Search Results (386)

The electrification of agricultural equipment is a critical pathway to address the dual challenges of increasing global food production and ensuring sustainable agricultural development. As the core power unit, the permanent magnet synchronous motor (PMSM) drive system faces severe challenges in achieving high performance, robustness, and reliable control in complex farmland environments characterized by sudden load changes, extreme operating conditions, and strong interference. This paper provides a comprehensive review of key technological advancements in PMSM drive systems for agricultural electrification. First, it analyzes solutions to enhance the reliability of power converters, including high-frequency silicon carbide (SiC)/gallium nitride (GaN) power device packaging, thermal management, and electromagnetic compatibility (EMC) design. Second, it systematically elaborates on high-performance motor control algorithms such as Direct Torque Control (DTC) and Model Predictive Control (MPC) for improving dynamic response; robust control strategies like Sliding Mode Control (SMC) and Active Disturbance Rejection Control (ADRC) for enhancing resilience; and the latest progress in fault-tolerant control architectures incorporating sensorless technology. Furthermore, the paper identifies core challenges in large-scale applications, including environmental adaptability, real-time multi-machine coordination, and high reliability requirements. Innovatively, this review proposes a closed-loop intelligent control paradigm encompassing environmental disturbance prediction, control parameter self-tuning, and actuator dynamic response. This paradigm provides theoretical support for enhancing the autonomous adaptability and operational quality of agricultural machinery in unstructured environments. Finally, future trends involving deep AI integration, collaborative hardware innovation, and agricultural ecosystem construction are outlined. Full article

To improve the reliability and fault tolerance of electric helicopter propulsion systems, this paper presents a master–slave fault-tolerant control method based on an extended state observer (ESO) for dual-redundant electric propulsion systems that addresses dynamic coupling disturbances. First, the control architecture puts the master motor in speed loop mode and puts the slave motor in torque loop mode with an ESO to estimate disturbances and compensate for mechanical coupling torque through feedforward control based on Lyapunov stability theory. Second, a least squares parameter identification method establishes a current-torque mapping model to ensure consistent dual-motor output. Then, fault-tolerant switching is implemented, transitioning from normal torque mode coordination to independent speed mode with adaptive PI adjustment during faults. Experimental validation shows that the total torque stabilizes at 240 N·m, and the synchronization error remains within ±0.5 N·m during normal operation. Under single-motor fault scenarios, the ESO detects disturbances within 15 ms with >95% accuracy. The system speed decreases to a minimum of 2280 rpm (5% deviation) and recovers within 3.5 s. Compared to traditional PI control, this method improves torque synchronization by 65.4%, speed stability by 62.6%, and dynamic response by 51.2%. Finally, the results validate that the method effectively suppresses coupling interference and meets aviation safety standards, providing reliable, fault-tolerant solutions for electric helicopter propulsion. Full article

Battery energy storage systems (BESSs) have become integral components of grid modernization because of their ability to provide system stabilization in the presence of high levels of renewable generation. Specifically, the dynamic response capabilities of BESSs can be a valuable tool in ensuring reliability and security of the grid during contingencies. This paper explores the utilization of BESSs in improving the contingency response of the SRP power system by providing selection criteria that enable a viable and cost-effective solution from a planning perspective. In particular, this study focuses on optimal BESS selection from a list of actual queued projects to enhance system stability by maintaining voltage and mitigating fault impacts. Additionally, the work involves generating both normal and abnormal operational scenarios for varying loads and renewable generation profiles of the system to capture diverse sources of uncertainty. A comprehensive reliability planning approach is adopted to identify the worst-case scenarios and ensure network robustness by optimizing BESS operations under these conditions. The results obtained by applying the proposed methodology to a 2500+-bus real-world system of SRP indicates that with as few as four strategically selected BESS units, the system is able to effectively mitigate more than 90% of under-voltage violations and approximately 75% of over-voltage violations. Full article

China’s high-speed railway (HSR) network relies heavily on bridge structures to ensure track regularity, with many lines crossing seismically active near-fault zones. Near-fault ground motions are characterized by significant vertical components (VGMs), which challenge conventional seismic design practices. Although seismic isolation techniques are widely adopted, the effects of VGMs on the dynamic response and damage mechanisms of HSR track–bridge systems remain insufficiently studied. To address this gap, this study develops a refined finite element model (FEM) in OpenSEES that integrates CRTS II slab ballastless tracks, bridge structures, and friction pendulum bearing (FPB). Using nonlinear time-history analyses, the research systematically investigates structural responses and damage degrees under different ratios of vertical-to-horizontal peak ground acceleration (α_VH) and multiple seismic intensity levels (frequent, design, and rare earthquakes). Key findings reveal that α_VH values in near-fault regions frequently range between 0.5 and 1.5, often exceeding current design code specifications. The impact of VGMs intensifies with seismic intensity: negligible under frequent earthquakes but significantly amplifying damage to piers, bearings, and track interlayer components (e.g., sliding layers and CA mortar layers) during design and rare earthquakes. While seismic isolation effectively mitigates structural responses through energy dissipation by bearings, it may increase sliding layer displacements and lead to bearing failure under rare earthquakes. Based on these insights, tiered α_VH values are recommended for seismic design: 0.65 for frequent, 0.9 for design, and 1.2 for rare earthquakes. These findings provide critical references for the seismic design of HSR infrastructure in near-fault regions. Full article

Induction motor (IM) drives are widely used in industrial applications, particularly within the renewable energy sector, owing to their fast dynamic response and robust performance. Reliable condition monitoring is essential to ensure uninterrupted operation, minimize unexpected downtime, and avoid associated financial losses. Although numerous studies have introduced advanced fault detection techniques for IMs, early fault identification remains a significant challenge, especially in systems powered by electronic drives. To address the limitations of manual feature extraction, deep learning methods, particularly conventional convolutional neural networks (CNNs), have emerged as promising tools for automated fault diagnosis. However, enhancing their capability to capture a broader spectrum of spatial features can further improve detection accuracy. This study presents a novel fault detection framework based on a multi-wide-kernel convolutional neural network (MWK-CNN) tailored for drive-fed induction motors. By integrating convolutional kernels of varying widths, the proposed architecture effectively captures both fine-grained details and large-scale patterns in the input signals, thereby enhancing its ability to distinguish between normal and faulty operating states. Electrical signals acquired from drive-fed IMs under diverse operating conditions were used to train and evaluate the MWK-CNN. Experimental results demonstrate that the proposed model exhibits heightened sensitivity to subtle fault signatures, leading to superior diagnostic accuracy and outperforming existing state-of-the-art approaches for fault detection in drive-fed IM systems. Full article

The integration of the industrial Internet and big data analytics is reshaping enterprise decision-making models and providing new momentum for the transformation and upgrading of traditional manufacturing industries. In this study, a decision support system based on multi-source heterogeneous data fusion was established. The system carries out data collection, storage, and processing, as well as visualization analysis. The system also performs time-series data feature extraction and unstructured data processing in a three-layer architecture model to train models and generate decision-making. In case studies, the effectiveness of the system in predictive maintenance of equipment, dynamic optimization of supply chains, and product quality traceability was verified. A fault prediction model was developed based on an improved random forest algorithm, and it showed a high level of accuracy. Optimization strategies, such as modular system design, dynamic knowledge base updating, and human–machine collaborative decision-making, can be formulated using the system. To evaluate the system, a three-dimensional evaluation index system was built, including technology maturity, application adaptability, and benefit–output ratio. The developed system effectively improved the efficiency of enterprise resource allocation, shortened abnormality response times, and enhanced market adaptability. By using edge computing and digital twin technologies, a more flexible distributed decision-making architecture can be created in the system, promoting data-driven and intelligent decision-making in manufacturing industry. Full article

A dual-loop fault-tolerant control scheme is investigated for UAV attitude control systems subject to actuator faults, input saturation, and external disturbances in this paper. In the outer loop of attitude angles, a nonlinear dynamic inversion controller is developed as baseline controller for fast response and is augmented by a neural network disturbance observer to enhance the adaptability and robustness. Considering input saturation, actuator faults, and external disturbances in the inner loop of attitude angle velocities, the unbalanced input saturation is first converted into a time-varying system with unknown parameters and disturbances using a nonlinear function approximation method. An L1 adaptive fault-tolerant controller is then introduced to compensate for the effects of lumped uncertainties including system uncertainties, actuator faults, external disturbances, and approximation errors, and the stability and performance boundaries are verified by Lyapunov theorem and L1 reference system. Some simulation examples are carried out to demonstrate its effectiveness. Full article

Event-based cameras, as a revolutionary class of dynamic vision sensors, offer transformative advantages for capturing transient mechanical phenomena through their asynchronous, per-pixel brightness change detection mechanism. These neuromorphic sensors excel in challenging industrial environments with their microsecond-level temporal resolution, ultra-low power requirements, and exceptional dynamic range that significantly outperform conventional imaging systems. In this way, the event-based camera provides a promising tool for machine vibration sensing and fault diagnosis. However, the dynamic vision data from the event-based cameras have a complex structure, which cannot be directly processed by the mainstream methods. This paper proposes a dynamic vision-based non-contact machine fault diagnosis method. The Eagle Vision Transformer (EViT) architecture is proposed, which incorporates biologically plausible computational mechanisms through its innovative Bi-Fovea Self-Attention and Bi-Fovea Feedforward Network designs. The proposed method introduces an original computational framework that effectively processes asynchronous event streams while preserving their inherent temporal precision and dynamic response characteristics. The proposed methodology demonstrates exceptional fault diagnosis performance across diverse operational scenarios through its unique combination of multi-scale spatiotemporal feature analysis, adaptive learning capabilities, and transparent decision pathways. The effectiveness of the proposed method is extensively validated by the practical condition monitoring data of rotating machines. By successfully bridging cutting-edge bio-inspired vision processing with practical industrial monitoring requirements, this work creates a new paradigm for dynamic vision-based non-contact machinery fault diagnosis that addresses critical limitations of conventional approaches. The proposed method provides new insights for predictive maintenance applications in smart manufacturing environments. Full article

With the development of artificial intelligence technology, intelligent fault diagnosis methods based on deep learning have received extensive attention. Among them, convolutional neural network (CNN) has been widely applied in the fault diagnosis of rolling bearings due to its strong feature extraction ability. However, traditional CNN models still have deficiencies in the extraction of early weak fault features and the suppression of high noise. In response to these problems, this paper proposes a convolutional neural network (SAWCA-net) that integrates spectrum-guided dynamic variable-width convolutional kernels and dynamic interactive time-domain–channel attention mechanisms. In this model, the spectrum-adaptive wide convolution is introduced. Combined with the time-domain and frequency-domain statistical characteristics of the input signal, the receptive field of the convolution kernel is adaptively adjusted, and the sampling position is dynamically adjusted, thereby enhancing the model’s modeling ability for periodic weak faults in complex non-stationary vibration signals and improving its anti-noise performance. Meanwhile, the dynamic time–channel attention module was designed to achieve the collaborative modeling of the time-domain periodic structure and the feature dependency between channels, improve the feature utilization efficiency, and suppress redundant interference. The experimental results show that the fault diagnosis accuracy rates of SAWCA-Net on the bearing datasets of Case Western Reserve University (CWRU) and Xi’an Jiaotong University (XJTU-SY) reach 99.15% and 99.64%, respectively, which are superior to the comparison models and have strong generalization and robustness. The visualization results of t-distributed random neighbor embedding (t-SNE) further verified its good feature separability and classification ability. Full article

This paper proposes optimal sliding mode fault-tolerant control for multiple robotic manipulators in the presence of external disturbances and actuator faults. First, a quantitative prescribed performance control (QPPC) strategy is constructed, which relaxes the constraints on initial conditions while strictly restricting the trajectory within a preset range. Second, based on QPPC, adaptive gain integral terminal sliding mode control (AGITSMC) is designed to enhance the anti-interference capability of robotic manipulators in complex environments. Third, a critic-only neural network optimal dynamic programming (CNNODP) strategy is proposed to learn the optimal value function and control policy. This strategy fits nonlinearities solely through critic networks and uses residuals and historical samples from reinforcement learning to drive neural network updates, achieving optimal control with lower computational costs. Finally, the boundedness and stability of the system are proven via the Lyapunov stability theorem. Compared with existing sliding mode control methods, the proposed method reduces the maximum position error by up to 25% and the peak control torque by up to 16.5%, effectively improving the dynamic response accuracy and energy efficiency of the system. Full article

The substation grounding grid, as the primary path for fault current dissipation, is crucial for ensuring the safe operation of the power system and requires regular inspection. The pulsed eddy current method, known for its non-destructive and efficient features, is widely used in grounding grid detection. However, during the parameter identification process, it is prone to local minima or no solution. To address this issue, this paper first develops a pulsed eddy current forward response model for the substation grounding grid based on the magnetic dipole superposition principle, with accuracy validation. Then, a variable dimensional Bayesian parameter identification method is introduced, utilizing the Reversible-Jump Markov Chain Monte Carlo (RJMCMC) algorithm. By using nonlinear optimization results as the initial model and introducing a dual-factor control strategy to dynamically adjust the sampling step size, the model enhances coverage of high-probability regions, enabling effective estimation of grounding grid parameter uncertainties. Finally, the proposed method is validated by comparing the forward response model with field test results, showing that the error is within 10%, demonstrating both the accuracy and practical applicability of the proposed parameter identification method. Full article

This study investigates the formation mechanism and stress response characteristics of normal faults in coal-bearing strata through large-scale physical simulation experiments. A multi-layer heterogeneous model with a geometric similarity ratio of 1:300 was constructed using similar materials that were tailored to match the mechanical properties of real strata. Real-time monitoring techniques, including fiber Bragg grating strain sensors and a DH3816 static strain system, were employed to record the evolution of deformation, strain, and displacement fields during the fault development. The results show that the normal fault formation process includes five distinct stages: initial compaction, fault initiation, crack propagation, fault slip, and structural stabilization. Quantitatively, the vertical displacement of the hanging wall reached up to 5.6 cm, equivalent to a prototype value of 16.8 m, and peak horizontal stress increments near the fault exceeded 0.07 MPa. The experimental data reveal that stress concentration during the fault slip stage causes severe damage to the upper coal seam roof, with localized vertical stress fluctuations exceeding 35%. Structural planes were found to control crack nucleation and slip paths, conforming to the Mohr–Coulomb shear failure criterion. This research provides new insights into the dynamic coupling of tectonic stress and fault mechanics, offering novel experimental evidence for understanding fault-induced disasters. The findings contribute to the predictive modeling of stress redistribution in fault zones and support safer deep mining practices in structurally complex coalfields, which has potential implications for petroleum geomechanics and energy resource extraction in similar tectonic settings. Full article

The electromagnetic vibration characteristics of doubly fed induction machines (DFIMs) employed in variable-speed pumped storage units, which must accommodate frequent power response and operational mode transitions, serve as critical indicators for assessing unit safety and stability. Nevertheless, there persists a significant research gap regarding generalized vibration analysis models and comprehensive investigations into their steady-state and dynamic vibration performance. To address this challenge, this study develops a universal analytical model for electromagnetic excitation forces in DFIMs using Maxwell’s stress tensor method, explicitly incorporating operational conditions such as rotor eccentricity and load imbalance. Using a 300 MW DFIM as a case study, we employ a hybrid numerical-analytical approach to examine the detrimental effects of harmonic currents generated by rotor-side converters. Furthermore, we systematically analyze how spatial harmonics induced by mechanical faults and temporal harmonics arising from electrical faults collectively influence the electromagnetic vibration behavior. Experimental validation conducted on a 10 MW DFIM prototype through vibration displacement measurements confirms the efficacy of the proposed analytical framework. Full article

This study investigates the real-time monitoring of 3D printing using an endoscopic camera system integrated directly into the print head. The embedded endoscope enables continuous observation of the area surrounding the extruder, facilitating real-time inspection of the currently printed layers. A convolutional neural network (CNN) is employed to analyse captured images in the direction of print progression, enabling the detection of common defects such as stringing, layer shifting, and inadequate first-layer adhesion. The primary innovation of this work lies in its capacity for online quality assessment and immediate classification of print integrity within predefined thresholds. This system allows for the prompt termination of printing in the case of critical faults or dynamic adjustment of printing parameters in response to minor anomalies. The proposed solution offers a novel pathway for optimising additive manufacturing through real-time feedback on layer formation. Full article

This study investigates the uplift and exhumation history of the southern segment of the western margin of the Ordos Basin using low-temperature thermochronology, including zircon (U-Th)/He (ZHe), apatite fission-track (AFT), and apatite (U-Th)/He (AHe) data, combined with thermal history modeling. The study area exhibits a complex structural framework shaped by multiple deformation events, leading to the formation of extensively developed fault systems. Such faulting can adversely affect hydrocarbon preservation. To better constrain the timing of fault reactivation in this area, we carried out an integrated study involving low-temperature thermochronology and burial history modeling. The results reveal a complex, multi-phase thermal-tectonic evolution since the Late Paleozoic. The ZHe ages (291–410 Ma) indicate deep burial and heating related to Late Devonian–Early Permian tectonism and basin sedimentation, reflecting early orogenic activity along the western North China Craton. During the Late Jurassic to Early Cretaceous (165–120 Ma), the study area experienced widespread and differential uplift and cooling, controlled by the Yanshanian Orogeny. Samples on the western side of the fault show earlier and more rapid cooling than those on the eastern side, suggesting a fault-controlled, basinward-propagating exhumation pattern. The cooling period indicated by AHe data and thermal models reflects the Cenozoic uplift, likely induced by far-field compression from the rising northeastern Tibetan Plateau. These findings emphasize the critical role of inherited faults not only as thermal-tectonic boundaries during the Mesozoic but also as a pathway for hydrocarbon migration. Meanwhile, thermal history models based on borehole data further reveal that the study area underwent prolonged burial and heating during the Mesozoic, reaching peak temperatures for hydrocarbon generation in the Late Jurassic. The timing of major cooling events corresponds to the main stages of hydrocarbon expulsion and migration. In particular, the differential uplift since the Mesozoic created structural traps and migration pathways that likely facilitated hydrocarbon accumulation along the western fault zones. The spatial and temporal differences among the samples underscore the structural segmentation and dynamic response of the continental interior to both regional and far-field tectonic forces, while also providing crucial constraints on the petroleum system evolution in this tectonically complex region. Full article