Search Results (313)

Sustainable pavement design requires a balanced consideration of economic, environmental, and social impacts. In line with Federal Highway Administration (FHWA) guidelines for sustainable roadway infrastructure, incorporating recycled materials such as reclaimed asphalt pavement (RAP), recycled pavement material (RPM), recycled asphalt shingles (RASs), and warm-mix asphalt (WMA) has been shown to reduce natural resource depletion while promoting circular construction practices. This study investigates the structural performance of Portland cement concrete (PCC) pavements constructed on RAP and RPM base layers. A series of design scenarios was modeled using site-specific laboratory and field data—particularly subgrade soil properties and climatic conditions—from El Paso and San Antonio, Texas. The analysis incorporates unsaturated soil parameters and follows the performance thresholds set by the Mechanistic-Empirical Pavement Design Guide (MEPDG). Findings indicate that concrete mixture design, pavement structure, and local weather conditions are the primary drivers of distress in jointed plain concrete pavements (JPCPs). However, subsoil characteristics have a significant impact on joint faulting in JPCP and punchout occurrences in continuously reinforced concrete pavements (CRCPs), especially in thinner sections. Notably, the use of up to 50% recycled material in the base layer had minimal adverse effects on pavement performance, underscoring its viability as a sustainable design strategy for rigid pavements. Full article

This paper investigates the joint state and fault estimation problem for a class of nonlinear systems subject to both measurement censoring (MC) and random missing measurements (MMs). Recognizing that state estimation for nonlinear systems in complex environments is frequently compromised by MMs, MC phenomena, and actuator faults, a novel joint estimation framework that integrates improved Tobit Kalman filtering and federated fusion is proposed, enabling simultaneous robust estimation of system states and fault signals. Among them, the Tobit measurement model is introduced to characterize the phenomenon of MC, a set of Bernoulli random variables is used to describe the MM phenomenon and common actuator faults (abrupt and ramp faults) are considered. In the fusion estimation stage, each sensor transmits observations to the local estimator for preliminary estimation, then sends the local estimated values to the fusion center for generating fusion estimates. The local filtering error covariance is ensured and the upper bound is minimized by reasonably determining the filter gain, while the fusion center performs fusion estimation based on the federated fusion criterion. In addition, this paper proves the boundedness of the filtering error of the designed estimator under certain conditions. Finally, the effectiveness of the estimation framework is demonstrated through two engineering experiments. Full article

In the field of robotic fault detection, although the random forest (RF) algorithm is widely adopted, its limited accuracy remains a critical constraint in practical engineering applications. To address this technical challenge, this study proposes a Two-Stages Random Forest (TSRF) algorithm. This approach constructs a hierarchical architecture with a dynamic adaptive weighting strategy, where the class probability vectors generated in the 1st-stage serve as meta-features for the 2nd-stage classifier. Such hierarchical optimization enables the model to precisely identify fault-sensitive features, effectively overcoming the performance limitations of conventional single-model frameworks. To validate the proposed approach, we conducted comparative experiments using a multidimensional kinematic feature dataset from hexapod robot joint fault detection. Benchmark models included geometry-feature-based RF and physics-informed RF as established baselines. Experimental results demonstrate that TSRF achieves a classification accuracy of 99.7% on the test set, representing an 18.8% improvement over standard RF. This significant advancement provides a novel methodological framework for intelligent fault diagnosis in complex electromechanical systems. Full article

Fault-tolerant control for bionic robotic fish presents significant challenges due to the complex dynamics and asymmetric propulsion introduced by joint failures. To address this issue, this paper proposes a fault-tolerant following control framework for multi-joint bionic robotic fish by combining fuzzy control methodologies and dynamic model correction. Firstly, offline fault analysis is conducted based on the dynamic model under multi-variable parameter conditions, quantitatively deriving influence factor functions that characterize the effects of different joint faults on velocity and yaw performance of the robotic fish. Secondly, an adaptive-period yaw filtering algorithm combined with an improved line-of-sight navigation method is employed to accommodate the motion characteristics of bionic robotic fish. Thirdly, a dual-loop following control strategy based on fuzzy algorithms is designed, comprising coordinated velocity and yaw control loops, where velocity and yaw influence factors serve as fuzzy controller inputs with expert experience-based rule construction. Finally, extensive numerical simulations are conducted to verify the effectiveness of the proposed method. The obtained results indicate that the bionic robotic fish can achieve fault-tolerant following control under multiple fault types, offering a valuable solution for underwater operations in complex marine environments. Full article

In this paper, the molecular dynamics simulation method was adopted to systematically study the microstructure evolution behavior of TiAl alloys under impact compression under three typical crystal orientations ([001], [110], [111]). By analyzing the characteristics of structural phase transition, defect type evolution, dislocation expansion, and radial distribution function, the anisotropic response mechanism under the joint regulation of crystal orientation and impact velocity was revealed. The results show that the [111] crystal orientation is most prone to local amorphous transformation at high strain rates, and its structural collapse is due to the rapid accumulation and limited reconstruction of dislocations/faults. The [001] crystal orientation is prone to forming staggered stacking of layers and local HCP phase transformation, presenting as a medium-strength structural disorder. Under the strain regulation mechanism dominated by twinning, the [110] orientation exhibits superior structural stability and anti-disorder ability. With increases in the impact velocity, the defect type gradually changes from isolated dislocations to large-scale HCP regions and amorphous bands, and there are significant differences in the critical velocities of amorphous transformation corresponding to different crystal orientations. Further analysis indicates that the HCP structure and the formation of layering faults are important precursor states of amorphous transformation. The evolution of the g(r) function verifies the stepwise disintegration process of medium and long-range ordered structures under shock induction. It provides a new theoretical basis and microscopic perspective for the microstructure regulation, damage tolerance improvement, and impact resistance design of TiAl alloys under extreme stress conditions. Full article

This paper addresses the core contradiction in fault diagnosis of gearboxes in heavy-duty equipment, where it is challenging to achieve both lightweight and robustness in dynamic industrial environments. Current diagnostic algorithms often struggle with balancing computational efficiency and diagnostic accuracy, particularly in noisy and variable operating conditions. Many existing methods either rely on complex architectures that are computationally expensive or oversimplified models that lack robustness to environmental interference. A novel, lightweight, and robust diagnostic network, YConvFormer, is proposed. Firstly, a time–frequency joint input channel is introduced, which integrates time-domain waveforms and frequency-domain spectrums at the input layer. It incorporates an Efficient Channel Attention mechanism with dynamic weighting to filter noise in specific frequency bands, suppressing high-frequency noise and enhancing the complementary relationship between time–frequency features. Secondly, an axial-enhanced broadcast attention mechanism is proposed. It models long-range temporal dependencies through spatial axial modeling, expanding the receptive field of shock features, while channel axial reinforcement strengthens the interaction of harmonics across frequency bands. This mechanism refines temporal modeling with minimal computation. Finally, the YConvFormer lightweight architecture is proposed, which combines shallow feature processing with global–local modeling, significantly reducing computational load. The experimental results on the XJTU and SEU gearbox datasets show that the proposed method improves the average accuracy by 6.55% and 19.58%, respectively, compared to the best baseline model, LiteFormer. Full article

With the accelerating global transition toward sustainable energy systems, power grids with a high share of renewable energy face increasing challenges due to volatility and uncertainty, necessitating advanced flexibility resource planning and stability optimization strategies. This paper presents a comprehensive distribution network planning framework that coordinates and integrates multiple types of flexibility resources through joint optimization and network reconfiguration to enhance system adaptability and operational resilience. A novel virtual network coupling modeling approach is proposed to address topological constraints during network reconfiguration, ensuring radial operation while allowing rapid topology adjustments to isolate faults and restore power supply. Furthermore, to mitigate the uncertainty and fault risks associated with extreme weather events, a CVaR-based risk quantification framework is incorporated into a bi-level optimization model, effectively balancing investment costs and operational risks under uncertainty. In this model, the upper-level planning stage optimizes the siting and sizing of flexibility resources, while the lower-level operational stage coordinates real-time dispatch strategies through demand response, energy storage operation, and dynamic network reconfiguration. Finally, a hybrid SA-PSO algorithm combined with conic programming is employed to enhance computational efficiency while ensuring high solution quality for practical system scales. Case study analyses demonstrate that, compared to single-resource configurations, the proposed coordinated planning of multiple flexibility resources can significantly reduce the total system cost and markedly improve system resilience under fault conditions. Full article

This study applies the Weighted Overlay Analysis (WOA) method integrated with GIS to assess landslide susceptibility along Al Figrah Road in Al-Madinah Al-Munawarah, western Saudi Arabia. Seven key conditioning factors, elevation, slope, aspect, drainage density, lithology, soil type, and precipitation were integrated using high-resolution remote sensing data and expert-assigned weights. The output susceptibility map categorized the region into three zones: low (93.5 million m²), moderate (271.2 million m²), and high risk (33.1 million m²). Approximately 29% of the road corridor lies within the low-risk zone, 48% in the moderate zone, and 23% in the high-risk zone. Ten critical sites with potential landslide activity were detected along the road, correlating well with the high-risk zones on the map. Structural weaknesses in the area, such as faults, joints, foliation planes, and shear zones in both igneous and metamorphic rock units, were key contributors to slope instability. The findings offer practical guidance for infrastructure planning and geohazard mitigation in arid, mountainous environments and demonstrate the applicability of WOA in data-scarce regions. Full article

Dual three-phase PMSMs (DTP-PMSMs) have attracted increasing attention in the field of robotics industry for their higher power density and enhanced fault-tolerant ability. The non-phase-shifted DTP-PMSM (NPSDTP-PMSM), which shows naturally prevailed performance on zero-sequence current (ZSC) suppression, necessitates the investigation on the control method with improved fault-tolerant performance. In this paper, a novel fault-tolerant control (FTC) method for NPSDTP-PMSM is proposed, which concurrently simultaneously reduces copper loss and suppresses torque ripple under single and dual open phase fault. Firstly, the mathematical model of NPSDTP-PMSM is established, where the ZSC self-suppressing mechanism is revealed. Based on which, investigations on open phase fault and the copper loss characteristics for NPSDTP-PMSM are conducted. Subsequently, a novel fault-tolerant control method is proposed for NPSDTP-PMSM, where the torque ripple is reduced by mutual cancellation of harmonic torques from two winding sets and minimized copper loss is achieved based on the convex characteristic of copper loss. Experimental validation on an integrated robotic joint motor platform confirms the effectiveness of the proposed method. Full article

To address the problems of feature extraction, cost of obtaining labeled samples, and large differences in domain distribution in bearing fault diagnosis on variable operating conditions, an unsupervised domain-adaptive bearing fault diagnosis method based on migration learning using MSACNN-IJMMD-DANN (multi-scale and attention-based convolutional neural network, MSACNN, improved joint maximum mean discrepancy, IJMMD, domain adversarial neural network, DANN) is proposed. Firstly, in order to extract fault-type features from the source domain and target domain, this paper establishes a MSACNN based on multi-scale and attention mechanisms. Secondly, to reduce the feature distribution difference between the source and target domains and address the issue of domain distribution differences, the joint maximum mean discrepancy and correlation alignment approaches are used to create the metric criterion. Then, the adversarial loss mechanism in DANN is introduced to reduce the interference of weakly correlated domain features for better fault diagnosis and identification. Finally, the method is validated using bearing datasets from Case Western Reserve University, Jiangnan University, and our laboratory. The experimental results demonstrated that the method achieved higher accuracy across different migration tasks, providing an effective solution for bearing fault diagnosis in industrial environments with varying operating conditions. Full article

This study proposes a novel paradigm for enhancing the fault detection and isolation (FDI) of gas generators in all-electric auxiliary power unit (APU) by utilizing shaft power information from the starter/generator. First, we conduct an investigation into the challenges and opportunities for FDI that are brought about by APU electrification. Our analysis reveals that the electrification of APUs opens new possibilities for utilizing shaft power estimates from starters/generators to improve gas generator FDI. We then provide comprehensive theoretical and analytical evidence demonstrating why, how, and to what extent the shaft power information from the starter/generator can fundamentally enhance the estimation accuracy of system states and health parameters of the gas generator, while also identifying key factors influencing these improvements in FDI performance. The effectiveness of the proposed paradigm and its theoretical foundations are validated through extensive Monte Carlo simulation runs. The research findings provide a unique perspective in addressing three fundamental questions—why joint fault diagnosis of the starter/generator and gas generator in all-electric APUs is essential, how it can be implemented, and what factors determine its effectiveness—thereby opening up promising new avenues for FDI technologies in all-electric APU systems. Full article

In response to the limitations of traditional static reliability analysis methods in characterizing the reliability changes of the Integrated Power System, this paper proposes a time-varying reliability analysis framework based on a Dynamic Bayesian Network. By embedding a multi-physics coupled degradation model into the conditional probability nodes of the Dynamic Bayesian Network, a joint stochastic differential equation for the degradation process was constructed, and the dynamic correlation between continuous degradation and discrete fault events throughout the entire life cycle was achieved. A modified method for modeling continuous degradation systems was proposed, which effectively solves the numerical stability problem of modeling complex degradation systems. Finally, the applicability and correctness of the model were verified through numerical examples, and the results showed that the analysis framework can be effectively applied to time-varying reliability assessment and dynamic health management of complex equipment systems such as the Integrated Power System. Full article

In recent years, Academia and industry have conducted extensive and in-depth research on bearing-fault-diagnosis technology. However, the current modeling of time–space coupling characteristics in rolling bearing fault diagnosis remains inadequate, and the integration of multi-modal correlations requires further improvement. To address these challenges, this paper proposes a joint diagnosis framework integrating graph convolutional networks (GCNs) with attention-enhanced bidirectional gated recurrent units (BiGRUs). The proposed framework first constructs an improved K-nearest neighbor-based spatio-temporal graph to enhance multidimensional spatial–temporal feature modeling through GCN-based spatial feature extraction. Subsequently, we design an end-to-end spatio-temporal joint learning architecture by implementing a global attention-enhanced BiGRU temporal modeling module. This architecture achieves the deep fusion of spatio-temporal features through the graph-structural transformation of vibration signals and a feature cascading strategy, thereby improving overall model performance. The experiment demonstrated a classification accuracy of 97.08% on three public datasets including CWRU, verifying that this method decouples bearing signals through dynamic spatial topological modeling, effectively combines multi-scale spatiotemporal features for representation, and accurately captures the impact characteristics of bearing faults. Full article

In real-world scenarios, the rotational speed of bearings is variable. Due to changes in operating conditions, the feature distribution of bearing vibration data becomes inconsistent, which leads to the inability to directly apply the training model built under one operating condition (source domain) to another condition (target domain). Furthermore, the lack of sufficient labeled data in the target domain further complicates fault diagnosis under varying operating conditions. To address this issue, this paper proposes a spatiotemporal feature fusion domain-adaptive network (STFDAN) framework for bearing fault diagnosis under varying operating conditions. The framework constructs a feature extraction and domain adaptation network based on a parallel architecture, designed to capture the complex dynamic characteristics of vibration signals. First, the Fast Fourier Transform (FFT) and Variational Mode Decomposition (VMD) are used to extract the spectral and modal features of the signals, generating a joint representation with multi-level information. Then, a parallel processing mechanism of the Convolutional Neural Network (SECNN) based on the Squeeze-and-Excitation module and the Bidirectional Long Short-Term Memory network (BiLSTM) is employed to dynamically adjust weights, capturing high-dimensional spatiotemporal features. The cross-attention mechanism enables the interaction and fusion of spatial and temporal features, significantly enhancing the complementarity and coupling of the feature representations. Finally, a Multi-Kernel Maximum Mean Discrepancy (MKMMD) is introduced to align the feature distributions between the source and target domains, enabling efficient fault diagnosis under varying bearing conditions. The proposed STFDAN framework is evaluated using bearing datasets from Case Western Reserve University (CWRU), Jiangnan University (JNU), and Southeast University (SEU). Experimental results demonstrate that STFDAN achieves high diagnostic accuracy across different load conditions and effectively solves the bearing fault diagnosis problem under varying operating conditions. Full article

The Jinchuan area is located along the northeastern margin of Longshou Shan, in the western part of the North China Plate. Since the Paleoproterozoic period, it has undergone complex geological evolution. A systematic analysis of the tectonic evolution in this region reveals key information about the tectonic background and evolutionary characteristics since the Paleoproterozoic period and serves as a crucial approach for understanding metallogenic processes and achieving breakthroughs in deep mineral exploration. Based on detailed field investigations, this study analyzes the structural characteristics of the area, focusing on conjugate shear joints, folds, and faults. Combined with previous research findings, the evolution of the tectonic stress field is analyzed. The results indicate that the orientation of the maximum principal stress underwent the following six distinct phases of change: nearly north-south (NS) → nearly east-west (EW) → nearly north-south (NS) → north-northeast-south-southwest (NNE-SSW) → northwest-southeast (NW-SE) → northeast-southwest (NE-SW). Integrating these results with the regional tectonic framework, the study systematically reconstructs the tectonic evolution of the Jinchuan area. This research provides important scientific insights and practical value for enhancing geological understanding of the region and guiding mineral resource exploration and development. Full article