Search Results (1,632)

As one of the key components in rotating machinery, rolling bearings have a crucial impact on the safety and efficiency of production. Acoustic signal is a commonly used method in the field of mechanical fault diagnosis, but an overlapping phenomenon occurs very easily, which affects the diagnostic accuracy. Therefore, effective blind source separation and noise reduction of the acoustic signals generated between different devices is the key to bearing fault diagnosis using acoustic signals. To this end, this paper proposes a blind source separation method based on an AFSA-FastICA (Artificial Fish Swarm Algorithm, AFSA). Firstly, the foraging and clustering characteristics of the AFSA algorithm are utilized to perform global optimization on the aliasing matrix W, and then inverse transformation is performed on the global optimal solution W, to obtain a preliminary estimate of the source signal. Secondly, the estimated source signal is subjected to CEEMD noise reduction, and after obtaining the modal components of each order, the number of interrelationships is used as a constraint on the modal components, and signal reconstruction is performed. Finally, the signal is subjected to frequency domain feature extraction and bearing fault diagnosis. The experimental results indicate that,  the new method successfully captures three fault characteristic frequencies (1f_i, 2f_i, and 3f_i), with their energy distribution concentrated in the range of 78.9 Hz to 228.7 Hz, indicative of inner race faults. Similarly, when comparingthe diffent results with each other, the denoised source signal spectrum successfully captures the frequencies 1f_o, 2f_o, and 3f_o and their sideband components, which are characteristic of outer race faults. The sideband components generated in the above spectra are preliminarily judged to be caused by impacts between the fault location and nearby components, resulting in modulated frequency bands where the modulation frequency corresponds to the rotational frequency and its harmonics. Experiments show that the method can effectively diagnose the bearing faults. Full article

The inherent complexity of modern software frequently leads to critical issues such as defects, performance degradation, and system failures. Among these, system crashes pose a severe threat to reliability, as they demand rapid fault localization to minimize downtime and restore functionality. A critical step of fault localization is predicting the residence of crashing faults, which involves determining whether a fault is located within the stack trace or outside it. This task plays a crucial role in software quality assurance by enhancing debugging efficiency and reducing testing costs. This study introduces SCM, a two-stage composite feature selection framework designed to address this challenge. The SCM framework integrates spectral clustering for feature grouping, which organizes highly correlated features into clusters while reducing redundancy and capturing non-linear relationships. Maximal information coefficient analysis is then applied to rank features within each cluster and select the most relevant ones, forming an optimized feature subset. A decision tree classifier is then applied to predict the residence of crashing faults. Extensive experiments on seven open-source software projects show that the SCM framework outperforms seven baseline methods, which include four classifiers and three ranking approaches, across four evaluation metrics such as F-measure, g-mean, MCC, and AUC. These results highlight its potential in improving fault localization. Full article

The UNESCO World Heritage Centre announced in 2011 that the Wadi Rum Protected Area (WRPA) is a global landmark for natural and cultural attraction, which represents an emerging industrial suburban and a critical socio-economic significance to the country of Jordan. The study area in Wadi Rum is located northeast of the Gulf of Aqaba between the African and Arabian plates. The region is historically characterized by significant tectonic activity and seismic events. This study focuses on characterizing the subsurface structural features of Wadi Rum through the application of the geophysical method of controlled-source audio-frequency magnetotellurics (CSAMT). CSAMT data were collected from 16 sounding stations, processed, and qualitatively interpreted. The qualitative interpretation involved two main approaches: constructing sounding curves for each station and generating apparent resistivity maps at fixed depths (frequencies). The results revealed the presence of at least four distinct subsurface layers. The surface layer exhibited relatively low resistivity values (<200 Ω·m), corresponding to alluvial and wadi sediments, as well as mud flats. Two intermediate layers were identified: the first showed very low resistivity values (80–100 Ω·m), likely due to medium-grained bedded sandstone, while the second displayed intermediate resistivity values (100–800 Ω·m), representing coarse basal conglomerates and coarse sandstone formations. The deepest layer demonstrated very high resistivity values (>1000 Ω·m), which were likely attributed to basement rocks. Analysis of resistivity maps, combined with prior geological information, indicates that the subsurface in the study area features a graben-like structure, characterized by two detected faults trending in the northeast (NE) and southwest (SW) directions. The findings of this study, by providing critical insights into the subsurface structure, make a considerable contribution to the urban sustainability of the region, which is necessary for the careful assessment of potential hazards and the strategic planning of future infrastructure development within the protected area. Full article

The Xiangluwanzi gold deposit, located in the southern Jilin Province of Northeast China, is hosted within the Jurassic Guosong Formation, and surrounded by Archean granitoids. The ore bodies are governed by near-EW and NE-trending faults. Four alteration/mineralization stages have been distinguished: I, pyrite–sericite–quartz; II, gold–pyrite–quartz; III, sphalerite–quartz–carbonate; and IV, quartz–carbonate. Four types of fluid inclusions (FIs) were identified: pure CO₂, CO₂-rich, CO₂-bearing, and NaCl–H₂O fluid inclusions. Stage-I quartz veins contain all FIs, whereas stage II quartz veins host CO₂-rich, CO₂-bearing, and NaCl-H₂O FIs. Only NaCl–H₂O FIs were present in stages-III and -IV quartz veins. The homogenization temperatures of the FIs range, respectively, from 233 to 279, 185–242, 171–217, and 148–170 °C in stages I–IV, having salinities of 2.62–8.54, 2.81–7.58, 4.32–6.58, and 3.37–5.25 wt% NaCl equivalents, respectively. The H (−93.5‰ to −75.9‰) and O (^δ18O_H2O = −5.8‰ to 4.6‰) isotopic compositions suggest magmatic water was gradually diluted by meteoric water. Carbon isotopic values (22.8‰ to −17.8‰) suggest the incorporation of organic carbon from surrounding strata via water–rock interactions. Fluid boiling, fluid mixing, and water–rock interactions are the primary mechanisms driving mineral precipitation. Full article

Different locations and types of faults affect the safe and reliable operation of DC microgrids. Therefore, this paper proposes a secondary multiple fault-tolerant control scheme for a DC microgrid based on a sliding mode observer to ensure the voltage is restored to the rated value and realize the proportional current sharing of all sources. Firstly, the secondary control model of the DC microgrid is established, considering the multiple faults of actuators and sensors simultaneously. Secondly, the system model is transformed into two subsystems by bilinear coordinate transformation, and multiple faults decoupling between the sensor and actuator is realized. Then, two sliding mode observers are designed for the two transformed subsystems. The sliding mode variable structure equivalent principle is used to reconstruct the faults at different positions without knowing the fault models in advance, which is convenient for subsequent processing. Then, the fault-tolerant controller based on the sliding mode observer is designed, which uses the reconstructed value to offset the influence of sensor and actuator faults on the DC microgrid and realizes the fault-tolerant control of the DC microgrid. Finally, the effectiveness of the proposed control strategy is verified by experiments. Full article

The growing demand for electricity, integration of renewable energy sources, and recent advances in power electronics have driven the development of HVDC systems. Multi-terminal HVDC (MTDC) grids, enabled by Voltage Source Converters (VSCs), provide increased operational flexibility, including the ability to reverse power flow and independently control both active and reactive power. However, fault propagation in DC grids occurs more rapidly, potentially leading to significant damage within milliseconds. Unlike AC systems, HVDC systems lack natural zero-crossing points, making fault isolation more complex. This paper presents the implementation of a wavelet-based protection algorithm to detect faults in a four-terminal VSC-HVDC grid, modelled in MATLAB and SIMULINK. The study considers several fault scenarios, including two internal DC pole-to-ground faults, an external DC fault in the load branch, and an external AC fault outside the protected area. The discrete wavelet transform, using Symlet decomposition, is applied to classify faults based on the wavelet entropy and sharp voltage and current signal variations. The algorithm processes the decomposition coefficients to differentiate between internal and external faults, triggering appropriate relay actions. Key factors influencing the algorithm’s performance include system complexity, fault location, and threshold settings. The suggested algorithm’s reliability and suitability are demonstrated by the real-time implementation. The results confirmed the precise fault detection, with fault currents aligning with the values in offline models. The internal faults exhibit more entropy than external faults. Results demonstrate the algorithm’s effectiveness in detecting faults rapidly and accurately. These outcomes confirm the algorithm’s suitability for a real-time environment. Full article

Nowadays, renewable energy facilities are coming to the forefront in order to protect nature and prevent climate change. In this context, location-based analyses are carried out for the most optimal use of renewable energy resources. This study aims to identify suitable locations for photovoltaic (PV) farms in Gaziantep using the Analytical Hierarchy Process (AHP) and Geographic Information System (GIS) technologies. The research incorporates various criteria, including solar radiation, land use, slope, aspect, distance to road, fault line proximity, distance to powerlines, and wind speed to evaluate potential sites for solar energy production. The AHP method is applied to prioritize these criteria through a pairwise comparison matrix and to calculate the weight values for each factor. The analysis reveals that approximately 80% of Gaziantep’s land is suitable for PV farm installation, with the southern region being the most favorable. Furthermore, the comparison between existing PV installations and the identified suitable areas highlights a high degree of alignment, with most of the current PV farms located in areas classified as suitable or highly suitable. Additionally, it was determined that 92% of the existing PV farms have been established within suitable areas. This indicates a high alignment between the identified suitable zones and the locations of the current PV installations, reflecting an effective site selection process based on the applied criteria. The study concludes that GIS-based AHP is an effective tool for rapid and reliable decision-making in renewable energy site selection, offering a valuable approach for future solar energy projects in Gaziantep and similar regions. Full article

In the process of diagnosing the inter-turn short circuit fault of the joint permanent magnet synchronous motor of an industrial robot, due to the small and sparse fault sample data, it is easy to misdiagnose, and it is difficult to quickly and accurately evaluate the fault degree, lock the fault location, and track the fault causes. A multi-task causal knowledge fault diagnosis method for inter-turn short circuits of permanent magnet synchronous motors based on meta-learning is proposed. Firstly, the variation of parameters under the motor’s inter-turn short circuit fault is thoroughly investigated, and the fault characteristic quantity is selected. Comprehensive simulations are conducted using Simulink, Simplorer, and Maxwell to generate data under different inter-turn short circuit fault states; meanwhile, the sample data are accurately labeled. Secondly, the sample data are introduced into the learning network for training, and the multi-task synchronous diagnosis of the fault degree and position of the short circuit between turns is realized. Finally, the Neo4j database based on causality knowledge of motor inter-turn short circuit fault is constructed. Experiments show that this method can diagnose the fault location, fault degree, and fault cause of the motor with different voltage unbalanced degrees. The diagnosis accuracy of fault degree is 99.75 ± 0.25%, and the diagnosis accuracy of fault location and fault degree is 99.45 ± 0.21%. Full article

Afghanistan is located on the Eurasian tectonic plate’s edge, a highly seismically active region. It is bordered by the northern boundary of the Indian plate and influenced by the collisional Arabian plate to the south. The Hindu Kush and Pamir Mountains in Afghanistan are part of the western extension of the Himalayan orogeny and have been uplifted and sheared by the convergence of the Indian and Eurasian plates. These tectonic activities have generated numerous active deep faults across the Hindu Kush–Himalayan region, many of which intersect Afghanistan, resulting in frequent high-magnitude earthquakes. This tectonic interaction produces ground shaking of varying intensity, from high to moderate and low, with the epicenters often located in the northeast and extending southwest across the country. This study maps Afghanistan’s tectonic structures, identifying the most active geological faults and regions with heightened seismicity. Historical earthquake data were reviewed, and recent destructive events were incorporated into the national earthquake dataset to improve disaster management strategies. Additionally, the study addresses earthquake hazards related to building and infrastructure design, offering potential solutions and directions to mitigate risks to life and property. Full article

This article presents research on the possibilities of using information and communication technologies in monitoring systems for electrical networks with isolated neutral, aimed at improving and automating production functions in the energy sector. This aligns with the digitalization policy of Kazakhstan’s economy and is part of similar programs in the field of the electric power industry. This article explores an approach to organizing a digital monitoring system for emergency conditions, specifically single-phase ground faults in medium-voltage lines within the range of 6–35 kV, including the new voltage class of 20 kV. A version of such a system is proposed, based on a combination of a server, a wireless information network, and remote digital voltage measurement nodes. This wireless information and communication network is designed to detect the locations of single-phase ground faults (SPGF) using specialized zero-sequence voltage sensors installed at various points along the power transmission lines, along with wireless signal transmission channels to the dispatcher’s server. To ensure protection against industrial interference, based on the results of practical environment modeling, a transmission technology most resistant to external noise is selected. This article proposes the selection of equipment necessary for implementing wireless transmission technology and develops two versions of a digital voltmeter design based on low-power programmable microcontrollers. The proposed technical solutions require further experimental validation, and therefore, the authors plan to conduct additional research and practical experiments in the future. Full article

The convergence zone of the Eurasian (EURA) and North Africa plate (NUBIA) is primarily marked by the activity between the Betics in south of Spain and the Rif and Atlas in Morocco. This area, where the diffuse tectonics between these plates are currently converging in a NW-SE direction, presents several continuous fault zones, such as the Betic–Alboran–Rif shear zone. The Royal Institute and Observatory of the Spanish Navy (ROA) currently operates geodetic stations in various parts of North Africa, some in particularly interesting locations, such as the Alhucemas (ALHU) rock, and also in more stable areas within the Nubian plate, such as Tiouine (TIOU). For the first time, the displacement velocities of the ROA CGNSS stations have been estimated to provide additional geodynamic information in an area with few stations. The obtained velocities have been compared with other recent studies in this field that included data older than 10 years or episodic campaigns without continuous stations. PRIDE (3.1.2) and SARI (February, 2025) software were used for processing, and the velocities were obtained by the ROA for international stations (RABT, SFER, MALA, HUEL, LAGO, TARI, and ALME). These initial results confirm the convergence trend between Eurasia and Nubia of approximately 4 mm/year in the NW-SE direction. It is also evident that there is independent behavior among the Atlas stations and those in the Moroccan Meseta compared to those located in the Rif mountain range, which could indicate the separation of smaller tectonic domains within the continental plate convergence zone. Along the Rif coast in Al Hoceima Bay, the faults are being approached; additionally, there is a slight clockwise displacement towards Melilla, which has also been demonstrated by stations in the Middle Atlas, such as TAZA. As for the stations in the Strait of Gibraltar, they exhibit a similar behavior until reaching the diffuse zone of the Guadalquivir basin where the diffuse convergence zone may exist. This may explain why stations to the north of the basin, such as LIJA or HUEL, change their behavior compared to nearby ones like SFER in the south. Furthermore, Alboran seems to follow the same displacement in direction and velocity as the other stations in North Africa and southern Spain. Full article

Root cause analysis is used to find the specific fault location and cause of a fault during system fault diagnosis. It is an important step in fault diagnosis. The root cause analysis method based on causality starts from the origin of the causal connection between transactions and infers the location and cause of the mechanism failure by analyzing the causal impact of variables between systems, which has methodological advantages. Causal analysis methods based on transfer entropy are proven to have biases in calculation results, so there is a phenomenon of calculating false causal relationships, which leads to the problem of insufficient accuracy in root cause analysis. Liang–Kleeman information flow (LKIF) is a kind of information entropy that can effectively carry out causal inference, which can avoid obtaining wrong causal relationships. We propose a root cause analysis method that combines graphical lasso and information flow. In view of the large amount of redundant information in industrial data due to the coupling effect of industrial systems, graphical lasso (Glasso) is a high-precision dimensionality reduction method suitable for large-scale and high-dimensional datasets. To ensure the timeliness of root cause analysis, graphical lasso uses dimensionality reduction of the data. Then, LKIF is used to calculate the information flow intensity of each relevant variable, infer the causal relationship between the variable pairs, and trace the root cause of the fault. On the Tennessee Eastman simulation platform, root cause analysis was performed on all faults, and two root cause analysis solutions, transfer entropy and information flow, were compared. Experimental results show that the LKIF–Glasso method can effectively detect the root cause of faults and display the propagation of faults throughout the process. It further shows that information flow has a better effect in root cause analysis than transfer entropy. And through the root cause analysis of the step failure of the stripper, the reason why information flow is superior to transfer entropy is explained in detail. Full article

To improve the accuracy of leak identification and location of water supply pipelines, a novel convolution gated recurrent unit method based on the attention mechanism is proposed in this paper. Firstly, a convolutional neural network is used to capture the localspatio-temporal characteristics of the signal. Secondly, a gated recurrent unit is used to extract the signal’s long dependence relationship. Finally, an attention mechanism is combined to highlight the influence of key features in the learning process, so as to achieve accurate recognition of the pipeline pressure state. The accurate identification of leakage faults is expected to further improve the location accuracy of pipeline leakage points, which is very important for the practical application of the algorithm in engineering. In order to verify the effectiveness of the proposed method, a simulated leakage test platform is set up for the leakage simulation test. The test results of different leakage conditions show that the recognition accuracy of the proposed network structure is 98.75% for test samples, which is higher than other network structures of the same type. According to the identification results of leakage characteristics, the VMD method is used to extract the high-frequency components of the negative pressure wave signal, so as to obtain the inflection point of the negative pressure wave, so as to determine the arrival time difference of the signal, and the arrival time method based on the negative pressure wave is used to locate the leakage point. Across 12 leak locations, the maximum relative error is 7.67%, the minimum relative error is 0.86%, and the average relative error is only 2.97%, achieving the best performance among the various methods. The positioning accuracy meets the requirement of practical application and the algorithm has good robustness. Full article

The Xiahe-Hezuo area in Gansu Province, China, located in the West Qinling Metallogenic Belt, is characterized by complex regional geological structures and abundant mineral resources. A number of gold-polymetallic deposits have been identified in this region, demonstrating significant potential for gold-polymetallic mineral prospecting within the metallogenic belt. This study focuses on regional Mineral Prospectivity Mapping (MPM) in the Xiahe-Hezuo area. To address the common challenge of small-sample data limitations in geological prediction, we introduce a Wasserstein Generative Adversarial Network with gradient penalty (WGAN-GP) to generate high-fidelity geological feature samples, effectively expanding the training dataset. A Convolutional Neural Network (CNN) was used to train and predict on both pre- and post-augmentation data. The experimental results show that, before augmentation, the CNN model’s Receiver Operating Characteristic (ROC) value was 0.9648. After data augmentation with the WGAN-GP, the CNN model’s ROC value improved to 0.9792. Additionally, the CNN model’s classification performance was significantly enhanced, with the training set accuracy increasing by 5% and the test set accuracy improving by 2%, successfully overcoming the issue of insufficient model generalization caused by small sample sizes. The mineralization prediction results based on data augmentation delineate five prospective mineralization targets, whose spatial distribution exhibits strong correlations with known deposits and fault structural belts, confirming the reliability of the predictions. This study validates the effectiveness of data augmentation techniques in MPM and provides a transferable technical framework for MPM in data-scarce regions. Full article

As the last link of power transmission, the safe operation of the distribution network directly affects the experience of power users, and short-time distribution network faults can cause huge economic losses. There are few fault recording devices in rural or suburban distribution networks, and it is difficult to upload information, which brings difficulties to accurate fault location. In order to improve the accuracy of fault location, this study proposes a fault location method for distribution networks based on electric field-coupled voltage sensing and multi-source information fusion. First, an optimized resource pool architecture is proposed, and a distribution network data fusion platform is established based on this architecture to effectively integrate voltage, current and other fault data. Second, in order to overcome the problem of expanding the fault location range that may be caused by the current-based matrix algorithm, this study proposes an improved directed graph-based matrix algorithm and combines it with the matrix algorithm of voltage quantities to form a joint location criterion, which improves the accuracy of fault location. Finally, for the single-ended ranging method, which is easily affected by the wave impedance discontinuity points in the system or the transition resistance in the line, this article introduces a fault ranging algorithm based on double-ended electrical quantities, which improves the accuracy and applicable range of fault ranging. Through simulation verification, we found that the matrix algorithm based on the electrical quantity can accurately locate the fault section in the case of a single fault with a single power supply. The proposed joint matrix algorithm can accurately locate the fault section in the case of a single fault with multiple power sources. The ranging algorithm based on double-ended electrical quantities has higher ranging accuracy in both interphase short circuits and grounded short circuits, and the ranging results are not affected by the fault type, fault location and transition resistance, which can effectively improve the efficiency and reliability of fault location. Full article