Search Results (1,657)

Abstract: This paper presents a fault-tolerant model predictive control approach for cross-rudder autonomous underwater vehicles to achieve heading control, considering rudder stuck faults and unknown disturbances. Specifically, additive faults in the rudders are addressed, and an active fault-tolerant control strategy is employed. Fault models of autonomous underwater vehicles have been established to develop the fault-tolerant control method. In the controller design, the stuck faults of complete rudder failure are incorporated to ensure the heading angle control of the autonomous underwater vehicle in faulty conditions. Furthermore, the fault term is decoupled from the control input, and the decoupled control input, along with corresponding constraints, is incorporated into the model’s predictive controller design. This approach facilitates controller reconfiguration, thereby enhancing and optimizing control performance. Simulation results demonstrate that the proposed fault-tolerant model predictive control method can effectively achieve stable navigation and heading adjustment under rudder fault conditions in autonomous underwater vehicles. Full article

This paper proposes a transformer temperature early warning method based on an adaptive sliding window and stacking ensemble learning algorithm, aiming to improve the accuracy and robustness of temperature prediction. The transformer temperature early warning system is crucial for ensuring the safe operation of the power system, and temperature prediction, as the foundation of early warning, directly affects the early warning effectiveness. This paper analyzes the characteristics of transformer temperature using support vector regression, random forest, and gradient boosting regression as base learners and ridge regression as the meta-learner to construct a stacking model. At the same time, Bayesian optimization is used to automatically adjust the sliding window size, achieving adaptive sliding window processing. The experimental results indicate that the temperature prediction method based on adaptive sliding window and stacking significantly reduces prediction errors, enhances the model’s adaptability and generalization ability, and provides more reliable technical support for transformer fault warning. Full article

As coal seams are mined at greater depths, the threat of high water pressure from the confined aquifer in the floor that mining operations face has become increasingly prominent. Taking the Madaotou mine field in the Datong Coalfield as the research object, in the context of mining under pressure, for the main coal seams in the mining area, first of all, an improved evaluation method for the vulnerability of floor water inrush is adopted for hazard prediction. Secondly, numerical simulation is used to conduct a simulation analysis on the fault zones in high-risk areas. By using the fuzzy C-means clustering method (FCCM) to improve the classification method for the normalized indicators in the original variable-weight vulnerability evaluation, the risk zoning for water inrush from the coal seam floor is determined. Then, through the numerical simulation method, a simulation analysis is carried out on high-risk areas to simulate the disturbance changes of different mining methods on the fault zones so as to put forward reasonable mining methods. The results show that the classification of the variable-weight intervals of water inrush from the coal seam floor is more suitable to be classified by using fuzzy clustering, thus improving the prediction accuracy. Based on the time effect of the delayed water inrush of faults, different mining methods determine the duration of the disturbance on the fault zones. Therefore, by reducing the disturbance time on the fault zones, the risk of karst water inrush from the floor of the fault zones can be reduced. Through prediction evaluation and simulation analysis, the evaluation of the risk of water inrush in coal mines has been greatly improved, which is of great significance for ensuring the safe and efficient mining of mines. Full article

As the core for energy conversion in pumped storage plants, the pump turbine is also a key component in the process of building a clean power grid, owing to its fast and accurate load regulation. This paper introduces the current status of research and development of pump turbines from the perspectives of significance, design and optimization, operational performance, advanced research methods, etc. Internal and external characteristics such as transient flow evolution, structural vibration, flow-induced noise, etc., not only reflect operational performance (hydraulic, cavitation, sediment abrasion, and stability performance, etc.) but also directly affect the safe and efficient operation of the system. It is worth mentioning that the space-time evolution of internal and external characteristics is an emerging research direction, the results of which can be used to predict the operational conditions of pump turbines. Moreover, the development and application of intelligent condition monitoring and fault diagnosis aim to prevent failures and accidents in pumped storage plants. Full article

Fault fracture zones, characterized by high weathering, low strength, and a high degree of fragmentation, are common adverse geological phenomena encountered in tunneling projects. This paper performed a series of large-scale triaxial compression tests on the cohesive soil–rock mixture (SRM) samples with dimensions of 500 mm × 1000 mm to investigate the influence of rock content P_BV (20, 40, and 60% by volume), rock orientation angle α, and confining pressure on their macro-mechanical properties. Furthermore, a triaxial numerical model, which takes into account P_BV and α, was constructed by means of PFC^3D to investigate the evolution of the mechanical properties of the cohesive SRM. The results indicated that (1) the influence of the α is significant at high confining pressures. For the sample with an α of 0°, shear failure was inhibited, and the rock blocks tended to break more easily, while the samples with an α of 30° and 60° exhibited fewer fragmentations. (2) P_BV significantly affected the shear behaviors of the cohesive SRM. The peak deviatoric stress of the sample with an α of 0° was minimized at lower P_BV (<20%), while both the deformation modulus and peak deviatoric stress were larger at high P_BV (>60%). Based on these findings, an equation correlating shear strength and P_BV was proposed under consistent α and matrix strength conditions. This equation effectively predicts the shear strength of the cohesive SRM with different P_BV values. Full article

Induction motors are essential components in industry due to their efficiency and cost-effectiveness. This study presents an innovative methodology for automatic fault detection by analyzing images generated from the Fourier spectra of current signals using deep learning techniques. A new preprocessing technique incorporating a distinctive background to enhance spectral feature learning is proposed, enabling the detection of four types of faults: healthy motor coupled to a generator with a broken bar (HGB), broken rotor bar (BRB), race bearing fault (RBF), and bearing ball fault (BBF). The dataset was generated from three-phase signals of an induction motor controlled by a Direct Torque Controller under various operating conditions (20–1500 rpm with 0–100% load), resulting in 4251 images. The model, based on a Visual Geometry Group (VGG) architecture with 19 layers, achieved an overall accuracy of 98%, with specific accuracies of 99% for RAF, 100% for BRB, 100% for RBF, and 95% for BBF. A new model interpretability was assessed using explainability techniques, which allowed for the identification of specific learning patterns. This analysis introduces a new approach by demonstrating how different convolutional blocks capture particular features: the first convolutional block captures signal shape, while the second identifies background features. Additionally, distinct convolutional layers were associated with each fault type: layer 9 for RAF, layer 13 for BRB, layer 16 for RBF, and layer 14 for BBF. This methodology offers a scalable solution for predictive maintenance in induction motors, effectively combining signal processing, computer vision, and explainability techniques. Full article

The time period around the Noachian–Hesperian boundary, 3.7 billionyears ago, was an epoch when great geodynamical and environmental changes occurred on Mars. Currently available remote sensing data are crucial for understanding the Martian heat loss pattern and its global thermal state in this transitional period. We here derive surface heat flows in specific locations based on the estimations of the depth of five large thrust faults in order to constrain both surface and mantle heat flows. Then, we use heat-producing element (HPE) abundances mapped from orbital measurements by the Gamma-Ray Spectrometer (GRS) onboard the Mars Odyssey 2001 spacecraft and geographical crustal thickness variations to produce a global model for the surface heat flow. The heat loss contribution of large mantle plumes beneath the Tharsis and Elysium magmatic provinces is also considered in our final model. We thus obtain a map of the heat flow variation across the Martian surface at the Noachian–Hesperian boundary. Our model also predicts an average heat flow between 32 and 50 mW m−2, which implies that the heat loss of Mars at that time was lower than the total radioactive heat production of the planet, which has profound implications for the thermal history of Mars. Full article

This paper presents an enhanced fault prediction framework for synchronous condensers in UHVDC transmission systems, integrating Large Language Models (LLMs) with optimized Wavelet Packet Transform (WPT) for improved diagnostic accuracy. The framework innovatively employs LLMs to automatically optimize WPT parameters, addressing the limitations of traditional manual parameter selection methods. By incorporating a Multi-Head Attention Gated Recurrent Unit (MHA-GRU) network, the system achieves superior temporal feature learning and fault pattern recognition. Through intelligent parameter optimization and advanced feature extraction, the LLM component intelligently selects optimal wavelet decomposition levels and frequency bands, while the MHA-GRU network processes the extracted features for accurate fault classification. Experimental results on a high-capacity synchronous condenser demonstrate the framework’s effectiveness in detecting rotor, air-gap, and stator faults across diverse operational conditions. The system maintains efficient real-time processing capabilities while significantly reducing false alarm rates compared to conventional methods. This comprehensive approach to fault prediction and diagnosis represents a significant advancement in synchronous condenser fault prediction, offering improved accuracy, reduced processing time, and enhanced reliability for UHVDC transmission system maintenance. Full article

In the process of CBM development, the fracturing effect has always been a major controlling factor for CBM productivity. The coal fragmentation degree is a special geological feature in the process of CBM development and research, and other types of reservoirs are not involved in this study. This paper addresses the problem of the inaccurate prediction of the reservoir fragmentation degree by studying the influence of the reservoir type and depth plane curvature on the reservoir fragmentation degree based on the coalbed characteristics of a block. It also studies the influence of faults on the reservoir fragmentation degree based on the reservoir geological characteristics and seismic inversion results. Combined with dynamic data on coalbed methane production, the influence of different geological characteristics on the productivity of coalbed methane wells is studied. The research results show that the reservoir fragmentation degree is mainly affected by the reservoir type. In the coal-forming period or after coal forming, the stronger the tectonic movement is, the higher the reservoir fragmentation degree is. Another manifestation of tectonic movement is faults. The effect of the reservoir fragmentation degree on production is negative. The better the reservoir fragmentation degree is, the worse the reconstruction effect of the coalbed methane well is, and the worse the later production effect is. At the same time, the faults generated by tectonic movement affect not only the reservoir fragmentation degree but also the water production of coalbed methane wells. The closer a well is to a fault, the greater the risk is of high water production and low gas production. Therefore, in the process of selecting a desert area, a complex reservoir fragmentation degree and areas with strong tectonic movement should be avoided. This study takes a structural control block as the research object to study the main controlling factors of coalbed methane reservoir productivity in complex structures. At present, there is no relevant research on this structure in terms of controlling productivity at home or abroad. The research in this paper can provide technical support for the development of similar CBM reservoirs. This method can guide the development of coalbed methane fields and lay a foundation for the selection of favorable coalbed methane reservoir areas. Full article

This article presents the results obtained from a high-resolution wide-azimuthal 3D seismic reflection method used for the prediction and detailed exploration of complex ore targets in the Zhezkazgan ore district of Central Kazakhstan. We demonstrate the ability of modern seismic data processing and interpretation systems to identify underground mine objects associated with stratiform copper sandstones and improve geological models. The 3D seismic imaging tools, along with the implementation of a modern seismic processing sequence, allow for the clarification of geological structures in the studied area. The target stratigraphic horizons, large faults, and microtectonic disturbances (small faults and cracks) are clearly delineated in the seismic volumes. The use of seismic attribute analyses on geological data is tested to identify ore horizons and deposits with volumetric predictions of copper mineralization. Recommendations for further exploration drilling were developed, and five new wells were drilled. Copper mineralization was confirmed in all recommended wells. We carried out a marketing review in Kazakhstan and uncovered an increased interest among subsoil use companies in 3D seismic exploration technology to investigate existing mining objects of different genetic types. These results demonstrate the expediency of 3D seismic exploration aimed at identifying ore targets. Full article

Production management serves as a pivotal component in the operational activities of oilfield sites, with the effectiveness of management practices directly influencing the success of developmental outcomes. To enhance the maintenance-free operational period of oil production systems, elevate management standards, and reduce overall operational costs, advanced technologies such as artificial intelligence (AI) and big data analytics have been strategically integrated into oilfield operations. These technologies are able to incorporate data resources from all stages of oilfield production, thus providing a comprehensive view of oilfield production and guidance for production. This study uses a series of diagnostic and predictive methods to construct a management system that allows for the comprehensive monitoring and fault diagnosis of oil production systems, which can ensure the intelligent management of oil production systems at multiple levels throughout their life cycle. Automated monitoring workflows and proactive analytical processes are at the heart of the framework, enabling real-time monitoring and predictive decision-making. This not only minimizes the likelihood of system failure but also optimizes resource allocation and operational efficiency. Full article

Partial discharge (PD) is a commonly encountered discharge-related fault in transformers. Due to the unique characteristics of the environment where PD occurs, challenges such as difficulty in data acquisition and scarcity of samples arise. Convolutional neural networks (CNNs) are widely used in pattern recognition because of their strong feature extraction capabilities. To improve the recognition accuracy of PD models, this paper integrates CNN, bidirectional long short-term memory (BiLSTM), and an attention mechanism. In the proposed model, CNN is employed to extract local spatial and temporal features, BiLSTM is utilized to extract global bidirectional spatial and temporal features, and the attention mechanism assigns adaptive weights to the features. Additionally, to address the issues of sample scarcity and data imbalance, an improved GAN is introduced to augment the data. The experimental results demonstrate that the CNN-BiLSTM-attention method proposed in this paper significantly improves the prediction accuracy. With the help of GAN, the proposed method achieves a recognition accuracy of 97.36%, which is 1.8% higher than that of the CNN+CGAN(Conditional Generative Adversarial Network) method and 5.8% higher than that of thetraditional recognition model, SVM, making it the best-performing method among several comparable methods. Full article

This work introduces an end-to-end methodology, from data gathering to fault notification, for the predictive maintenance of rotary components of machine tools. This is done through fingerprint routines; that is, processes that are executed periodically under the same no-load conditions to obtain a snapshot of the machine condition. High-frequency vibration data gathered during these routines combined with knowledge about the machine structure and its components are used to obtain failure-specific features. These features are then introduced to an anomaly and paradigm shifts detection algorithm. The method is evaluated through three distinct scenarios. First, we use synthetically generated data to test its ability to detect controlled variations and edge cases. Second, we use with publicly available data obtained from bearing run-to-failure tests under normal load conditions on a specially designed test rig. Finally, the methodology is validated using real-world data collected from a spindle bearing installed in a machine tool. The novelty of this work lies in performing anomaly detection using failure-specific features derived from fingerprint routines, ensuring stability over time and enabling precise identification of machine conditions with minimal data requirements. Full article

The most effective way to solve urban traffic congestion in mega cities is to develop rail transit, which is also an important strategy for sustainable urban development. Improving the service performance of rail transit equipment is the key to ensuring the sustainable operation of urban rail transit. Automatic fare collection (AFC) is an indispensable system in urban rail transit. AFC directly serves passengers, and its condition directly affects the sustainability and safety of urban rail transit. This study proposes remaining useful life (RUL) prediction framework for AFC systems. Firstly, it proposes the quantification of AFC health state based on health degree, and proposes a health state assessment method based on digital analog fusion, which compensates for the shortcomings of single data-driven or model driven health methods. Secondly, it constructs a multi feature extraction method based on multi-layer LSTM, which can capture long-term temporal dependencies and multi-dimensional feature, overcoming the limitation of low model accuracy because of the weak data features. Then, the SSA-XGBoost model for AFC RUL prediction is proposed, which effectively performs global and local searches, reduces the possibility of overfitting, and improves the accuracy of the prediction model. Finally, we put it into practice of the AFC system of Shanghai Metro Line 10. The experiment shows that the proposed model has an MSE of 0.00111 and MAE of 0.02869 on the test set, while on the validation set, MSE is 0.00004 and MAE is 0.00659. These indicators are significantly better than other comparative models such as XGBoost, random forest regression, and linear regression. In addition, the SSA-XGBoost model also performs well on R-squared, further verifying its effectiveness in prediction accuracy and model fitting. Full article

In this study, a predictive maintenance (PdM) system focused on feature selection for the detection and classification of simulated defects in wind turbine blades has been developed. Traditional PdM systems often rely on numerous, broadly chosen diagnostic indicators derived from vibration data, yet many of these features offer little added value and may even degrade model performance. General feature selection methods might not be suitable for PdM solutions, as information regarding observed faults is often misinterpreted or lost. To address these issues, a structured feature selection method based on correlation analysis supplemented with comprehensive visual evaluation was proposed. Unlike generic dimensionality reduction techniques, this approach preserves critical domain-specific information and avoids misinterpretation of fault indicators. By applying the proposed method, it was possible to successfully filter out redundant features, enabling simpler machine learning (ML) models to match or even surpass the performance of more complex deep learning (DL) architectures. The best results were achieved by a deep neural network trained on the full dataset, with accuracy, precision, recall, and F1 score of 97.30%, 97.23%, 97.23%, and 97.23%, respectively, while the top-performing ML model (a voting classifier trained on the reduced dataset) attained scores of 97.13%, 96.99%, 96.95%, and 96.94%. The proposed method for reducing condition indicators successfully decreased their number by approximately 3.27 times, simultaneously significantly reducing computational time of prediction, reaching up to 50% reduction for complex models. In doing so, we lowered computational demands and improved classification efficiency without compromising accuracy for ML models. Although feature reduction did not similarly benefit the metrics for DL models, these findings highlight that well-chosen, domain-relevant condition indicators can streamline data input and deliver interpretable, cost-effective PdM solutions suitable for industrial applications. Full article