Search Results (12,775)

This paper is predominantly intended to explore the distribution rule of the sand body of the Zhuhai Formation on the north slope of the Baiyun Sag. The Zhuhai Formation was deposited during a rifting phase. Influenced by tectonic movements, the investigated area developed a set of contemporaneous normal faults extending in the near W-E direction. The formation of faults alters the palaeomorphology, exerting a certain influence on the distribution of sedimentary sand deposits. To clarify the correlation between faults and sand bodies will be advantageous for an even distribution of sand bodies in the Zhuhai Formation. This paper systematically integrates the results of previous research findings, drillcore logging and analysis, and 3D seismic data. The seismic sedimentology method is adopted to identify three types of fracture systems and four types of associations between the sand body distribution and faults in the investigated area. In line with the difference of the fault inclination and spatial relationship, faults can be divided into three types, namely, the graben-type, transition zone, and syntropy-type. Graben-type fault combinations exhibit the opposite dip. Syntropy-type fault combinations display the same dip. Transition zone faults intersect at a tiny angle. It is noteworthy that the existence of a fault will exert a certain influence on the sediment transport direction and distribution pattern. On the basis of the fault group classification, four associations between the sand body distribution and graben-type, transport-type, syntropy-ladder-type, and syntropy-lifting-type faults are identified by considering taking into account these base shape factors. The syntropy-ladder type is conducive to the extension of the sediment along the source direction. Both graben-type and syntropy-lifting-type faults can accumulate sediments. The transport type changes the direction of the sediment supply. Full article

In this study, an active fault-tolerant control problem was addressed for a rigid spacecraft in the presence of unknown actuator faults, uncertainties, and disturbances. First, an adaptive sliding mode iterative learning-based observer (ASILO) is proposed for diagnosing and reconstructing unknown faults. It achieves greater accuracy and rapidity while consuming less computing resources by constructing adaptive gain based on an auxiliary error. Specifically, it significantly improved the computational efficiency by 76% compared with the Strong Tracking Kalman Filter while achieving a similar accuracy. It also enhanced the accuracies relative to the traditional ILO and adaptive ILO by 67% and 36%, respectively, and demonstrated 82% and 52% increases in rapidity. Then, fault-tolerant control with resilient prescribed performance (RPP) that can adapt to changing initial conditions and adaptively adjust performance constraints online by sensing faults and error trends is proposed. It avoided the control singularity by constructing adaptive resilient boundaries with almost no impact on the computational overhead. It significantly improved the performance and conservatism. Finally, the robustness and effectiveness of the proposed strategy were demonstrated by numerical simulations. Full article

To address the challenge of accurately identifying tree-related high-impedance earth faults (THIEFs), a method based on long-term fluctuations in zero-sequence current is proposed. The analysis of the recorded data from staged tests in a real test network reveals the development patterns of THIEFs and their zero-sequence characteristics during the fault process. It was found that, over an extended duration, the fluctuation characteristics of the zero-sequence current RMS value curves for THIEFs differ significantly from those of the other types of high-impedance earth faults (HIEFs). By applying approximate arc length parameterization, the RMS value curve is standardized. The curvature standard deviation, the average curvature variation rate, and its standard deviation are used as feature parameters. An identification method based on an improved grey wolf optimization probabilistic neural network is constructed. Validation with staged test results demonstrates that the proposed method achieves a success rate of 97.5%, accurately distinguishing THIEFs from other types of HIEF. Full article

Infilled joints or faults are often subjected to long-term stable shear forces, and nature surface processes of normal unloading can change the frictional balance. Therefore, it is essential to study the sliding behavior of such granular materials under such unloading conditions, since they are usually the filling matter. We conducted two groups of normal unloading direct shear tests considering two variables: unloading rate and the magnitude of constant shear force. Dry sands may slide discontinuously during normal unloading, and the slip velocity does not increase uniformly with unloading time. Due to horizontal particle interlacing and normal relaxation, there will be sliding velocity fluctuations and even temporary intermissions. At the stage of sliding acceleration, the normal force decreases with a higher unloading rate and increases with a larger shear force at the same sliding velocity. The normal forces obtained from the tests are less than those calculated by Coulomb’s theory in the conventional constant-rate shear test. Under the same unloading rate, the range of apparent friction coefficient variation is narrower under larger shear forces. This study has revealed the movement patterns of natural granular layers and is of enlightening significance in the prevention of corresponding geohazards. 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

With the requirement of flexible operation of hydraulic turbine units, Francis turbine units have to adjust their output into extended operating ranges in order to match the demand of the power grid, which leads to more off-design conditions. In off-design conditions, hydraulic excitation causes excessive stress, pressure pulsation, and vibration on the machines. Different designs of Francis turbines cause different hydraulic excitations and vibrational behaviors. To conduct better condition monitoring and fault prognosis, it is of paramount importance to understand the vibrational behavior of a machine. In order to reveal the influence factors of the vibration behavior of Francie turbine units, field tests have been conducted on two similar-designed Francis turbine units and vibration features have been compared in this research. The vibrational behavior of two Francis turbine units installed in the same power station is compared under extended operating condition. Field tests have been performed on the two researched units and the vibration has been compared using the spectrum analysis method. The vibration indicators are extracted from the test data and the variation rules have been compared. By comparing the vibration behavior of the two machines, the design and installation difference of the two machines have been analyzed. This research reveals the effects of different designs and installations of Francis turbines on the vibration performance of the prototype units. The obtained results give guidance to the designers and operators of Francis turbine units. Full article

This paper deals with the operation method of an intelligent fault isolation device (IFID), which can detect and estimate faults in rapid and accurate ways considering the slope characteristics of fault currents with distribution line constants. Namely, the proposed operation method in IFID calculates the slope of the fault current with distribution line constants, and reduces its operation time by comparing the calculated slope value to the setting value to detect and evaluate the fault condition. Moreover, this paper implements the DC 400 V, 10 kW scaled IFID consisting of hardware (H/W) and software (S/W) sections based on the proposed operation method. The H/W section is composed of main and current limit switches, a current limit resistor, voltage and current sensors, and switching mode power supply (SMPS). Also, the S/W section consists of a control board and code composer studio (CCS) to calculate the slope characteristics of the fault current and control the semiconductor device. From the test results based on the proposed operation method, it was found that the IFID considering the slope characteristics of fault currents can detect and evaluate the fault condition and limit the fault current faster than the existing method to consider the fault current only. Full article

In ship propulsion, accurately diagnosing faults in permanent magnet synchronous motor is essential but challenging due to limitations in the intuitive characterization and feature extraction of fault signals. This study presents an innovative approach to motor fault detection by integrating phase-contrastive current dot patterns with an enhanced residual network, enhancing the diagnostic effect. Initially, the research involves creating a dataset that simulates stator currents. It is achieved through mathematical modeling of two common faults in permanent magnet synchronous motors: inter-turn short circuits and demagnetization. Subsequently, the parameters of the phase-contrastive current dot pattern are optimized using the Hunter-Prey Optimization technique to convert the three-phase stator currents of the motor into grayscale images. Lastly, a residual network, which includes a Squeeze-and-Excitation module, is engineered to boost the identification of crucial fault characteristics. The experimental results show that the proposed method achieves a high accuracy rate of 98.5% in the fault diagnosis task of motors, which can accurately identify the fault information and is significant in enhancing the reliability and safety of ship propulsion systems. Full article

Deep learning has performed well in feature extraction and pattern recognition and has been widely studied in the field of fault diagnosis. However, in practical engineering applications, the lack of sample size limits the potential of deep learning in fault diagnosis. Moreover, in engineering practice, it is usually necessary to obtain multidimensional fault information (such as fault localization and quantification), while current methods mostly only provide single-dimensional information. Aiming at the above problems, this paper proposes an Attention-based Multidimensional Fault Information Sharing (AMFIS) framework, which aims to overcome the difficulties of multidimensional bearing fault diagnosis in a small sample environment. Specifically, firstly, a shared network is designed to capture the common knowledge of the Fault Localization Task (FLT) and the Fault Quantification Task (FQT) and save it to the global feature pool. Secondly, two branching networks for performing FLT and FQT were constructed, and an attentional mechanism (AM) was used to filter out features from the shared network that were more relevant to the task to enhance the branching network’s capability under small samples. Meanwhile, we propose an innovative Dynamic Adjustment Strategy (DAS) designed to adaptively regulate the training weights of FLT and FQT tasks to achieve optimal training results. Finally, extensive experiments are conducted in two cases to verify the effectiveness and superiority of AMFIS. Full article

A comprehensive understanding of airflow temperature distribution within high-temperature tunnels is crucial for developing effective cooling strategies that ensure a safe environment and acceptable construction costs. In this paper, we introduce a novel cooling strategy that integrates thermal insulation layers and heat exchangers aligned along the tunnel axis (TIL-HE strategy). We investigate variations in airflow temperature and valid ventilation distance (VVD) and compare them with two other cooling strategies: natural tunnels only employing mechanical ventilation (NT strategy) and tunnels featuring thermal insulation layers (TIL strategy), through the 3D k-ε turbulence model in COMSOL Multiphysics. Our findings indicate that (1) the TIL-HE strategy demonstrates superior cooling performance, resulting in significantly lower airflow temperatures and markedly higher VVD; (2) higher water velocity and more heat exchangers contribute to lower airflow temperature and prolonged VVD; (3) positioning the heat exchangers within the surrounding rock rather than inside the insulation layer leads to even lower airflow temperature and longer VVD. Longitudinal-arranged heat exchangers present fewer construction challenges compared to traditional radial-drilled ones, ultimately reducing tunnel construction costs. These findings provide valuable insights for optimizing cooling strategies and engineering parameters in high-temperature tunnel environments. Full article

In this paper, we propose a method for locating discontinuities in grounding grids using a multistatic radar. The objective is to determine the fault position in the structure by injecting an ultra-wideband pulse (Gaussian monocycle) at one of the corners of the grid and analyzing the transient signals obtained at two sensors and at the transceiver. To perform the analysis and validation of the developed method, simulations based on the finite-difference time-domain (FDTD) technique were carried out to numerically solve Maxwell’s equations. The voltage signals obtained in an intact grounding grid are used as a reference. Differences between these reference voltages and the voltages obtained with the faulty grid are calculated. With these difference signals, the parameters of the radar ellipses and circle are obtained, which delimit the area where the fault can be found. These parameters depend on the wave propagation speed and the arrival times of the signals at the sensors and the transceiver. The results show that the proposed method is able to reduce the estimated fault location area to a range of 2% to 19% of the total grid area. In addition, the average distance between the actual fault and the center of the estimated region varies between 3.0 and 4.0 m. Full article

This paper presents a new method for fault location in transmission lines with series compensation, using data from voltage and current measurements at both terminals, applied to artificial neural networks. To determine the fault location, we present the proposal of using current phasors, obtained from the oscillography recorded during the short circuit, as the input to the neural network for training. However, the method does not rely on the internal voltage values of the sources or their respective equivalent Thevenin impedances to generate training files for the neural network in a transient simulator. The source data are not known exactly at the time of the short circuit in the transmission line, leading to greater errors when neural networks are applied to real electrical systems of utility companies, which reduces the dependency on electrical network parameters. To present the new method, a conventional fault location algorithm based on neural networks is initially described, highlighting how the dependency on source parameters can hinder the application of the artificial neural network in real cases encountered in utility electrical systems. Subsequently, the new algorithm is described and applied to simulated and real fault cases. Low errors are obtained in both situations, demonstrating its effectiveness and practical applicability. It is noted that the neural networks used for real cases are trained using simulated faults but without any data from the terminal sources. Although we expect the findings of this paper to have relevance in transmission lines with series compensation, the new method can also be applied to conventional transmission lines, i.e., without series compensation, as evidenced by the results presented. Full article

Fuel cells (FCs) offer several operational advantages when integrated as a power source in electric vehicles (EVs). Since the voltage of these cells is typically low, usually less than 1 V, the power conversion system requires a DC–DC converter capable of providing a high voltage conversion ratio to match the input voltage of the motor propulsion system, which can exceed 400 V and reach up to 800 V. The modular DC–DC boost converter proposed in this paper is designed to achieve a high voltage step-up ratio for the input FC voltages through the use of isolated series-connecting boosting submodules connected. The power electronic topology employed in the submodules (SMs) is designed to provide a flexible output voltage while maintaining a continuous input current from the fuel cells with minimal current ripple to improve the FC’s performance. The proposed step-up modular converter provides several benefits including scalability, better controllability, and improved reliability, especially in the presence of partial faults. Computer simulations using MATLAB/SIMULINK^® software (R2024a) have been used to study the feasibility of the proposed converter when connected to a permanent magnet synchronous motor (PMSM). Also, experimental results using a 1 kW prototype composed of four SMs have been obtained to validate the performance of the proposed converter. Full article

With the escalating proliferation of wind power plants, the imperative focus on system robustness and stability intensifies. Doubly fed induction machines (DFIMs) are extensively employed in land-based wind power plants due to their performance advantages. While the stator windings are directly connected to the power system, the rotor windings are connected via power converters, making these units vulnerable to voltage disturbances. During faults, voltage drops at the stator terminals lead to elevated voltages and currents on the rotor side due to electromagnetic coupling between stator and rotor, potentially damaging rotor insulation and costly power electronics. Historically, wind power plants employing DFIMs were disconnected from the grid during faults—an unsatisfactory solution given the burgeoning number of these installations. Consequently, grid operators and IEEE standard 2800 mandate fault ride-through (FRT) capabilities to maintain system stability during disturbances. This paper provides a short review of the existing techniques for protecting DFIMs during faults, focusing on both passive and active protection methods. Additionally, a simple calculation is presented to compare two different protection strategies, illustrating the differences in their effectiveness. The review emphasizes the necessity for developing models that represent all protection methods for DFIMs, due to the clear differences in the results obtained. Full article

Solid-state DC circuit breakers provide crucial support for the safe and reliable operation of low-voltage DC distribution networks. A hardware topology based on a cascaded structure with dual-stage, current-limiting, small-capacity, solid-state DC circuit breakers has been proposed. The hardware topology uses a series–parallel configuration of cascaded SCR (thyristors) and MOSFETs (metal oxide semiconductor field-effect transistors) in the transfer branch, which enhances the breaking capacity of the transfer branch. Additionally, a secondary current-limiting circuit composed of an inductor and resistor in parallel is integrated at the front end of the transfer branch to effectively improve the current-limiting performance of the circuit breaker. Meanwhile, a dissipation branch is introduced on the fault side to reduce the energy consumption burden on surge arresters. For the power supply system of the hardware part, a capacitor-powered method is adopted for safety and efficiency, with a capacitor switch serially connected to the capacitor power supply for high-precision control of the power supply. Current detection branches are introduced into each branch to provide conditions for the on–off control of semiconductor switching devices and experimental data analysis. The high-frequency control of semiconductor devices is achieved using optocoupler signal isolation chips and high-speed drive chips through a microcontroller STM32. Simulation verification based on MATLAB/SIMULINK software and experimental prototype testing have been conducted, and the results show that the hardware topology is correct and effective. Full article