Search Results (555)

Wet friction components are critical to power transmission in petroleum drilling machinery, where their reliability directly affects system stability. Surface defects, such as scratches and plowing grooves, can significantly degrade transmission performance, highlighting the importance of interface morphology prediction for intelligent wear diagnosis. In this study, interface morphology data under different conditions are acquired using a UMT-Tribolab test platform and a white light interferometer. The Scale-Invariant Feature Transform (SIFT) algorithm is employed to achieve precise localization of microscopic regions before and after testing. Based on this, an NRBO-VMD-Transformer model is developed to predict the interface morphology of wet friction components under varying conditions. The results demonstrate that SIFT enables accurate localization of microscopic regions, while the proposed model achieves high-precision prediction of interface morphology evolution. These findings provide a reliable basis for interface morphology prediction and wear evolution analysis of wet friction components. Full article

This paper introduces the research progress of path planning, trajectory tracking control, and multi-machine collaborative operation systems for agricultural robots. It summarizes the development laws of 3D terrain modeling and adaptive path planning algorithms for complex agricultural environments such as hills and mountains, and analyzes the dynamic disturbance characteristics of agricultural machinery under slip, sideslip, and dynamic load changes. Through comprehensive analysis, it is found that traditional kinematic control models have limitations in complex and unstructured environments. Combining soil mechanics mechanisms, variable load identification, and robust control strategies is key to improving trajectory tracking stability and operational quality. In terms of multi-machine collaboration, this paper discusses master–slave collaboration, distributed control, and task allocation modes. It further identifies that the stability of collaboration and interoperability standards between devices in weak network environments are currently the main bottlenecks limiting the large-scale application of this technology. Finally, this paper provides prospects for future research directions and suggests strengthening the closed-loop integration of perception, decision-making, and dynamic models, establishing industry unified standards, and enhancing the safety of the entire lifecycle of operations, providing suggestions for the unmanned application of agricultural robots. Full article

Traditional signal processing methods for rotating machinery fault diagnosis rely heavily on human experience, while deep learning models often suffer from unstable classification boundaries and poor generalization under complex operating conditions. To address these issues, this paper proposes a hybrid fault diagnosis method based on CWT and CNN-DOA-LSSVM. Firstly, CWT is employed to convert one-dimensional vibration signals into high-resolution time-frequency maps, fully highlighting the transient impact features of faults. Secondly, CNNs automatically extract deep discriminative features, avoiding the cumbersome process of manual feature engineering. Thirdly, LSSVM replaces the Softmax classification layer in traditional CNNs to overcome the deficiency of the Softmax classifier in nonlinear classification. Finally, by leveraging the two-stage separation mechanism of exploration and exploitation in DOA, along with its unique forgetting-supplement and dream-sharing strategies, an adaptive optimal configuration of the key parameters of LSSVM is achieved. Validation results on the Southeast University gearbox dataset and the Huazhong University of Science and Technology bearing dataset show that the proposed method achieves average classification accuracies of 99.59% and 99.50%, respectively, demonstrating good performance in both classification accuracy and stability. Full article

Power batteries used in electric-powered vessels, new-energy tractors or construction machinery typically require prolonged, continuous operation at high power levels, which can lead to significant heat buildup and pose serious threats to battery safety, cycle life, and operational stability. Traditional air-cooled and liquid-cooled systems struggle to meet the requirements for efficient heat dissipation under heavy loads. Phase change materials (PCMs) are ideal for passive battery thermal management due to their high latent heat but are severely limited by low thermal conductivity and liquid leakage. In this study, nitrogen-doped carbon nanotubes@SiO₂ (PNT@SiO₂) were synthesized and further fabricated into oriented porous aerogels by directional freeze-drying using cellulose-based materials as the skeleton. Polyethylene glycol-8000 (PEG-8000) was loaded via vacuum impregnation to obtain the PSAP composite PCM. The optimized composite exhibits a thermal conductivity of 0.93 W/m·K, 3.2 times that of pure PEG, with 96% PEG loading and a phase change enthalpy of 158 J/g. Battery thermal management tests demonstrate its excellent temperature control and heat suppression performance. This study provides a high-performance and feasible thermal management solution for power batteries used in relevant fields. Full article

Rotor imbalance and abnormal vibration are classical operating conditions in rotating machinery and can often be identified by conventional vibration analysis. However, the development of low-power, self-powered, and distributed sensing nodes remains important for long-term condition monitoring, particularly in scenarios where external power supply, wiring, and maintenance are constrained. Existing vibration sensors, including piezoelectric and capacitive types, are constrained by power consumption and degraded performance under low-frequency and weak excitation. To address this issue, a magnetically repulsive cushion triboelectric nanogenerator (MRCT) is proposed to enable self-powered vibration sensing. The magnetic-repulsion cushion allows the upper friction layer to undergo stable contact–separation motion under a non-contact restoring force, while the microstructured strip electrode array (MSEA) enhances the triboelectric output and signal stability. A hybrid convolutional neural network–gated recurrent unit (CNN-GRU) deep-learning model is employed to extract time-domain and frequency-domain features from the collected signals, enabling real-time identification of rotor vibration amplitude, frequency, and imbalance weight. Experimental results show that the MRCT provides stable output, a high signal-to-noise ratio, and an identification accuracy above 98% for predefined rotor imbalance-weight states under laboratory conditions. In addition, a shaft-misalignment-related abnormal vibration condition was examined on the motor platform. The corresponding time-domain and frequency-domain analyses show that the MRCT voltage signal exhibits distinguishable signal variations under normal and misalignment-related conditions, including spectral changes around the 2× rotational frequency. A laboratory-scale AIoT-oriented demonstration further verifies the feasibility of integrating MRCT signal acquisition, CNN-GRU inference, wireless transmission, and GUI-based visualization. It should be noted that the present work mainly focuses on imbalance-state recognition, while the misalignment-related experiment provides an additional sensor-response verification. Broader validation involving mechanical looseness, bearing defects, variable-speed operation, cross-machine testing, and long-term industrial conditions remains necessary. Full article

Smart manufacturing, Industry 4.0, and cyber-physical systems (CPSs) require sensing architectures capable of resolving both spatially distributed asset behavior and highly localized process states. This review examines optical fiber sensors (OFSs) and integrated photonic sensors for industrial monitoring through a deployment-oriented, multi-scale perspective. The discussion covers five major application regimes: continuous infrastructure surveillance, structural health monitoring (SHM) of load-bearing composites, dynamic condition monitoring of machinery, in situ observability in advanced manufacturing, and localized chemical or gas sensing. Extended fiber-optic networks, including distributed fiber-optic sensing (DFOS) based on Rayleigh, Raman, and Brillouin scattering, together with multiplexed fiber Bragg grating (FBG) sensors, provide passive, embeddable, and remotely interrogated monitoring for large-scale assets and harsh environments. Photonic integrated circuits (PICs) shift transduction to compact node-level devices for localized thermal, mechanical, refractive-index, absorption, vibration, and inertial measurements, while plasmonic and dielectric nanophotonic sensors extend optical monitoring toward surface-selective and chemically specific detection. Across these platforms, digital signal processing (DSP), machine learning (ML), sensor fusion, and digital-twin (DT) coupling are treated as artificial-intelligence-enabled (AI-enabled) layers for signal recovery, inverse mapping, uncertainty reduction, and predictive maintenance. The review argues that scalable industrial adoption is less limited by sensing physics than by the complete deployment chain: packaging, fiber–chip interfacing, calibration stability, interrogation robustness, and AI-enabled data interpretation. This manuscript is structured as a deployment-oriented narrative review of optical fiber and integrated photonic sensors for industrial monitoring and smart manufacturing. Full article

Accurate fault diagnosis of rotating machinery is critical for ensuring the reliability of the energy, industrial, and transportation sectors. However, conventional methods face significant challenges, including the susceptibility of the Snow Ablation Optimizer (SAO) to local optima, the instability of Multiscale Differential Symbolic Entropy (MDSE) with short time series, and the non-adaptability of Support Vector Machine parameters. To address these issues, this study proposes a parameter-adaptive fault diagnosis framework integrating an improved SAO with Adaptive Refined Composite Multiscale Differential Symbolic Entropy (Adaptive-RCMDSE). First, the Logistic Sine Cosine strategy (LSC) is introduced to enhance SAO’s global search capability, forming the LSC-SAO algorithm. Subsequently, an Adaptive-RCMDSE method is developed wherein LSC-SAO optimizes the control parameter to significantly improve feature stability for short time series. Furthermore, an Adaptive Support Vector Machine (Adaptive-SVM) model is constructed, employing LSC-SAO to automatically tune the penalty factor and kernel parameters for precise fault identification. Finally, validation is performed on gearbox, ball bearing, and axle box bearing datasets. Results indicate that the proposed method achieves superior diagnostic performance, with average accuracies of 99.70%, 99.29%, and 99.28%, respectively, outperforming existing methods. This work provides an effective and robust solution for intelligent health monitoring of rotating machinery. Full article

Hilly and mountainous regions are strategically vital for national food security. However, due to complex topographical constraints, their agricultural mechanization levels remain severely underdeveloped. This creates a critical bottleneck in agricultural modernization. Conventional agricultural machinery faces multifaceted challenges in terrain adaptability, operational efficiency, and safety assurance when deployed in these environments, necessitating the urgent development of specialized chassis with enhanced trafficability and stability. Following a systematic literature review of key technologies, including power transmission systems, traveling and support mechanisms, leveling control, and navigation tracking, this study reveals that current chassis technology is advancing toward intelligentization, enhanced efficiency, environmental sustainability, and improved terrain adaptability. The analysis demonstrates that multiple technological pathways, encompassing mechanical, hydraulic, and electric drives, are exhibiting convergent and complementary trends. Future research and development should prioritize the following areas: integrated intelligent coordinated control architectures, green and sustainable power system innovation, modular and reconfigurable platform design, and the establishment of collaborative frameworks among industry, academia, research institutions, and application sectors. Comprehensive standardization systems are also needed. These strategic directions are essential for comprehensively elevating agricultural mechanization levels and maximizing developmental benefits in hilly and mountainous regions. Full article

Effective fault diagnosis of wind turbine blades and rotating machinery is critical for ensuring operational reliability and reducing maintenance costs. This study introduces a healthy-reference modeling framework that combines physics-informed neural network (PINN) with deviation-based dynamic regime features for systematic fault detection. At first, healthy and faulty data are normalized, then PINN is trained solely on healthy data, creating a reference model that predicts normal behavior. Deviations between measured signals and the healthy-reference predictions are then analyzed to extract key dynamic regime features, including energy, stability, drift, intermittency, and persistence, capturing subtle variations caused by faults. An interpretable Support Vector Machine (SVM) classifier uses these features to identify fault types such as ball, inner race, outer race, crack, erosion, and unbalance. Classification is performed using dynamic feature combinations while energy is often used as the base feature. The result shows energy with persistence combination performance is better than other feature combinations, and fused features achieved higher accuracy for both datasets. The approach is validated on both bearing data and an experimental blade dataset, demonstrating strong performance across different mechanical systems. Comparative evaluation with three different approaches, including Cross-load Scalogram-based CNN, Spectrogram-based CNN, and Hybrid SVM, highlights that the proposed healthy reference framework offers a data-efficient, interpretable, and robust solution for fault detection. This work highlights the importance of modeling healthy dynamics before classification, capturing both how strong a fault is and how it behaves over time, which offers a practical approach for wind turbine condition monitoring with limited data. Full article

Reliable and intelligent fault diagnosis of rotating machinery is crucial for the safety and stability of industrial systems. Nevertheless, the acquisition of labeled fault data is often difficult in practical applications because of the high cost of maintenance, the rarity of fault events, and the inherent safety risks associated with fault induction experiments. As a result, most real-world datasets consist mainly of healthy operating samples, which makes bearing fault diagnosis under fault-free training conditions particularly challenging. The objective of this study was to develop a simulation-driven diagnostic framework capable of identifying real bearing faults without using real fault samples during model training. To achieve this objective, pseudo-fault data were generated by superimposing periodic impulse–resonance responses, governed by theoretical bearing fault characteristic frequencies, onto healthy vibration signals. The synthesized dataset was further analyzed using wavelet packet decomposition and envelope spectrum analysis to extract discriminative time–frequency features. These features were then fed into the proposed Hierarchical Convolutional Attention Network (HCANet), which captured hierarchical multi-scale representations while emphasizing fault-related components. Furthermore, a Central Clustering Loss was employed to encourage intra-class compactness and enhance inter-class separability, thereby improving the generalization capability of the diagnostic model. Experimental validation on two bearing datasets showed that the proposed method achieved high diagnostic accuracy when tested on real fault samples, despite being trained exclusively on healthy signals and synthesized pseudo-fault samples. These results demonstrated the effectiveness of the proposed simulation-driven strategy and highlighted its potential as a practical solution for bearing fault diagnosis in zero-real-fault-data scenarios. Full article

This study proposes an attitude stabilization method based on an improved adaptive Kalman filter (AKF). The aim is to address attitude fluctuations and rollover risks in rail-based agricultural transport platforms on hilly terrain caused by slope changes, load shifts and vibrations. A dynamic model integrating the load distribution and center-of-mass migration was established, and an adaptive noise covariance mechanism was used to precisely estimate the roll and pitch angles in real time. A dual-channel proportional–integral–derivative controller was designed for automatic leveling, and a rollover risk index (RRI) was adopted for safety evaluation. Simulations revealed the ability of the improved AKF to decrease the roll estimation (RMSE) from 1.2684° to 0.8670° and the stabilization time from 0.6250 to 0.3830 s for the roll and from 0.6930 to 0.4110 s for the pitch. Under 10–30° slope disturbances, the average RRI decreased from 0.1861 to 0.1506. Field tests further demonstrated decreases in the peak roll and pitch angles from 4.8° and 4.1° to 3.1° and 2.7°, respectively, and a decrease in the average RRI from 0.203 to 0.169. The improvements in estimation accuracy, leveling performance, and operational safety under complex disturbances indicate the strong engineering potential of the proposed method. Full article

Dry gas seals (DGSs) are currently the preferred sealing method for high-speed rotating machinery, widely used in the fields of petrochemicals and energy and power. This study analyzes the effect of groove structure and operating parameters (rotary ring speed and inlet pressure) on the performance of the sealing system. The results show that a swallowtail-like groove demonstrates a dual effect of improving film stability and reducing leakage under specific working conditions. Specifically, under the inlet pressure of 4.5852 MPa and rotational speed of 10,380 rpm, the swallowtail-like groove achieves a 1.84% reduction in leakage and a 0.32% increase in opening force compared with a conventional spiral groove. Rotational speed has the greatest impact on the gas film stability of the cluster spiral groove. Increasing inlet pressure enhances the dynamic stabilization of gas film. Dynamic analysis indicates that the opening force demonstrates a linear proportionality with inlet pressure, whereas leakage follows an exponential growth. This work can provide guidance for optimizing the groove structure in dry gas sealing systems. Full article

Intelligent mechanical fault diagnosis plays a key role in maintaining rotating machinery. Although data-driven unsupervised domain adaptation methods have achieved considerable progress, their industrial applications are often restricted by low-quality sensor data. Non-stationary vibration signals and background noise easily corrupt target pseudo-labels, while conventional methods focusing on global statistical matching usually neglect local structures, leading to confirmation bias under dynamic loads. To improve diagnostic reliability, we propose a Noise-Resilient Whitened Domain Adaptation (NRWDA) framework. To handle covariance fluctuations caused by changing working conditions, a Lipschitz-bounded Temporal Whitening (LTW) module is designed as a low-pass filter. An Entropy-guided Prototype Truncation (EPT) mechanism is adopted to discard ambiguous labels and better calibrate semantic centers. In addition, a Dispersion-Adaptive Contrastive Sharpening (DACS) strategy is introduced to dynamically adjust the contrastive temperature based on predictive dispersion, thus tightening decision boundaries. The proposed method is evaluated on CWRU, PU, and MFPT datasets. The PU dataset, featuring fluctuating loads and non-stationary signals, poses a strict test, yet our model maintains its stability even at a 0 dB SNR—a condition where standard approaches usually break down. During the $P0\to P3$ transfer task involving substantial radial force variations, NRWDA secures a 72.36% accuracy and surpasses established baselines. These findings confirm that our technique successfully isolates dependable diagnostic features from corrupted sensor measurements within actual industrial settings. Full article

Heritable thoracic aortic diseases (HTAD) are inherited conditions that increase the risk of thoracic aortic aneurysms, dissections, and premature aortic rupture. Advances in human genetics and experimental models have transformed our understanding of these disorders from a phenotype-based classification system to a mechanism-based view involving extracellular matrix (ECM) architecture, transforming growth factor-β (TGFβ) signaling, and vascular smooth muscle cell contractility. Marfan syndrome, Loeys–Dietz syndrome, and nonsyndromic HTAD demonstrate how genetic mutations can disrupt the components that stabilize the aortic wall. These pathogenic mechanisms influence matrix organization, intracellular signaling, and the contractile machinery within the mechanically stressed proximal aorta. In this review, we summarize current mechanistic insights into the major forms of HTAD and discuss how new molecular and cellular concepts could influence surveillance, genetic counseling, and genotype-guided therapeutic strategies. Full article

As a key component of sugarcane harvesting machinery, the spiral sugarcane lifter (SSL) enhances harvesting quality by lifting lodged sugarcane (LSC) into a posture suitable for stalk-base cutting and feeding. To improve the SSL’s lifting performance for LSC, this study developed a rigid–flexible coupling (RFC) simulation model of the sugarcane–SSL interaction and conducted kinematic and force analyses to identify the main shortcomings of the original design. Critical structural and operational parameters affecting lifting performance–including the lifting roller pitch, roller diameter, roller inclination angle, and lifter shoe length—were redesigned using mechanism-based constraints and simulation-assisted evaluation. The optimized SSL exhibited increased lifting speed and stability under low–speed, severe–lodging conditions. Under side-forward lodging (side deflection angle = 30°), the average maximum vertical height of the centroid (VHC) increased by 40.36%, and paired comparisons across three simulated lodging-angle scenarios showed significant improvement. Field tests under severe lodging at 0.55 m/s (≈2 km/h) yielded an average absolute simulation–to–field error of 5.37%. These findings support the effectiveness of the proposed parameter redesign for the tested medium-size harvester, although further validation is required under higher forward speeds, greater biomass throughput, and more variable soil conditions. Full article