Search Results (41)

This study investigates fire behavior and personnel evacuation safety in a nuclear chemical workshop based on the Fire Dynamics Simulator (FDS) and real fire experiment. The typical fire scenario caused by cable faults at middle distribution box locations was analyzed to evaluate the effects of ignition position on fire growth and smoke propagation. The FDS results show that the upper-layer temperature reaching approximately 180 °C at 173 s, while visibility at 2 m height decreases to 10 m at 176 s and CO₂ concentration rises to 1%. The CO concentration at 2 m reaches 500 ppm at around 290 s. The calculated Available Safe Egress Time (ASET) of 145 s exceeds the Required Safe Egress Time (RSET) of 117 s, indicating acceptable evacuation safety under this scenario. A full-scale real fire experiment was further conducted under a 5 MW fire. Temperature measurements showed that the thermocouple tree nearest the fire source reached a maximum temperature of approximately 620 °C, posing a severe threat to unprotected steel roof structures. The temperatures below 2 m remained relatively lower, decreasing from about 250 °C to 150 °C. These results demonstrate that the concentrated fire scenario primarily endangers roof load-bearing structures, whereas the thermal conditions in the evacuation zone are comparatively less severe. Full article

Extreme and sudden temperature fluctuations observed as a result of global climate change increase the environmental pressure on energy transmission infrastructure. These meteorological changes significantly increase the risk of failure for porcelain insulators, which exhibit low thermal resistance and are susceptible to sudden arcing and surface deformations. In this study, a hybrid CNN–BiLSTM–SE architecture augmented with the Squeeze-and-Excitation attention mechanism is proposed using surface leakage current signals to diagnose healthy, cracked, and broken structural conditions in three-unit porcelain insulators. The SE block in the architecture dynamically rescales feature maps from CNN layers on a channel-by-channel basis. Thus, it highlights the signal characteristic that is dominant for fault diagnosis just before the BiLSTM units learn temporal dependencies. Leakage current data were obtained under an experimental setup at 60 kV for 15 different conditions covering all possible combinations of healthy, cracked, and broken insulator units. The raw signals were preprocessed with the Savitzky–Golay filter to suppress noise while preserving the diagnostic waveform morphology. 24 features covering time-domain statistics, frequency-domain spectral characteristics, and wavelet-domain energy components were extracted and used as model inputs. The CNN–BiLSTM–SE architecture achieved a classification accuracy of 93.83%, surpassing the standalone CNN (88.89%), BiLSTM (87.65%), and CNN–BiLSTM (91.36%) models, as well as classical machine-learning baselines (SVM: 87.65%, Random Forest: 90.12%, Boosted Trees: 87.65%). Full article

Efficient task assignment for mobile robots is a crucial challenge in modern intralogistics. This paper presents an integrated cyber-physical framework combining predictive tree search on switching max-plus linear systems with a physical IoT-based dispatch interface. The scheduling problem is modelled as a discrete event system, where standard max-plus algebra captures robot synchronization, and a switching mechanism represents alternative resource assignments. To address real-world operational disturbances, the predictive model is enhanced with a fault-tolerant control (FTC) mechanism that dynamically estimates and adapts to non-stationary transport delays. The resulting decision space, which grows exponentially with the prediction horizon, is explored via a predictive tree search algorithm utilizing a quadratic cost function to penalize excessive and uneven transport times. The physical dispatch layer is realized using KIS.BOX IoT devices acting as operator-controlled stations, communicating with the central controller via a WebSocket/STOMP event stream and a lightweight REST API. Simulation results obtained in a Blender 3D environment demonstrate that the proposed FTC predictive strategy significantly reduces the variance of task completion times under fault conditions compared to a baseline First-In-First-Out approach. Furthermore, the IoT integration successfully simulates and validates the feasibility of human-in-the-loop task injection within a realistic, stochastic scenario. Full article

In the era of digital transformation, the operational reliability of hydraulic energy conversion systems is paramount for the overall efficiency of sustainable integrated energy infrastructures. This study evaluates the robustness of machine learning-based fault diagnosis for positive displacement pumps, which are critical components in energy-intensive industrial applications. The research addresses a key challenge: the instability of diagnostic features under varying operational regimes. Using vibration signals from units at three distinct wear levels, we evaluated multiple machine learning architectures, including SVM, KNN, and ensemble trees. Our findings reveal that traditional data-driven models suffer a performance degradation of over 21% when subjected to domain shifts caused by load variability. To mitigate this, we implemented a frequency-domain signal conditioning layer that aligns extracted descriptors with physically meaningful wear phenomena. This enhanced feature representation improved classification accuracy to 93.5% under variable load conditions. The results demonstrate that improving the robustness of diagnostic models is essential for reliable operation, maintenance planning, and energy efficiency of hydraulic energy conversion systems within modern industrial energy infrastructures. Full article

The reliability assessment of power supply systems is complicated by the branched network topology, redundancy, switching operations, post-failure restoration, operating constraints, and uneven equipment degradation. Additional difficulties arise because calculations based on averaged normative parameters often fail to reflect the actual condition of specific components. This review critically compares reliability assessment methods for power supply systems, with particular attention to the technical condition of electrical equipment. Fault tree analysis and the logic–probabilistic method are discussed as structural approaches. Markov models are examined as tools for describing transitions between equipment states. Monte Carlo-based simulation is considered for calculating adequacy deficits and the operational consequences of supply interruptions. Reliability parameterization based on health indices, correction factors, and consumed life is also analyzed. The analysis indicates that no single methodology is equally effective at all problem levels. The main contribution is to separate technical condition parameterization, structural operability modeling, and adequacy assessment into interacting layers. This structure links condition indicators, such as health indices or consumed life, with system-level reliability and deficit indices through element-state probabilities. The review therefore supports a multi-level framework in which technical condition is considered at the element level, structural reliability is evaluated by the logic–probabilistic method, and failure consequences are assessed by simulation. Full article

The safe operation of power battery systems relies on the dynamic symmetric equilibrium of electrochemical distribution and thermal management states, whereas fault occurrence is often accompanied by symmetry breaking. To achieve accurate fault diagnosis and symmetry restoration, this study proposes a symmetrical closed-loop framework (DBP-KG-FBN) that integrates digital battery passport (DBP) text mining, knowledge graph (KG), and fuzzy Bayesian network (FBN). Power battery fault diagnosis is critical to new energy vehicle (NEV) safety; however, conventional methods face two key limitations: (1) they inadequately exploit multi-source heterogeneous textual data in DBPs; and (2) they fail to handle uncertainty in fault propagation. The methodology proceeds as follows. First, a BERT-BiLSTM-CRF model extracts fault-related entities and relations from unstructured DBP text, which are structured into a Neo4j-based knowledge graph. Second, via rule-based topological mapping, the KG topology is transformed into a Bayesian network through structurally symmetric transformation between the semantic and probabilistic layers, with cyclic dependencies resolved by introducing latent variables. Third, network parameters are determined by integrating fuzzy set theory with game theory-based weighting to quantify uncertainty and subjectivity in expert evaluations, thereby achieving symmetric utilization of subjective and objective information. This enables bidirectional symmetric reasoning for forward fault prediction and backward fault traceability. Experimental results demonstrate that while maintaining symmetric stability of the diagnostic knowledge topology, the proposed DBP-KG-FBN method achieves a diagnostic accuracy of 0.92 (Top-3). This symmetrical closed-loop framework significantly outperforms fault tree analysis (FTA) and event tree analysis (ETA) in diagnostic accuracy and reasoning efficiency. It transforms unstructured DBP data into computable knowledge for intelligent battery diagnosis. Future work will expand the corpus via transfer learning and optimize adaptive weighting algorithms for expert evaluations. Full article

Tree contact discharge is a key contributing factor to wildfires caused by medium-voltage insulated conductors. Prolonged abrasion of the insulation layer by branches gradually creates weak points in the insulation. When subjected to lightning strikes, these areas are prone to forming lightning-induced pinholes, which can subsequently trigger partial discharge and even ignition. This study systematically investigates the discharge-induced ignition mechanism for 10 kV overhead insulated conductors in tree contact scenarios by establishing an experimental platform integrated with high-speed imaging, ultraviolet detection, and simulation methods. Three types of typical defects were set up in the experiments: complete insulation abrasion, lightning puncture holes accompanied by localized abrasion, and lightning puncture holes without abrasion. The development process and characteristics of different discharge forms were observed and analyzed. The results indicate that the tree contact discharge ignition mechanism can be categorized into two types: thermal accumulation and direct arcing. The former occurs when insulation abrasion or composite defects exist, where sustained partial discharge or a high-resistance current leads to gradual heat accumulation, resulting in an ignition delay lasting tens of seconds. The latter occurs when only small defects such as lightning puncture holes exist in the insulation layer. A concentrated arc forms due to gap breakdown under high voltage, leading to a millisecond-level ignition process. The study found that different discharge forms produce significantly distinct ablation and carbonization patterns on both the insulation layer and the branch surface, reflecting differences in energy transfer pathways. Simulation analysis further indicated that the thickness of the insulation layer affects the electric field distribution in the tree contact gap, with the initial discharge field strength decreasing as the thickness increases. This study provides experimental evidence and classification guidance for tree contact fault monitoring, insulation condition assessment, and wildfire prevention and control in medium-voltage distribution networks. Full article

Early and reliable fault detection in Brushless Direct Current (BLDC) motors is essential for improving system reliability and reducing unplanned industrial downtime. This study presents a controlled experimental investigation of data-driven machine learning approaches for the classification of multiple common BLDC motor faults. Four representative fault-related indicators were obtained under systematically designed operating conditions, and a consistent feature extraction procedure was applied prior to model development. A comparative evaluation was conducted using Multi-Layer Perceptron (MLP), Support Vector Machines (SVM), k-Nearest Neighbour (kNN), and decision tree-based classifiers. All models were trained and tested on the same dataset using an identical validation protocol to ensure methodological fairness and reproducibility. Performance was assessed through standard classification metrics, enabling a transparent comparison of predictive capability and stability. The results show that the MLP model achieved the highest overall classification accuracy (91.6%), closely followed by SVM (91.4%) and kNN (90.2%). Although the performance differences are moderate, the neural network demonstrated more consistent behaviour in scenarios where fault signatures exhibited overlapping characteristics. These findings suggest that non-linear feature interactions play a significant role in BLDC fault discrimination and can be effectively captured by multi-layer architectures. The study provides a reproducible experimental framework and a balanced performance assessment that may support both academic research and the practical development of intelligent condition monitoring systems for BLDC-driven applications. Full article

While the practical Byzantine fault tolerance (PBFT) consensus algorithm provides excellent theoretical fault tolerance, its performance in practical blockchain applications is often constrained by high communication overhead, especially in scenarios with limited node resources and high mobility, such as Vehicular Ad hoc Networks (VANETs). To address these blockchain-specific limitations without sacrificing the fundamental safety guarantees against arbitrary Byzantine failures, this paper proposes a novel PBFT-optimized consensus algorithm based on a dynamic reputation tree (DRT-PBFT). First, to address the issue of limited storage resources, we propose a block synchronization method based on differentiated storage of reputation values. The lower-reputation nodes retain only “micro-blocks” that contain essential information of the complete block, while the higher-reputation nodes store and synchronize complete blocks, significantly reducing the storage overhead. Second, on the basis of the reputation values, we construct a tree communication topology from the leaf node layer in a bottom-up manner. Messages are transmitted from multiple child nodes to their parent node, resolving the problem of a single message source in the tree structure. Additionally, we optimize the consensus process, reducing the number of mutual communications between nodes to a linear level. Finally, to address the problem of malicious nodes in the tree structure, we introduce a dynamic reconstruction mechanism for the reputation tree. When child node messages are inconsistent, the parent node splits the child nodes to mitigate the influence of malicious nodes, enhancing both the security and scalability of the consensus process. The experimental results show that, compared with typical improved PBFT algorithms, the proposed algorithm improves the average throughput by 34.1% and reduces the average latency by 27.4%. Moreover, compared with the full replication block synchronization method, the differentiated storage method reduces the storage overhead by 26.3%, making it potentially more suitable for large-scale VANET scenarios. Full article

Plateau–orogenic belts host a substantial share of global gold resources, yet quantitative prospectivity mapping is challenged by complex mineralization and strongly heterogeneous, multi-scale datasets. Using the Mayoumu area (Tibet) as a representative orogenic gold district, we develop an integrated multi-source workflow that fuses remote-sensing alteration information with regional geochemical and structural constraints within an ensemble-learning framework. Alteration anomalies were mapped from GF-5 hyperspectral imagery using mixture-tuned matched filtering (MTMF) and from Sentinel-2 multispectral imagery using the iCrosta method to extend alteration signals across scales. Geochemical anomalies were extracted from 1:200,000 stream-sediment data through isometric log-ratio (ILR) transformation and robust principal component analysis (RPCA). At the same time, ore-controlling structures were quantified using Euclidean-distance-to-fault layers. Three Boosting-based ensemble models—gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM)—were trained to predict mineral prospectivity. Performance was evaluated using confusion matrix metrics and ROC–AUC, and key predictors were interpreted using SHAP. All three models achieved AUC values > 0.90, with LightGBM performing best (AUC = 0.94) and delineating high-prospectivity zones that coincide with known occurrences and highlight additional targets. The proposed workflow provides a practical, transferable reference for gold prospectivity mapping in complex orogenic belts worldwide. Full article

Unrecognized mechanical faults and emergency sounds in vehicles can compromise safety, particularly for individuals with hearing impairments and in sound-insulated or autonomous driving environments. As intelligent transportation systems (ITSs) evolve, there is a growing need for inclusive, non-intrusive, and real-time diagnostic solutions that enhance situational awareness and accessibility. This study introduces an interpretable, sound-based machine learning framework to detect vehicle faults and emergency sound events using acoustic signals as a scalable diagnostic source. Three purpose-built datasets were developed: one for vehicular fault detection, another for emergency and environmental sounds, and a third integrating both to reflect real-world ITS acoustic scenarios. Audio data were preprocessed through normalization, resampling, and segmentation and transformed into numerical vectors using Mel-Frequency Cepstral Coefficients (MFCCs), Mel spectrograms, and Chroma features. To ensure performance and interpretability, feature selection was conducted using SHAP (explainability), Boruta (relevance), and ANOVA (statistical significance). A two-phase experimental workflow was implemented: Phase 1 evaluated 15 classical models, identifying ensemble classifiers and multi-layer perceptrons (MLPs) as top performers; Phase 2 applied advanced feature selection to refine model accuracy and transparency. Ensemble models such as Extra Trees, LightGBM, and XGBoost achieved over 91% accuracy and AUC scores exceeding 0.99. SHAP provided model transparency without performance loss, while ANOVA achieved high accuracy with fewer features. The proposed framework enhances accessibility by translating auditory alarms into visual/haptic alerts for hearing-impaired drivers and can be integrated into smart city ITS platforms via roadside monitoring systems. Full article

Lithology identification is fundamental for the logging evaluation of natural gas hydrate reservoirs. The Sanlutian field, located in the permafrost zones of the Qilian Mountains (PZQM), presents unique challenges for lithology identification due to its complex geological features, including fault development, missing and duplicated stratigraphy, and a diverse array of rock types. Conventional methods frequently encounter difficulties in precisely discerning these rock types. This study employs well logging and core data from hydrate boreholes in the region to evaluate the performance of four data-driven machine learning (ML) algorithms for lithological classification: random forest (RF), multi-layer perceptron (MLP), logistic regression (LR), and decision tree (DT). The results indicate that seven principal lithologies—sandstone, siltstone, argillaceous siltstone, silty mudstone, mudstone, oil shale, and coal—can be effectively distinguished through the analysis of logging data. Among the tested models, the random forest algorithm demonstrated superior performance, achieving optimal precision, recall, F1-score, and Jaccard coefficient values of 0.941, 0.941, 0.940, and 0.889, respectively. The models were ranked in the following order based on evaluation criteria: RF > MLP > DT > LR. This research highlights the potential of integrating artificial intelligence with logging data to enhance lithological classification in complex geological settings, providing valuable technical support for the exploration and development of gas hydrate resources. Full article

Unsupervised anomaly detection algorithms have gained significant attention in the field of mineral prospectivity mapping (MPM) due to their ability to reveal hidden mineralization zones by effectively modeling complex, nonlinear relationships between exploration data and mineral deposits. This study utilizes two tree-based anomaly detection algorithms, namely, isolation forest (IF) and extended isolation forest (EIF), to enhance MPM and exploration targeting. According to the conceptual model of porphyry copper deposits, several evidence layers were generated, including fault density, multi-element geochemical signatures, proximity to various alteration types (phyllic, argillic, propylitic, and iron oxide), and proximity to intrusive rocks. These layers were integrated using IF and EIF algorithms, and their results were subsequently compared with a geological map of the study area. The comparison revealed a high degree of overlap between the identified anomalous zones and geological features, such as andesitic rocks, tuffs, rhyolites, pyroclastics, and intrusions. Additionally, quantitative assessments through prediction-area plots validated the efficacy of both models in generating prospective targets. The results highlight the significant influence of hyperparameter tuning on the accuracy of prospectivity models. Furthermore, the study demonstrates that hyperparameter tuning is more intuitive and straightforward in IF, as it provides a clear and distinct tuning pattern, whereas EIF lacks such clarity, complicating the optimization process. Full article

To enhance the efficiency of reusing massive unstructured operation and maintenance (O&M) data generated during routine railway maintenance inspections, this paper proposes a Named Entity Recognition (NER) method that integrates multi-granularity semantics and a Multi-Scale Retention (MSR) mechanism. The proposed approach effectively transforms expert knowledge extracted from manually processed fault data into structured triplet information, enabling the in-depth mining of track circuit O&M text data. Given the specific characteristics of railway domain texts, which include a high prevalence of technical terms, ambiguous entity boundaries, and complex semantics, we first construct a domain-specific lexicon stored in a Trie tree structure. A lexicon adapter is then introduced to incorporate these terms as external knowledge into the base encoding process of RoBERTa-wwm-ext, forming the lexicon-enhanced LE-RoBERTa-wwm model. Subsequently, a hidden feature extractor captures semantic representations from all 12 output layers of LE-RoBERTa-wwm, performing weighted fusion to fully leverage multi-granularity semantic information across encoding layers. Furthermore, in the downstream processing stage, two computational paradigms are designed based on the MSR mechanism and the Regularized Dropout (R-Drop) mechanism, enabling low-cost inference and efficient parallel training. Comparative experiments conducted on the public Resume and Weibo datasets demonstrate that the model achieves F1 scores of 96.75% and 72.06%, respectively. Additional experiments on a track circuit dataset further validate the model’s superior recognition performance and generalization capability. Full article

In recent decades, excessive human activities have led to large-scale rocky desertification in karst areas. Vegetation restoration is one of the most important ways to control rocky desertification. In this study, vegetation surveys were conducted on three typical plantations in Jianshui County, Yunnan Province, a typical karst fault basin area, in 2016 and 2021. The plantations were Pinus massoniana forest (PM), Pinus yunnanensis forest (PY), and mixed forests of Pinus yunnanensis and Quercus variabilis (MF). Plant diversity and soil nutrients were compared during the five-year period. This paper mainly draws the following results: The plant diversity of PM, PY, and MF increased. With the increase of time, new species appeared in the tree layer, shrub layer, and herb layer of the three forests. Tree species with smaller importance values gradually withdrew from the community. In the tree layer, the Patrick index, Simpson index, and Shannon–Wiener index of the three forests increased significantly. The Pielou index changed from the highest for PM in 2016 to the highest for PY in 2021. In the shrub layer, the Pielou index of the three forests increased. The Patrick index changed from the highest for MF in 2016 to the highest for PY in 2021. There was no significant difference in species diversity index for the herb layer. With the increase of vegetation restoration time, the soil bulk density (BD) of the three forests decreased. There was no significant difference in soil total porosity (TP), soil capillary porosity (CP), and non-capillary porosity (NCP). The pH of PM increased significantly from 5.88~6.24 to 7.24~7.34. The pH of PY decreased significantly (p < 0.05). The contents of total nitrogen (TN) and ammonium nitrogen (NH₄⁺-N) in PY and MF decreased. The content of nitrate nitrogen (NO₃⁻-N) in the three forests increased significantly (p < 0.05). Total phosphorus (TP) content decreased in PM and MF. The content of available phosphorus (AP) in PM and PY increased. In general, with the increase of vegetation restoration time, plant diversity and soil physical and chemical properties have also been significantly improved. The results can provide important data support for vegetation restoration in karst areas. Full article