Search Results (285)

Using a Bayesian framework to invert earthquake source parameters from multi-source geodetic data has become an important research direction. To address the issue of Markov Chain Monte Carlo (MCMC) algorithms getting stuck in local optima during nonlinear inversion of fault geometric parameters, which is often caused by improperly set parameter bounds or large deviations in the initial values, this study proposes two strategies: ‘CFI (Converge First, Then Interval)’ and ‘IVI (Interval Value Iteration)’. Tests with 12 different experimental setups show that both strategies can prevent the chain from getting trapped in local optima. Among them, the ‘IVI’ strategy, when used with MCMC algorithms where the step size follows a normal distribution, can also significantly reduce the root-mean-square error. To verify its applicability, the ‘IVI’ strategy was applied to the Bayesian inversion of the 2022 Menyuan Mw6.6 earthquake. The results show that the inverted values for fault depth, strike, dip, and rake angles are closer to the GCMT results, with ascending and descending track fitting residuals of 2.71 cm and 2.64 cm, respectively. The conclusion of this paper is to recommend the ‘IVI’ strategy when the range of source parameters is unclear. If the approximate range of parameters is known, the ‘CFI’ strategy can be applied. The original interval constraint method is recommended when the parameter bounds are fully determinable and a reliable initial model of seismic source parameters is obtainable. Full article

In low-voltage (LV) distribution networks, system operating conditions are always unbalanced due to the unpredictability of the load demand in each phase, coupled with a potentially asymmetrical network structure due to different phase conductors’ sizes and lengths. The widespread diffusion of distributed generators (DGs) among network users has significantly contributed to reducing the overall load of the electrical system, but at the cost of making voltages slightly more unbalanced. In this article, an LV distribution test network equipped with several single-phase DGs has been considered, and all During-Fault Voltages (DFVs) have been studied, according to each possible type of short circuit. To provide a measure of the asymmetry of unsymmetrical voltage dips, three different indices based on the symmetrical components of the voltages have been considered; moreover, the Monte Carlo simulation (MCS) method has allowed for studying faults and asymmetries in a probabilistic manner. Through the probability density functions (pdfs) of the DFVs, it has been possible to assess the impact of single-phase DGs on the asymmetry of bus voltages due to short-circuits. Full article

The growing deployment of photovoltaic (PV) power plants has made reliable fault detection and classification a critical challenge for ensuring operational efficiency, safety, and economic viability. Faults on the direct current (DC) side, especially during the commissioning phase, can significantly affect power output and maintenance costs. This paper proposes a fault classification methodology for the direct current (DC) side of PV power plants, using the MATLAB/Simulink 2023b simulation environment for system modeling and dataset generation. The method accounts for different environmental and operational conditions—including irradiance and temperature variations—to enhance fault identification robustness. The main electrical faults—such as open circuit (OC), short circuit (SC), connector faults, and partial shading—are analyzed based on features extracted from current–voltage (I–V) and power–voltage (P–V) curves. The proposed classification system achieved 100% accuracy by applying the One-Versus-One (OVO) and One-Versus-Rest (OVR) techniques, using a dataset with 704 samples for one string and 2480 samples for three strings. The lowest accuracies were observed with the OVO technique: 99.03% for 1024 samples with one string, and 97.35% for 880 samples with three strings. The study also highlights the performance of multiclass machine learning techniques across different dataset sizes. The results reinforce the relevance of using machine learning integrated into the commissioning phase of PV systems, with the potential to improve reliability, reduce losses, and optimize the operational costs of solar plants. Future work should explore the application of this method to real-world data, as well as its deployment in the field to support companies and professionals in the sector. Full article

The vibration signal of rotating machinery is usually nonlinear and non-stationary, and the feature set has information redundancy. Therefore, a high-dimensional feature reduction method based on multi-manifold learning is proposed for rotating machinery fault diagnosis. Firstly, considering the non-uniformity of multi-fault feature distribution and the sensitivity of domain selection in traditional manifold learning methods, the neighborhood size of each data point is selected adaptively by using the relationship between neighborhood size and sample density. Then, the between-manifold graph and within-manifold graph are constructed adaptively by the class information, and the divergence matrix and edge distance corresponding to the manifold graph are calculated. Feature fusion reduction is achieved by maximizing edge distance and minimizing within-class differences. Finally, the multi-manifold theoretical dataset and several rotating machinery fault datasets are selected for testing. The results show that the proposed algorithm has higher fault identification accuracy than traditional manifold learning methods. Full article

Fault diagnosis under variable operating conditions remains challenging due to the limited adaptability of traditional methods. This paper proposes a transfer learning-based approach for bearing fault diagnosis across different rotational speeds, addressing the critical need for reliable detection in changing industrial environments. The method trains a diagnostic model on labeled source-domain data and transfers them to unlabeled target domains through a two-stage adaptation strategy. First, only the source-domain data are labeled to reflect real-world scenarios where target-domain labels are unavailable. The model architecture combines a convolutional neural network (CNN) for feature extraction with a self-attention mechanism for classification. During source-domain training, the feature extractor parameters are frozen to focus on classifier optimization. When transferring to target domains, the classifier parameters are frozen instead, allowing the feature extractor to adapt to new speed conditions. Experimental validation on the Case Western Reserve University bearing dataset (CWRU), Jiangnan University bearing dataset (JNU), and Southeast University gear and bearing dataset (SEU) demonstrates the method’s effectiveness, achieving accuracies of 99.95%, 99.99%, and 100%, respectively. The proposed method achieves significant model size reduction compared to conventional TL approaches (e.g., DANN and CDAN), with reductions of up to 91.97% and 64%, respectively. Furthermore, we observed a maximum reduction of 61.86% in FLOPs consumption. The results show significant improvement over conventional approaches in maintaining diagnostic performance across varying operational conditions. This study provides a practical solution for industrial applications where equipment operates under non-stationary speeds, offering both computational efficiency and reliable fault detection capabilities. Full article

Based on mechanical experiments conducted on bulk raw coal and coal of different types in order to explore the correlations between the fractal dimension and the grain size gradation and strength parameters of coal samples, the fractal statistics method was used to statistically analyze the grain size distribution characteristics of tectonic soft coal, while fractal theory was applied to study the grain size fractal characteristics of tectonic soft coals of categories III–V. The results of this study show that coal types III–V have increasing fractal dimension numbers, and the content of coarse particles decreases with an increasing fractal dimension number. Within this sampling range, the Class V coal is better graded, and the fractal dimension number decreases as the distance of the sampling point from the fault zone increases. In the direct shear experiments, the internal friction angle of the bulk raw coal decreased linearly with an increasing fractal dimension number, and the regularity of the cohesive force and the fractal dimension number was not strong, but the adhesion cohesion of the types of coal exhibited a positive exponential relationship with the fractal dimension, and the relationship between the internal friction angle and the fractal dimension was not strong. There was a positive exponential relationship, and the internal friction angle was relatively stable. The uniaxial compressive strength of the types of coal exhibited a good correlation with the coefficient of firmness of the coal samples and the fractal dimension, and the coefficient of firmness of the coal samples was the main factor influencing the uniaxial compressive strength of the types of coal compared with the particle size gradation. Full article

Condition monitoring and fault detection in wind turbines are essential for reducing repair and maintenance costs. Early detection of faults enables timely interventions before the damage worsens. However, existing methods often rely on costly scheduled inspections or lack the ability to effectively detect early stage damage, particularly under different operational speeds. This article presents a methodology based on convolutional neural networks (CNNs) and empirical mode decomposition (EMD) of vibration signals for the detection of blade crack damage. The proposed approach involves acquiring vibration signals under four conditions: healthy, light, intermediate, and severe damage. EMD is then applied to extract time–frequency representations of the signals, which are subsequently converted into images. These images are analyzed by a CNN to classify the condition of the wind turbine blades. To enhance the final CNN architecture, various image sizes and configuration parameters are evaluated to balance computational load and classification accuracy. The results demonstrate that combining vibration signal images, generated using the EMD method, with CNN models enables accurate classification of blade conditions, achieving 99.5% accuracy while maintaining a favorable trade-off between performance and complexity. Full article

Split-shaft wind turbines decouple the turbine’s shaft from the generator’s shaft, enabling several modifications in the drivetrain. One of the significant achievements of a split-shaft drivetrain is the reduction in size of the excitation circuit. The grid-side converter is eliminated, and the rotor-side converter can safely reduce its size to a fraction of a full-size excitation. Therefore, this low-power-rated converter operates at low voltage and handles regular operations well. However, fault conditions may expose weaknesses in the converter and push it to its limits. This paper investigates the effects of the reduced-size rotor-side converter on the voltage ride-through capabilities required from all wind turbines. Four different protection circuits, including the active crowbar, active crowbar along a resistor–inductor circuit (C-RL), series dynamic resistor (SDR), and new-bridge fault current limiter (NBFCL), are employed, and their effects are investigated and compared. Wind turbine controllers are also utilized to reduce the impact of faults on the power electronic converters. One effective method is to store excess energy in the generator’s rotor. The proposed low-voltage ride-through strategies are simulated in MATLAB Simulink (2022b) to validate the results and demonstrate their effectiveness and functionality. Full article

Rolling bearings are indispensable but also the most fault-prone components of rotating machinery, typically used in fields such as industrial aircraft, production workshops, and manufacturing. They encounter diverse mechanical stresses, such as vibration and friction during operation, which may lead to wear and fatigue cracks. From this standpoint, the present study combined a 1-D convolutional neural network (1-D CNN) with a long short-term memory (LSTM) algorithm for classifying different ball-bearing health conditions. A physics-guided method that adopts fault characteristics frequencies was used to calculate an optimal input size (sample length). Moreover, grid search was utilized to optimize (1) the number of epochs, (2) batch size, and (3) dropout ratio and further enhance the efficacy of the proposed 1-D CNN-LSTM network. Therefore, an attempt was made to reduce epistemic uncertainty that arises due to not knowing the best possible hyper-parameter configuration. Ultimately, the effectiveness of the physics-guided optimized 1-D CNN-LSTM was tested by comparing its performance with other state-of-the-art models. The findings revealed that the average accuracies could be enhanced by up to 20.717% with the help of the proposed approach after testing it on two benchmark datasets. Full article

Oil condition monitoring can ensure the safe operation of mechanical equipment. Metal debris is full of friction information, and the identification of debris material helps to locate wear of parts. A method based on impedance analysis is proposed to identify debris material in this article. The differences in permeability and conductivity result in the nonlinear variation trend of inductance–resistance amplitude with debris volume. By establishing a database of amplitude–size curves, debris information (material and size) can be obtained through impedance analysis. Based on experimental and simulation results, iron, stainless steel, aluminum, copper, and brass particles are effectively distinguished. This method is not affected by oil’s light transmittance, other impurities, and debris surface dirt and can be used to distinguish metals with similar colors. This work provides a novel solution for debris material identification, which is expected to promote the development of fault diagnosis. Full article

To address the challenges of extracting rolling bearing degradation information and the insufficient performance of conventional convolutional networks, this paper proposes a rolling bearing degradation state identification method based on the improved monopulse feature extraction and a one-dimensional dilated residual convolutional neural network (1D-DRCNN). First, the fault pulse envelope waveform features are extracted through phase scanning and synchronous averaging, and a two-stage grid search strategy is employed to achieve FCC calibration. Subsequently, a 1D-DRCNN model is constructed to identify rolling bearing degradation states under different working conditions. The experimental study collects the vibration signals of nine degradation states, including the different sizes of inner and outer ring local faults as well as normal conditions, to comparatively analyze the proposed method’s rapid calibration capability and feature extraction quality. Furthermore, t-SNE visualization is utilized to analyze the network response to bearing degradation features. Finally, the degradation state identification performance across different network architectures is compared in pattern recognition experiments. The results show that the proposed improved feature extraction method significantly reduces the iterative calibration computational burden while effectively extracting local fault degradation information and overcoming complex working condition influence. The established 1D-DRCNN model integrates the advantages of dilated convolution and residual connections and can deeply mine sensitive features and accurately identify different bearing degradation states. The overall recognition accuracy can reach 97.33%. Full article

The data-driven intelligent fault diagnosis method has shown great potential in improving the safety and reliability of train operation. However, the noise interference and multi-scale signal characteristics generated by the train transmission system under non-stationary conditions make it difficult for the network model to effectively learn fault features, resulting in a decrease in the accuracy and robustness of the network. This results in the requirements of train fault diagnosis tasks not being met. Therefore, a novel parallel multi-scale attention residual neural network (PMA-ResNet) for a train transmission system is proposed in this paper. Firstly, multi-scale learning modules (MLMods) with different structures and convolutional kernel sizes are designed by combining a residual neural network (ResNet) and an Inception network, which can automatically learn multi-scale fault information from vibration signals. Secondly, a parallel network structure is constructed to improve the generalization ability of the proposed network model for the entire train transmission system. Finally, by using a self-attention mechanism to assign different weight values to the relative importance of different feature information, the learned fault features are further integrated and enhanced. In the experimental section, a train transmission system fault simulation platform is constructed, and experiments are carried out on train transmission systems with different faults under non-stationary conditions to verify the effectiveness of the proposed network. The experimental results and comparisons with five state-of-the-art methods demonstrate that the proposed PMA-ResNet can diagnose 19 different faults with greater accuracy. Full article

The identification of critical fault information plays a crucial role in ensuring the reliability and stability of power systems. However, existing fault-identification technologies heavily rely on high-dimensional sensor data, which often contain redundant and noisy information. Moreover, conventional data preprocessing approaches typically employ fixed time windows, neglecting variations in fault characteristics under different system states. This limitation may lead to incomplete feature selection and ineffective dimensionality reduction, ultimately affecting the accuracy of fault classification. To address these challenges, this study proposes a method of critical fault information identification that integrates a scalable time window with Principal Component Analysis (PCA). The proposed method dynamically adjusts the time window size based on real-time system conditions, ensuring more flexible data capture under diverse fault scenarios. Simultaneously, PCA is employed to reduce dimensionality, extract representative features, and remove redundant noise, thereby enhancing the quality of the extracted fault information. Furthermore, this approach lays a solid foundation for the subsequent application of deep learning-based fault-diagnosis techniques. By improving feature extraction and reducing computational complexity, the proposed method effectively alleviates the workload of operation and maintenance personnel while enhancing fault classification accuracy. Our experimental results demonstrate that the proposed method significantly improves the precision and robustness of fault identification in power systems. Full article

Existing intelligent fault diagnosis methods have been widely developed and proven to be effective in monitoring the operating status of key mechanical components. However, centrifugal fans, as important equipment in energy and manufacturing industries, have been used for a long time in complex and harsh environments such as boiler plants and gas turbines. Therefore, the vibration signals they generate show complex and diverse characteristics, which brings great challenges to the monitoring of centrifugal fan operation status. To solve this problem, this paper proposes a centrifugal fan blade fault diagnosis method based on a modulational depthwise convolution (DWconv)–one-dimensional convolution neural network (MDC-1DCNN). Specifically, firstly, a convolutional modulation module (CMM) with strong local perception and global modeling capability is designed by drawing on the Transformer self-attention mechanism and global context modeling idea. Second, multiple DWconv layers of different sizes are introduced to capture high-frequency shocks and low-frequency fluctuation information of different frequencies and durations in the signal. Next, a DWconv layer of size 11 is embedded in the multilayer perceptron to enhance spatial information representation while saving computational resources. Finally, to verify the effectiveness of the method, this paper simulates and analyzes the actual working state of centrifugal fan blades, constructs a simulation dataset, and builds a centrifugal fan experimental bench to obtain a real dataset. The experimental results show that the MDC-1DCNN framework significantly outperforms the existing methods in both simulation and experimental bench datasets, fully proving its versatility and effectiveness in centrifugal fan blade fault diagnosis. Full article

Under mixed lubrication, the macro size is affected by the wear of the surface roughness peaks, which results in degradation of the bearing accuracy. To study the wear characteristics of rolling bearings under mixed lubrication, based on the elastohydrodynamic lubrication theory and Archard wear model, and considering the coupling of the oil film and roughness, a wear prediction model of angular contact ball bearings under mixed lubrication was established, and the influence of the working parameters and hardness on bearing wear was analyzed. The results show that the wear depth of the outer grove increases with an increase in the load, or a decrease in the rotational speed or the initial viscosity of lubricating oil. The load has the most significant effect on the wear depth of the outer grove. There is a critical value for the load, rotational speed, and initial viscosity of the lubricating oil, which varies with the parameters of other working conditions and the hardness of the materials. When the increase in load exceeds the critical value or the rotational speed and initial viscosity of lubricating oil are less than the critical value, the outer groove fails because the wear depth exceeds the critical value of wear depth. The ratio of the load on the rolling element to the hardness of the outer grove at different entrainment speeds and initial viscosities of lubricating oil can be used to predict the wear degree of the outer grove. When the ratio is greater than a certain threshold, the outer grove is faulted owing to wear, and the threshold decreases with an increase in the initial viscosity of lubricating oil or the decrease in rotational speed. Full article