Search Results (246)

Rotating machinery is essential to modern industrial systems, where rolling bearings play a critical role in ensuring mechanical stability and operational efficiency. Failures in bearings can result in serious safety risks and significant financial losses, which highlights the need for accurate and robust methods for diagnosing bearing faults. Traditional diagnostic methods relying on single-source data often fail to fully leverage the rich information provided by multiple sensors and are more prone to performance degradation under noisy conditions. Therefore, this paper proposes a novel bearing fault diagnosis method based on a multi-level fusion framework. First, the Symmetrized Dot Pattern (SDP) method is applied to fuse multi-source signals into unified SDP images, enabling effective fusion at the data level. Then, a combination of RepLKNet and Bidirectional Gated Recurrent Unit (BiGRU) networks extracts multi-modal features, which are then fused through a cross-attention mechanism to enhance feature representation. Finally, information entropy is utilized to assess the reliability of each feature channel, enabling dynamic weighting to further strengthen model robustness. The experiments conducted on public datasets and noise-augmented datasets demonstrate that the proposed method significantly surpasses other single-source and multi-source data fusion models in terms of diagnostic accuracy and robustness to noise. Full article

The aircraft power supply system plays a crucial role in maintaining the stability and safety of airborne avionics. With the evolution toward more electric and all-electric aircraft, its architecture increasingly adopts symmetrical configurations, such as dual-redundant paths and three-phase balanced outputs. However, these symmetry-based designs are often disrupted by diverse fault mechanisms encountered in complex operational environments. This review contributes a comprehensive and structured analysis of how such fault events lead to symmetry-breaking phenomena across different subsystems, including generators, converters, controllers, and distribution networks. Unlike previous reviews that treat faults in isolation, this study emphasizes the underlying physical mechanisms and hierarchical fault propagation characteristics, revealing how structural coupling and multi-physics interactions give rise to failure modes. The paper concludes by outlining future research directions in symmetry-aware fault modeling and intelligent maintenance strategies, aiming to address the growing complexity and reliability demands of next-generation aircraft. Full article

Micro-motors are essential for power drive systems, and efficient fault diagnosis is crucial to reduce safety risks and economic losses caused by failures. However, the fault signals from micro-motors typically exhibit weak and unclear characteristics. To address this challenge, this paper proposes a novel fault diagnosis method that integrates jump plus AM-FM mode decomposition (JMD), symmetrized dot pattern (SDP) visualization, and an improved convolutional neural network (ICNN). Firstly, we employed the jump plus AM-FM mode decomposition technique to decompose the mixed fault signals, addressing the problem of mode mixing in traditional decomposition methods. Then, the intrinsic mode functions (IMFs) decomposed by JMD serve as the multi-channel inputs for symmetrized dot pattern, constructing a two-dimensional polar coordinate petal image. This process achieves both signal reconstruction and visual enhancement of fault features simultaneously. Finally, this paper designed an ICNN method with LeakyReLU activation function to address the vanishing gradient problem and enhance classification accuracy and training efficiency for fault diagnosis. Experimental results indicate that the proposed JMD-SDP-ICNN method outperforms traditional methods with a significantly superior fault classification accuracy of up to 99.2381%. It can offer a potential solution for the monitoring of electromechanical structures under complex conditions. Full article

Inter-turn short-circuit faults in induction motors might lead to overheating, torque imbalances, and eventual motor failure. This paper presents a fault detection framework for accurately identifying ITSC faults under various operating conditions. The proposed method integrates negative-sequence current analysis utilizing wavelet-based filtering and symmetrical component decomposition. A fault detection index to effectively monitor motor health and detect faults is presented. Moreover, the fault location is determined by phase angles of fundamental components of negative-sequence currents. Experimental validations were carried out for an inverter-fed induction motor under variable speed and load cases. These showed that the proposed approach has high sensitivity to early-stage inter-turn short circuits. This makes the framework highly suitable for real-time condition monitoring and predictive maintenance in inverter-fed motor systems, thereby improving system reliability and minimizing unplanned downtime. Full article

Petal-shaped distribution networks are receiving increasing attention due to their enhanced reliability. However, the integration of distributed generators (DGs) significantly alters the fault characteristics during single-phase to ground faults. Traditional short-circuit calculation methods become inadequate due to the unmodeled effects of negative sequence current control in DGs. To address this challenge, this study establishes, for the first time, a mathematical model for single-phase to ground faults in a petal-shaped network with DG integration under both positive and negative sequence control. It explicitly derives the DGs’ output current under three control goals: maintaining constant active power, maintaining constant reactive power, and injecting a symmetric three-phase current. Utilizing the symmetrical component method, a composite sequence network incorporating the DGs’ negative sequence current output is developed. Based on the node–voltage relationships, an analytical short-circuit current calculation method suitable for multiple control goals is proposed. Validation via MATLAB R2022a simulations demonstrates high-fidelity accuracy: in Case 1 with different fault locations, the maximum relative error is 0.31%, while in Case 2, it is 2.04%. These results quantify the critical impact of the negative sequence current—reaching up to 14.78% of the DG output during severe voltage sags—providing theoretical support for the protection design of a petal-shaped distribution network with high DG integration. Full article

Effective fault feature extraction and classification methods serve as the foundation for achieving the efficient fault diagnosis of planetary gearboxes. Considering the vibration signals of planetary gearboxes that contain both symmetrical and asymmetrical components, this paper proposes a novel feature extraction method integrating an adaptive multi-bandpass filter (AMBPF) and refined composite multi-scale fuzzy entropy (RCMFE). And a dream optimization algorithm (DOA)–least squares support vector machine (LSSVM) is also proposed for fault classification. Firstly, the AMBPF is proposed, which can effectively and adaptively separate the meshing frequencies, harmonic frequencies, and their sideband frequency information of the planetary gearbox, and is combined with RCMFE for fault feature extraction. Secondly, the DOA is employed to optimize the parameters of the LSSVM, aiming to enhance its classification efficiency. Finally, the fault diagnosis of the planetary gearbox is achieved by the AMBPF, RCMFE, and DOA-LSSVM. The experimental results demonstrate that the proposed method achieves significantly higher diagnostic efficiency and exhibits superior noise immunity in planetary gearbox fault diagnosis. Full article

Enhancing the resilience of renewable energy systems in ultra-weak grids is crucial for promoting sustainable energy adoption and ensuring a reliable power supply during disturbances. Ultra-weak grids characterized by a very low Short-Circuit Ratio, less than 2, and high grid impedance significantly impair voltage and frequency stability, imposing challenging conditions for Inverter-Based Resources. To address these challenges, this paper considers a 110 KVA, three-phase, two-level Voltage Source Converter, interfacing a 700 V DC link to a 415 V AC ultra-weak grid. X/R = 1 is controlled using Sinusoidal Pulse Width Modulation, where the Grid-Connected Converter operates in Grid-Forming Mode to maintain voltage and frequency stability under a steady state. During symmetrical and asymmetrical faults, the converter transitions to Grid-Following mode with current control to safely limit fault currents and protect the system integrity. After fault clearance, the system seamlessly reverts to Grid-Forming Mode to resume voltage regulation. This paper proposes an improved control strategy that integrates voltage feedforward reactive power support and virtual capacitor-based virtual inertia using Active Disturbance Rejection Control, a robust, model-independent controller, which rapidly rejects disturbances by regulating d and q-axes currents. To test the practicality of the proposed system, real-time implementation is carried out using the OPAL-RT OP4610 platform, and the results are experimentally validated. The results demonstrate improved fault current limitation and enhanced DC link voltage stability compared to a conventional PI controller, validating the system’s robust Fault Ride-Through performance under ultra-weak grid conditions. Full article

Abnormal operations, such as faults occurring in an electrical power system (EPS), disrupt its balanced operation, posing potential hazards to human lives and the system’s equipment. Effective monitoring, control, protection, and coordination are essential to mitigate these risks. The complexity of these processes is further compounded by the presence of intermittent distributed energy resources (DERs) in active distribution networks (ADNs) with bidirectional power flow, which introduces a fast-changing dynamic aspect to the system. The deployment of phasor measurement units (PMUs) within the EPS as highly responsive equipment can play a pivotal role in addressing these challenges, enhancing the system’s resilience and reliability. However, synchrophasor measurement-based studies and analyses of power system phenomena may be hindered by the absence of PMU blocks in certain simulation tools, such as PSCAD, or by the existing PMU block in Matlab/Simulink R2021b, which exhibit technical limitations. These limitations include providing only the positive sequence component of the measurements and lacking information about individual phases, rendering them unsuitable for certain measurements, including unbalanced and non-symmetrical fault operations. This study proposes a new reliable PMU model in Matlab and tests it under normal and abnormal conditions, applying real-time simulation and controller-hardware-in-the-loop (CHIL) techniques. Full article

To address the challenges of prolonged current isolation times and high dependency on varistors in traditional flexible short-circuit fault isolation schemes for DC systems, this paper proposes a rapid fault isolation circuit design based on an adaptive solid-state circuit breaker (SSCB). By introducing an adaptive current-limiting branch topology, the proposed solution reduces the risk of system oscillations induced by current-limiting inductors during normal operation and minimizes steady-state losses in the breaker. Upon fault occurrence, the current-limiting inductor is automatically activated to effectively suppress the transient current rise rate. An energy dissipation circuit (EDC) featuring a resistor as the primary energy absorber and an auxiliary varistor (MOV) for voltage clamping, alongside a snubber circuit, provides an independent path for inductor energy release after faults. This design significantly alleviates the impact of MOV capacity constraints on the fault isolation process compared to traditional schemes where the MOV is the primary energy sink. The proposed topology employs a symmetrical bridge structure compatible with both pole-to-pole and pole-to-ground fault scenarios. Parameter optimization ensures the IGBT voltage withstand capability and energy dissipation efficiency. Simulation and experimental results demonstrate that this scheme achieves fault isolation within 0.1 ms, reduces the maximum fault current-to-rated current ratio to 5.8, and exhibits significantly shorter isolation times compared to conventional approaches. This provides an effective solution for segment switches and tie switches in millisecond-level self-healing systems for both low-voltage (LVDC, e.g., 750 V/1500 V DC) and medium-voltage (MVDC, e.g., 10–35 kV DC) smart DC distribution grids, particularly in applications demanding ultra-fast fault isolation such as data centers, electric vehicle (EV) fast-charging parks, and shipboard power systems. Full article

This study addresses the long-standing difficulty of predicting the remaining useful life (RUL) of rolling bearings from highly non-stationary vibration signals by proposing WaveAtten, a symmetry-aware deep learning framework. First, mirror-symmetric and bi-orthogonal Daubechies wavelet filters are applied to decompose each raw signal into multi-scale approximation/detail pairs, explicitly preserving the left–right symmetry that characterizes periodic mechanical responses while isolating asymmetric transient faults. Next, a bidirectional sparse-attention module reinforces this structural symmetry by selecting query–key pairs in a forward/backward balanced fashion, allowing the network to weight homologous spectral patterns and suppress non-symmetric noise. Finally, the symmetry-enhanced features—augmented with temperature and other auxiliary sensor data—are fed into a long short-term memory (LSTM) network that models the symmetric progression of degradation over time. Experiments on the IEEE PHM2012 bearing dataset showed that WaveAtten achieved superior mean squared error, mean absolute error, and R² scores compared with both classical signal-processing pipelines and state-of-the-art deep models, while ablation revealed a 6–8% performance drop when the symmetry-oriented components were removed. By systematically exploiting the intrinsic symmetry of vibration phenomena, WaveAtten offers a robust and efficient route to RUL prediction, paving the way for intelligent, condition-based maintenance of industrial machinery. Full article

To address the challenges posed by the difficulty of extracting fault features from rotating machinery with weak fault features, this paper proposes a rotating machinery structural faults feature enhancement and diagnosis method based on multi-sensor information fusion. Firstly, Savitzky–Golay filtering suppresses noise and enhances fault features. Secondly, the designed multi-sensor symmetric dot pattern (SDP) transformation method fuses multi-source information of the rotating machinery structural faults, providing more comprehensive and richer fault feature information for diagnosis. Finally, the ResNet18 model performs fault diagnosis. To validate the feasibility and effectiveness of the proposed method, two datasets verify its performance. The accuracy of the experimental results was 99.16% and 100%, respectively, demonstrating the feasibility and effectiveness of the proposed method. To further validate the superiority of the proposed method, it was compared with different 2D signal transformation methods. The comparison results indicate that the proposed method achieves the best fault diagnosis accuracy compared to other methods. Full article

Recent advances in technologies such as blockchain, the Internet of Things (IoT), Cyber–Physical Systems (CPSs), and the Industrial Internet of Things (IIoT) have driven the digitalization and intelligent transformation of modern industries. However, embedded control devices within power system communication infrastructures have become increasingly susceptible to cyber threats due to escalating software complexity and extensive network exposure. We have seen that symmetric conventional patching techniques—both static and dynamic—often fail to satisfy the stringent requirements of real-time responsiveness and computational efficiency in resource-constrained environments of all kinds of power grids. To address this limitation, we have proposed a hardware-assisted runtime patching framework tailored for embedded systems in critical power system networks. Our method has integrated binary-level vulnerability modeling, execution-trace-driven fault localization, and lightweight patch synthesis, enabling dynamic, in-place code redirection without disrupting ongoing operations. By constructing a system-level instruction flow model, the framework has leveraged on-chip debug registers to deploy patches at runtime, ensuring minimal operational impact. Experimental evaluations within a simulated substation communication architecture have revealed that the proposed approach has reduced patch latency by 92% over static techniques, which are symmetrical in a working way, while incurring less than 3% CPU overhead. This work has offered a scalable and real-time model-driven defense strategy that has enhanced the cyber–physical resilience of embedded systems in modern power systems, contributing new insights into the intersection of runtime security and grid infrastructure reliability. Full article

Single-phase short-circuit faults are severe asymmetrical fault modes in high renewable energy power systems. They can easily cause large-scale renewable energy to enter the low-voltage ride-through (LVRT) state. When such symmetrical or asymmetrical faults occur in the transmission channels of high-proportion wind power clusters, they may trigger the tripping of thermal power units and a transient voltage drop in most wind turbines in the high-proportion wind power area. This causes an instantaneous active power deficiency and poses a low-frequency oscillation risk. To address the deficiencies of wind turbine units in fault ride-through (FRT) and active frequency regulation capabilities, a power emergency support scheme for wind power clusters based on doubly fed variable-speed pumped storage dynamic excitation is proposed. A dual-channel energy control model for variable-speed pumped storage units is established via AC excitation control. This model provides inertia support and FRT energy simultaneously through AC excitation control of variable-speed pumped storage units. Considering the transient stability of the power network in the wind power cluster transmission system, this scheme prioritizes offering dynamic reactive power to support voltage recovery and suppresses power oscillations caused by power deficiency during LVRT. The electromagnetic torque completed the power regulation within 0.4 s. Finally, the effectiveness of the proposed strategy is verified through modeling and analysis based on the actual power network of a certain region in Northeast China. Full article

Traditional fault feature generation models often face issues of uncontrollability, singularity, and slow convergence, limiting diagnostic accuracy. To address these challenges, this paper proposes a symmetry-aware approach that combines a conditional variational autoencoder (CVAE) and an auxiliary classifier generative adversarial network (ACGAN) for fault feature generation, leveraging symmetry characteristics inherent in fault data distributions and adversarial learning. Specifically, symmetrical Gaussian distributions in the CVAE enable robust extraction of latent fault features conditioned on fault classes, which are then input to the symmetrical adversarial framework of the ACGAN to guide the generator and discriminator toward a symmetrical Nash equilibrium. The original and generated features are jointly utilized in a convolutional neural network (CNN) for fault classification. Experimental results on the CWRU dataset show that the proposed CVAE-ACGAN achieves an average accuracy of 99.21%, precision of 97.81%, and recall of 98.24%, surpassing the baseline CNN. Similar improvements are achieved on the PADERBORN dataset. Furthermore, the model achieves significantly lower root mean square error (RMSE) and mean absolute error (MAE) than competing methods, confirming high consistency between the generated and real features and supporting its superior generalization and reliability. Visualization via confusion matrices and t-SNE further demonstrates clear boundaries between fault categories. These results affirm the value of incorporating symmetry principles into feature generation for mechanical fault diagnosis. Full article

Rolling bearing fault diagnosis in electric hoists faces significant challenges due to heavy noise and complex vibration interferences, which obscure fault signatures and hinder conventional demodulation methods. While existing techniques like the Teager–Kaiser energy operator (TKEO) and its variants (e.g., HO-AEO, SD-AEO) offer filterless demodulation, their susceptibility to noise and dependency on preprocessing limit diagnostic accuracy. This study proposes a Multi-resolution Higher-order Symmetric Analytic Energy Operator (MHSAEO) to address these limitations. The MHSAEO integrates three innovations: (1) dynamic non-adjacent sampling to suppress stochastic errors, (2) AM-FM dual demodulation via symmetric energy orthogonality, and (3) adaptive spectral mining for full-band feature extraction. Experimental validation on a 10-ton electric hoist bearing system demonstrates that the MHSAEO achieves signal-to-noise ratio improvements (SNRIs) of −3.83 dB (outer race faults) and −2.12 dB (inner race faults), successfully identifying the characteristic fault frequencies of both inner (145.9 Hz) and outer races in electric hoist bearings with 2nd–5th harmonics. Compared to traditional methods, the MHSAEO reduces computational time by 30.1 × (0.0328 s vs. 0.9872 s) without requiring preprocessing. The results confirm its superior anti-interference capability and real-time performance over the TKEO, HO-AEO, and hybrid denoising–TKEO approaches. Full article