Search Results (235)

Accurate gear fault diagnosis under variable operating conditions remains challenging because vibration signals are affected by noise, speed-load variations, and condition-dependent feature shifts. To address these issues, this study proposes a gear fault diagnosis framework that integrates multidomain vibration feature fusion with a back-propagation neural network optimized by the sparrow search algorithm (SSA-BP). Vibration signals collected from a planetary gearbox fault-implantation platform were used to identify seven health states, including normal condition, sun gear pitting, sun gear fracture, sun gear wear, planetary gear pitting, planetary gear fracture, and planetary gear wear. For each signal segment, a 20-dimensional feature vector was constructed by combining nine time-domain features, three frequency-domain features, and eight wavelet packet energy features. SSA was employed to optimize the initial weights and biases of a double-hidden-layer BP neural network before supervised training. Experimental results show that the proposed feature fusion scheme achieved a classification accuracy of 98.30%, outperforming single-domain and pairwise feature combinations. In overall fault classification, SSA-BP obtained 98.26% accuracy, 98.26% macro-recall, 98.27% macro-precision, and 98.26% macro-F1. Moreover, SSA-BP reduced the convergence iterations from 826 to 312 compared with traditional BP and maintained 95.18% accuracy under high-speed and high-load conditions with scarce training samples. These results demonstrate that the proposed SSA-BP model provides improved convergence efficiency, diagnostic accuracy, and cross-condition robustness for intelligent gearbox condition monitoring. Full article

The Tonglushan ore field is an important component of the polymetallic mineralization belt in the middle and lower reaches of the Yangtze River in China. The skarn-type Cu, Fe, Au, and Mo molybdenum deposits are mainly developed in the contact zone between the rock mass and the strata, as well as in the contact zone between residual and capturing bodies in the rock body. The distribution of ore bodies is controlled by faults and strata, but there is a lack of large-scale geophysical information on the contact relationship between the ore-forming geological body and the host rock and on the deep spatial morphology of the ore-forming structure and intrusion rock. The study uses the JS-WEM2 wide-field electromagnetic instrument and the RS230 spectrometer to conduct the ground frequency domain electromagnetic and radiometric spectrometry measurements on four profiles. The measurement results indicate that the fault distribution in the Tonglushan ore field is predominantly in the NW-trending and NE-trending directions. The NW-trending Tonglushan–Lijiashan fault (F2) is a steeply dipping fault; the NE-trending faults are minor, with steep dips, generally extending no deeper than −1000 m. The Tonglushan stock exhibits the northeastward uplift, characterized by southward overlap and southeastward dip. The deep resistivity is greater than 3000 Ω·m, while the resistivity below −1000 m is less than 2000 Ω·m due to the fault influence. The ore bodies are mainly distributed along the contact zones where variations in the occurrence of the rock intersect with the strata. On resistivity profiles, these zones show the gradient variation in resistivity and the distorted shape of the resistivity contour line. The radioactive element contents of wall rock above the ore bodies are characterized by high U, high Th, and low K. The Wide-Field Electromagnetic Method (WFEM) can effectively detect the distribution and morphology of rocks and faults, and combined with the radioactive characteristics of geological bodies, it can effectively identify concealed faults and the favorable mineralization target areas. Novelty: The study combines the WFEM with radiometric measurements to reduce uncertainty in exploration compared to using only one method. It improves the detection accuracy and target identification ability of deep hidden ore bodies, providing the new technical method for deep mineral exploration in complex structural areas. Full article

This paper introduces a cutting-edge, real-time fault detection and classification method powered by artificial neural networks (ANNs), designed to significantly boost the reliability and sustainability of medium voltage (MV) underground cable distribution systems. The research analyzes the electrical and physical properties of MV underground cables and common fault types, including line-to-line, line-to-ground, and double line-to-ground faults. A simulation model is developed using MATLAB/Simulink R2025b to generate fault scenarios under various operating conditions. Raw data in the form of Voltage and current signals are generated and processed to extract significant features, which are then fed into the ANN model. The ANN is trained using a supervised learning approach, using a dataset of labeled fault instances. Key parameters like hidden layers, activation functions, and learning rates are optimized to improve the model’s performance. The results show that the proposed ANN-based fault detection technique achieves over 95% accuracy in detecting and classifying faults in real-time, with minimal computational delay. Comparative analysis with conventional fault classification techniques demonstrates the superiority of the ANN model in handling noisy and non-linear data. Full article

With the transition to deep coal mining, the transparent detection of hidden geological hazards in the floor strata is fundamental for production safety. In mine seismic exploration, gliding waves—inhomogeneous plane waves propagating along the coal–rock interface—offer a unique advantage for penetrating high-velocity floors via the skin effect, overcoming the total reflection limitations of conventional in-seam waves. This study investigates the propagation laws and anomaly response characteristics of floor gliding waves using super-critical incidence theory and high-order staggered-grid finite difference simulations. The results demonstrate that the apparent velocities of gliding P and S-waves are bounded by those of the coal and host rock, exhibiting minimal dispersion. Quantitative analysis using a penetration depth model reveals that while penetration depth is frequency-dependent—with lower frequencies providing deeper reach—high-frequency components remain essential for high-resolution imaging. Crucially, the proposed method was validated through a field Case Study at the 11123 working face. By utilizing a specialized deep-hole excitation strategy to ensure super-critical incidence, the inversion successfully identified a hidden fault extending up to 60 m below the floor, which was subsequently confirmed by rock roadway excavation. These findings establish a robust physical basis for designing underground floor-detection systems and provide a significant theoretical reference for addressing detection blind spots in deep mining environments. Full article

The M_L4.9 Wang Nua earthquake on 20 February 2019 is the largest earthquake to occur in Lampang Province in the past four decades and identifies the potential seismic hazard of previously unmapped faults in northern Thailand. We reanalyzed this earthquake sequence using waveform-based matched-filter detection and double-difference relocation techniques. The enhanced catalog increases the number of small earthquakes by 2.5 times compared with the official record. It also reveals microearthquakes down to M_L–0.3, including a previously unreported foreshock sequence beginning approximately four hours before the mainshock. Relocated hypocenters define an 8 km long, near-vertical N-S striking rupture zone at depths of 0.7–10.6 km. The focal mechanism of the mainshock indicates right-lateral strike-slip motion (strike ~189°, dip ~77°, rake ~–150°), aligned with the kinematics of other extensions of the Phayao Fault Zone. These results indicate that the sequence occurred on a previously unrecognized fault segment. This highlights the importance of high-resolution seismic analysis for improving hazard assessment in regions with concealed fault systems. Full article

The increasing reliance on smart grid communication systems has significantly raised the demand for robust cybersecurity measures to defend against advanced threats. This paper proposes a two-tier classification framework to enhance the detection and diagnosis of man-in-the-middle attacks within smart grid communication protocols. Initially, the model detects the presence of an attack and then identifies the specific type of man-in-the-middle attack through subsequent inferences. To achieve this, the “Man-in-the-Middle Attacks Targeting Modbus TCP/IP and MMS Protocols in the Smart Grid” dataset was carefully preprocessed and analyzed to better understand the underlying hidden characteristics. This understanding, coupled with existing works on fault detection and diagnosis, facilitated the engineering of new features from the original dataset. Four classifiers were employed in each tier: Random Forest, XGBoost, LightGBM, and CatBoost. The first tier exhibited exceptional performance, with the CatBoost framework achieving 99.6% accuracy. The second tier also demonstrated strong results, with the same model achieving 99.1% accuracy. Systematic model explainability was conducted using SHapley Additive exPlanations for both tiers and revealed that the highest accuracy was achieved using five features for the first and six for the second. The average inference time was approximately 4.76 milliseconds. The proposed framework is accurate, fast, interpretable, lightweight, and well-optimized for direct implementation in smart grid systems to detect and diagnose man-in-the-middle attacks. Full article

High-quality process data are essential for modern manufacturing processes to enable advanced control techniques, fault detection, and predictive maintenance. However, real-world industrial datasets often contain missing values due to sensor failures, power outages, and equipment maintenance. This paper proposes a novel implicit–explicit diffusion model that jointly utilizes both hidden and visible properties for industrial data imputation. The model employs a dual-branch architecture: one branch uses multi-scale dilated causal convolutions to capture hierarchical periodic patterns inherent in industrial time series, while the other branch leverages structured state space (S4) models to learn long-term dependencies. A gated fusion mechanism adaptively combines these complementary representations. Extensive experiments on Debutanizer and Sulfur Recovery Unit (SRU) datasets demonstrate that the proposed method achieves the best root mean squared error (RMSE) across all tested missing rates (20–80%) on both datasets, and exhibits particularly strong advantages in high-missingness scenarios (60–80%), where the proposed multi-scale and long-range modeling capabilities prove most beneficial. Full article

A water company’s water supply network is responsible for distributing good-quality water in quantities that meet customer needs, ensuring proper operation of the water supply network to ensure adequate pressure at the receiving points, efficiently repairing faults, and planning and executing maintenance, modernization, and expansion work. Managing a water supply network is a complex and complex process. A crucial challenge in water company management is detecting and locating hidden water leaks in the water supply network. Leak location in water distribution networks is a key challenge for utilities, as undetected leaks lead to water losses, increased energy consumption, and reduced service reliability. With the development of cyber-physical systems (CPSs), the integration of physical infrastructure with real-time digital monitoring has enabled more adaptive and responsive water operations. Data-driven decision-making in CPS in the water industry leverages classification and regression trees (C&RTs) to analyze real-time sensor data—such as pressure, flow, and consumption—to classify system states and predict potential faults. By transforming operational data into interpretable decision rules, C&RTs enable automated and timely maintenance actions that improve reliability, reduce water loss, and support intelligent infrastructure management. The aim of this study is to develop and evaluate AI-based optimization methods to enhance sustainability, efficiency, and resilience in the water industry by enabling autonomous, data-driven decision-making within CPSs, supporting smart industry practices, and addressing practical challenges associated with the actual implementation of smart water management solutions using simple solutions such as C&RTs. The accuracy of the best classifier was 86.15%. Further research will focus on using other types of decision trees that will improve classification accuracy. Full article

The safety and stability of underground coal mining are largely determined by the structural features of coal seams and surrounding rocks. Geological heterogeneities such as faults, fracture zones, and lithological variations strongly influence the distribution of rock pressure and the occurrence of geodynamic hazards. This highlights the need for reliable geophysical methods capable of identifying such zones under mining conditions. Electrical prospecting represents a promising diagnostic approach, as it is highly sensitive to changes in the physical properties of rocks. Unlike conventional geological mapping, it enables the detection of hidden structures and weakened zones often invisible to direct observation. Advances in instrumentation and data processing have further expanded the applicability of electrical methods in complex environments. This study introduces a methodology of electrical prospecting observations for the diagnosis of coal seams. The analysis focuses on conductivity anomalies that reflect tectonic disturbances, fracture systems, and lithological heterogeneities. Field investigations demonstrated the sensitivity of the method to local environmental variations. Comparison with geological records confirmed the validity of the approach: the identified anomalous zones correlated well with documented tectonic features. The methodology showed a stable performance and revealed potential for integration into mine monitoring systems. It allows the identification of areas associated with elevated rock pressure and possible geodynamic activity, thereby contributing to safer underground operations. In the longer term, electrical prospecting may be applied to other coal deposits, including those with a high gas content and complex structure. The development of automated interpretation tools and machine learning algorithms could further increase processing efficiency and improve predictive reliability. Overall, the results confirm that electrical prospecting in mining environments can become an effective instrument for enhancing safety and building more accurate geological–geophysical models of coal seams. Full article

The operational reliability of aero-engines is critically dependent on the health of rolling element bearings, while incipient fault diagnosis remains particularly challenging under small-sample conditions. Although multiscale entropy methods are widely used for complexity analysis, conventional coarse-graining strategies suffer from severe information loss and unstable estimation when data are extremely limited. To address this, the primary objective of this study is to develop a robust diagnostic framework that ensures feature consistency and classification stability even with minimal training samples. Specifically, this paper proposes an integrated approach combining Refined Time-shifted Multiscale Rating Entropy (RTSMRaE) with an Animated Oat Optimization (AOO)-optimized Extreme Learning Machine (ELM). By introducing a refined time-shift operator and a dual-weight fusion mechanism, RTSMRaE effectively preserves transient impulsive features across multiple scales while suppressing stochastic fluctuations. Meanwhile, the AOO algorithm is employed to optimize the input weights and hidden biases of the ELM, alleviating performance instability caused by random initialization and improving generalization capability. Experimental validation on both laboratory-scale and real-world aviation bearing datasets demonstrates that the proposed RTSMRaE-AOO-ELM framework achieves a diagnostic accuracy of 99.47% with a standard deviation of ±0.48% using only five training samples per class. These results indicate that the proposed method offers superior diagnostic robustness and computational efficiency, providing a promising solution for intelligent condition monitoring in data-scarce industrial environments. Full article

Uranium production tailing ponds in Kamyanske (Ukraine) are objects of increased radioecological danger. Violation of the stability and integrity of containment dams threatens the uncontrolled spread of radionuclides. The purpose of this study is to comprehensively assess the factors affecting the technical condition and environmental safety of the Sukhachivske tailing dam. The study included a visual inspection and detailed geophysical work using the natural pulse electromagnetic field of the Earth (NPEMFE) method. This method was chosen to identify hidden filtration paths and stress zones in the body of the earth dam. An analysis of the spatial distribution of waterlogging, filtration, and fissuring in the hydraulic structure was performed. Based on the results of the NPEMFE survey, six zones with varying degrees of waterlogging and stress–strain states of the structure were identified. The presence of externally unmanifested filtration paths and suffusion areas was established, and a tectonic scheme of fracture development in the dam body was compiled. A correlation was found between the dominant azimuths of crack extension (70–79° and 350–359°) and the directions of regional tectonic lineament zones, at the intersection of which the tailing pond is located. It has been established that modern tectonic movements along fault zones create zones of permeability, which serve as primary pathways for water filtration and further development of suffusion. This conclusion introduces a new tectonic feature for risk diagnosis and monitoring of similar hydraulic structures. Full article

Modern industrial equipment is a cyber-physical system (CPS) consisting of physical production components and digital controls. Lowering maintenance costs and increasing availability is important to improve its efficiency. Modern methods, based on solving event prediction problem, in particular, prediction of remaining useful life (RUL), are used as a crucial step in a framework of reliability-centered maintenance to increase efficiency. But modern methods of RUL forecasting fall short when dealing with real-world scenarios, where CPS are described by multidimensional continuous high-frequency data with working cycles with variable duration. To overcome this problem, we propose a new method for fault prediction, which is based on extraction of semantic state vectors (SSVs) from working cycles of equipment. To implement SSV extraction, a new method, based on convolutional autoencoder and extraction of hidden state, is proposed. In this method, working cycles are detected in input data stream, and then they are converted to images, on which an autoencoder is trained. The output of an intermediate layer of an autoencoder is extracted and processed into SSVs. SSVs are then combined into a time series on which RUL is forecasted. After optimization of hyperparameters, the proposed method shows the following results: RMSE = 1.799, MAE = 1.374. These values are significantly more accurate than those obtained using existing methods: RMSE = 14.02 and MAE = 10.71. Therefore, SSV extraction is a viable technique for forecasting RUL. Full article

Heating, ventilation, and air conditioning (HVAC) systems account for a significant portion of building energy consumption and play a crucial role in maintaining indoor comfort. However, hidden faults in air-handling units (AHUs) often lead to energy waste and degraded performance, highlighting the importance of reliable fault detection and diagnosis (FDD). This study proposes a simulation-driven FDD framework that integrates a standardized prototype dataset and an independent evaluation dataset generated from a calibrated EnergyPlus model representing a target facility, enabling controlled experimentation and transfer evaluation within simulation environments. Training data were generated from the DOE EnergyPlus Medium Office prototype model, while evaluation data were obtained from a calibrated building-specific EnergyPlus model of a research facility operated by Company H in Korea. Three representative fault scenarios—outdoor air damper stuck closed, cooling coil fouling (65% capacity), and air filter fouling (30% pressure drop)—were systematically implemented. A Deep Belief Network (DBN) classifier was developed and optimized through a two-stage hyperparameter tuning strategy, resulting in a three-layer architecture (256–128–64 nodes) with dropout and regularization for robustness. The optimized DBN achieved diagnostic accuracies of 92.4% for the damper fault, 98.7% for coil fouling, and 95.9% for filter fouling. These results confirm the effectiveness of combining simulation-based dataset generation with advanced deep learning methods for HVAC fault diagnosis. The results indicate that a DBN trained on a standardized EnergyPlus prototype can transfer to a second, independently calibrated EnergyPlus building model when AHU topology, control logic, and monitored variables are aligned. This study should be interpreted as a simulation-based proof-of-concept, motivating future validation with field BMS data and more diverse fault scenarios. Full article

The Datangpo-type Mn ore deposits in northeastern Guizhou (southern China) are a relatively newly discovered type of sedimentary exhalative manganese ore deposit. Previous three-dimensional geological modeling has revealed an NW-trending trough-like depression that obliquely intersects the ENE-trending Nanhua Rift within the Nanhua System in this area. This depression likely represents a paleorift that was present before the metallogenetic period; its intersection with the Nanhua Rift corresponds precisely with the area in which a series of super-large and large new-type Mn ore deposits are located. Here, we used remote sensing image processing techniques, along with hierarchical spatial data fusion and mining methods adopted for exploration, to investigate this paleorift. Specifically, Bouguer gravity data were used to obtain middle–lower-crust structural information; aeromagnetic ΔT data were used to obtain middle–upper-crust structural information; and remote sensing and outcrop data coupled with regional geological survey, mineral exploration, and geochemical exploration data were used to obtain near-surface structural information. Combining these data, we determined the control that different deep tectonic frameworks exert on the formation and distribution of Mn ore deposits within the study area. This study proposes a new conceptual method and technical protocol permitting an improved understanding of the material source and mineralization pattern of Mn ore deposits within the study area, while verifying the existence of the NW-trending Tongren Paleorift. Full article

In nuclear power plant fault diagnosis, traditional machine learning models (e.g., SVM and KNN) require full retraining on the entire dataset whenever new fault categories are introduced, resulting in prohibitive computational overhead. Deep learning models, on the other hand, are prone to catastrophic forgetting under incremental learning settings, making it difficult to simultaneously preserve recognition performance on both old and newly added classes. In addition, nuclear power plant fault data typically exhibit significant class imbalance, further constraining model performance. To address these issues, this study employs SHAP-XGBoost to construct a feature evaluation system, enabling feature extraction and interpretable analysis on the NPPAD simulation dataset, thereby enhancing the model’s capability to learn new features. To mitigate insufficient temporal feature capture and sample imbalance among incremental classes, we propose a cascaded spatiotemporal feature extraction network: LSTM is used to capture local dependencies, and its hidden states are passed as position-aware inputs to a Transformer for modeling global relationships, thus alleviating Transformer overfitting on short sequences. By further integrating frequency-domain analysis, an improved Adaptive Time–Frequency Network (ATFNet) is developed to enhance the robustness of discriminating complex fault patterns. Experimental results show that the proposed method achieves an average accuracy of 91.36% across five incremental learning stages, representing an improvement of approximately 20.7% over baseline models, effectively mitigating the problem of catastrophic forgetting. Full article