly outperforming existing approaches in both accuracy and efficiency. Furthermore, the method exhibits robust performance under strong noise interference and small-sample scenarios. These results confirm that PVIE offers a substantial advancement over current research by providing a more discriminative, lightweight, and robust solution for real-time industrial fault diagnosis.

Determining the implement in use during agricultural tractor operations from onboard data alone represents a practical challenge in field equipment utilization monitoring. This study investigates whether standard tractor CAN-Bus signals are sufficient to automatically identify the active implement without additional sensing hardware or manual operator input. Field tests were conducted with a 105 HP agricultural tractor performing three distinct operations ploughing, rotary tilling, and beet harvesting under real field conditions. A dataset was recorded at 10 Hz via an IoT-based edge-to-cloud telemetry system across five SAE J1939 parameters: wheel-based vehicle speed, engine torque percentage, hitch position, traction load, and engine speed. Random Forest and XGBoost classifiers were trained on the collected field data, and both achieved perfect classification performance on stratified hold-out test data. SHAP-based sensitivity analysis was subsequently applied to quantify the contribution of each parameter to the classification decisions and to validate the physical interpretability of the learned models. Class-level analysis further revealed that each operation is governed by a distinct feature hierarchy: speed is the primary discriminator for ploughing, torque dominates rotary tillage identification, and beet harvesting exhibits a distributed multi-parametric signature. These results demonstrate that routine CAN-Bus field data contains sufficient information to reliably predict equipment utilization, offering a scalable and infrastructure-free approach to implement identification in agricultural machinery.

As a localized high-intensity downdraft disaster, downbursts are a significant cause of wind-rain-induced damage to transmission lines. Their unique wind field characteristics make it difficult for existing design methods to comprehensively evaluate the resistance capacity of transmission lines. Current collapse analyses of transmission lines often fail to adequately consider the wind-rain field conditions during downbursts. Therefore, this study investigates the dynamic response and collapse mechanisms of transmission lines under downburst wind-rain conditions. First, a numerical simulation model is established to explore the distribution characteristics of wind and rain. A full-scale three-dimensional computational domain model is employed to simulate the wind-rain field, which is subsequently modified. The wind-rain velocity ratio is analyzed, and a fitting formula is proposed. Subsequently, combined distributed loads are applied to the transmission line to conduct parametric analyses of the dynamic response and investigate the collapse mechanisms. The results demonstrate that the computational domain model for simulating the wind-rain field is validated using relevant models, and the Vicroy model is modified for generating wind-rain loads. The horizontal velocity of raindrops does not synchronize with wind speed variations, and the proposed fitting formula for the wind-rain velocity ratio exhibits high accuracy. Downbursts significantly influence the dynamic response of the transmission tower-line system, with the most unfavorable wind attack angles, heights, rainfall intensities, and combined conditions identified. The collapse failure mode of the transmission line is characterized by initial damage and failure of diagonal members, leading to extensive structural collapse. The critical segments vary under different wind attack angles and reference heights, while rainfall has a minor impact on the collapse process. This study provides important technical insights for the wind-resistant design and safe operation of transmission lines.