Search Results (27)

Reliable underwater acoustic (UWA) communication is fundamental to marine sensing applications, including environmental monitoring, underwater sensor networks, and autonomous platforms, yet remains severely challenged by multipath propagation, Doppler effects, and limited bandwidth. This paper investigates a memory-based multicarrier modulation framework in which controlled phase continuity is introduced at the symbol-mapping stage to enhance robustness against channel-induced distortions. Unlike conventional memoryless multicarrier schemes, the proposed approach embeds intentional phase memory at the transmitter and exploits it at the receiver, improving reliability in highly dispersive underwater environments. A comprehensive bit-error-rate (BER) evaluation is conducted using extensive simulations over realistic shallow-water acoustic channel models. The analysis examines rational modulation indices, pulse-shaping filters, roll-off factors, transmitter–receiver separation distances, and receiver structures. Both matched-filter and zero-forcing receivers are considered to assess trade-offs between interference mitigation and noise amplification. Results demonstrate consistent and significant BER improvements compared with conventional memoryless multicarrier systems. A modulation index of 7/16 achieves the minimum BER with matched-filter detection, while 3/10 yields optimal performance with zero-forcing detection. The Dirichlet pulse provides the most robust performance across operating conditions. These findings establish phase-memory-aware multicarrier design as a practical strategy for reliable underwater sensing and communication systems. Full article

Incipient fault signatures in rolling bearings are often compromised by intense background noise and stochastic impulses. Conventional resonance demodulation frequently relies on rigid frequency partitioning, which tends to disrupt the physical continuity of resonance bands and results in the incomplete capture of essential diagnostic information. Furthermore, the robustness of prevailing optimal demodulation frequency band (ODFB) selection indicators remains limited under heavy noise interference. This study develops the WLERgram framework, which utilizes regularized Fourier series to capture the global morphology of the vibration spectrum. By anchoring filter boundaries at natural energy troughs, the method mitigates spectral truncation based on inherent signal characteristics. The framework integrates an Adaptive Morphological Consensus (AMC) strategy, employing multi-scale operators to extract rotation-correlated components and enhance resistance to incoherent interference. By incorporating a Weighted Logarithmic Energy Ratio (WLER) metric, the method utilizes a nonlinear operator to implement differential mapping between coherent fault harmonics and stochastic noise, enabling autonomous optimization of the demodulation band. Validations using synthetic simulations and experimental benchmarks (CWRU and UORED) suggest that WLERgram offers reliable feature extraction performance and diagnostic robustness under harsh noise environments. Full article

Rolling bearing faults originate from complex tribodynamic interactions among rolling elements, raceways, and the cage, yielding nonlinear, non-stationary vibration signals that are highly susceptible to noise and operating-condition variations, which compromises the reliability of diagnosis. To address this issue, this paper proposes the RConvNeXt–ECGA framework. The main contributions are twofold: (1) RConvNeXt is a convolutional module based on ConvNeXt, which achieves efficient multi-scale feature extraction through grouped parallel convolutions with multiple receptive fields; (2) Efficient Content-Guided Attention (ECGA) is a novel pixel-level attention mechanism, which adaptively reweights feature maps to highlight informative regions and suppress irrelevant interference. The proposed method achieves an average accuracy of 99.8% on bearing datasets from Case Western Reserve University and Huazhong University of Science and Technology, and 94.33% under cross-operating-condition tests, demonstrating superior robustness and generalization over representative deep learning-based baseline models. Full article

To address the challenges of difficult feature extraction for rolling bearing vibration signals, low efficiency in optimizing diagnostic model parameters, and the tendency to get trapped in local optima, this paper proposes an improved ICEEMDAN feature extraction method based on Bayesian optimization and adaptive noise signal ratio enhancement (BAYES-ICEEMDAN-SNR) and combines it with the improved Coriolis force optimization algorithm (ITOC) to optimize the least squares support vector machine (LSSVM) fault diagnosis model. Firstly, Bayesian optimization is used to adaptively determine the noise parameters and introduce a dynamic signal-to-noise ratio adjustment mechanism to enhance the robustness of feature extraction; secondly, Chebyshev chaotic mapping, Cauchy mutation, and dynamic reverse learning strategies are applied to enhance the global search and local escape capabilities of ITOC, thereby optimizing the hyperparameters of LSSVM; and finally, the Keesey-Chestnut University bearing dataset and Huazhong University of Science and Technology gear dataset are used for verification. The experimental results show that the average fault identification accuracy of the proposed method reaches over 97%, which is superior to that of the comparison models, and the effectiveness of each core improvement module of the proposed model is verified through ablation experiments, providing an effective solution for intelligent fault diagnosis of rolling bearings and gears. Full article

This work presents a lightweight and interpretable framework for the early warning of voltage stability degradation in distribution networks, based on fractal and spectral features from flow measurements. We propose a Fast Voltage Stability Index (FVSI), which combines four independent indicators: the Detrended Fluctuation Analysis (DFA) exponent α (a proxy for long-term correlation), the width of the multifractal spectrum Δα, the slope of the spectral density β in the low-frequency range, and the $c_2$ curvature of multiscale structure functions. The indicators are calculated in sliding windows on per-node series of voltage in per unit Vpu and reactive power Q, standardized against an adaptive rolling/first-N baseline, and anomalies over time are accumulated using the Exponentially Weighted Moving Average (EWMA) and Cumulative SUM (CUSUM). A full online pipeline is implemented with robust preprocessing, automatic scaling, thresholding, and visualizations at the system level with an overview and heat maps and at the node level and panel graphs. Based on the standard IEEE 13-node scheme, we demonstrate that the Fractal Voltage Stability Index (FVSI_Fr) responds sensitively before reaching limit states by increasing α, widening Δα, a more negative $c_2$, and increasing β, locating the most vulnerable nodes and intervals. The approach is of low computational complexity, robust to noise and gaps, and compatible with real-time Phasor Measurement Unit (PMU)/Supervisory Control and Data Acquisition (SCADA) streams. The results suggest that FVSI_Fr is a useful operational signal for preventive actions (Q-support, load management/Photovoltaic System (PV)). Future work includes the calibration of weights and thresholds based on data and validation based on long field series. Full article

Dimensionality reduction is fundamental for analyzing high-dimensional data, supporting visualization, denoising, and structure discovery. We present a systematic, large-scale benchmark of three widely used methods—Principal Component Analysis (PCA), Isometric Mapping (Isomap), and t-Distributed Stochastic Neighbor Embedding (t-SNE)—evaluated by average silhouette scores to quantify cluster preservation after embedding. Our full factorial simulation varies sample size $n\in \{100,200,300,400,500\}$, noise variance ${\sigma }^2\in \{0.25,0.5,0.75,1,1.5,2\}$, and feature count $p\in \{20,50,100,200,300,400\}$ under four generative regimes: (1) a linear Gaussian mixture, (2) a linear Student-t mixture with heavy tails, (3) a nonlinear Swiss-roll manifold, and (4) a nonlinear concentric-spheres manifold, each replicated 1000 times per condition. Beyond empirical comparisons, we provide mathematical results that explain the observed rankings: under standard separation and sampling assumptions, PCA maximizes silhouettes for linear, low-rank structure, whereas Isomap dominates on smooth curved manifolds; t-SNE prioritizes local neighborhoods, yielding strong local separation but less reliable global geometry. Empirically, PCA consistently achieves the highest silhouettes for linear structure (Isomap second, t-SNE third); on manifolds the ordering reverses (Isomap > t-SNE > PCA). Increasing ${\sigma }^2$ and adding uninformative dimensions (larger p) degrade all methods, while larger n improves levels and stability. To our knowledge, this is the first integrated study combining a comprehensive factorial simulation across linear and nonlinear regimes with distribution-based summaries (density and violin plots) and supporting theory that predicts method orderings. The results offer clear, practice-oriented guidance: prefer PCA when structure is approximately linear; favor manifold learning—especially Isomap—when curvature is present; and use t-SNE for the exploratory visualization of local neighborhoods. Complete tables and replication materials are provided to facilitate method selection and reproducibility. Full article

Marine magnetic surveys are vast and time-consuming, and researchers have long been seeking an economical mode for large-area data acquisition. A towed magnetic measurement system was developed based on the motion characteristics of the wave glider. By modifying the SeaSPY2 magnetometer, a twin-body towed configuration was developed, in which an S-shaped towing cable mitigates motion-induced impacts from the platform, and a high-precision GNSS positioning module was integrated into the system. Sea trials were conducted in the coastal waters near Qingdao. The results indicated that the system achieved an average cruising speed of 0.56 m/s, with the towed body’s pitch and roll angles controlled within ±5° and ±1°, respectively. The dynamic noise was measured at 0.0639 nT (Level 1), and the internal consistency for repeated survey lines and cross lines was 1.832 nT and 1.956 nT, respectively, meeting the requirements of marine magnetic survey standards. The system offers unmanned operation, zero carbon emissions, and a minimal environmental footprint, and long endurance, supporting applications such as nearshore exploration, mapping in sensitive marine areas, and underwater magnetic target detection. The research provides a novel unmanned technological solution for deep-sea magnetic surveys and lays the foundation for low-cost, cluster-based operations. Full article

Time series prediction of aerodynamic noise is critical for oscillatory instabilities analyses in fluid systems. Due to the significant dynamical and non-stationary characteristics of aerodynamic noise, it is challenging to precisely predict its temporal behavior. Here, we propose a method combining variational mode decomposition (VMD) and echo state network (ESN) to accurately predict the time series of aerodynamic noise induced by flow around a cylinder. VMD adaptively decomposes the noise signal into multiple modes through a constrained variational optimization framework, effectively separating distinct frequency-scale features between vortex shedding and turbulent fluctuations. ESN then employs a randomly initialized reservoir to map each mode into a high-dimensional dynamical system, and learns their temporal evolution by leveraging the reservoir’s memory of past states to predict their future values. Aerodynamic noise data from cylinder flow at a Reynolds number of 90,000 is generated by numerical simulation and used for model validation. With a rolling prediction strategy, this VMD-ESN method achieves accurate prediction within 150 time steps with a root-mean-square-error of only 3.32 Pa, substantially reducing computational costs compared to conventional approaches. This work enables effective aerodynamic noise prediction and is valuable in fluid dynamics, aeroacoustics, and related areas. Full article

In response to the challenges posed by complex and variable operating conditions of rolling bearings and the limited availability of labeled data, both of which hinder the effective extraction of key fault features and reduce diagnostic accuracy, this study introduces a model that combines a spatial attention (SA) mechanism with a multi-scale depthwise separable convolution module. The proposed approach first employs the Gramian angular difference field (GADF) to convert raw signals. This conversion maps the temporal characteristics of the signal into an image format that intrinsically preserves both temporal dynamics and phase relationships. Subsequently, the model architecture incorporates a spatial attention mechanism and a multi-scale depthwise separable convolutional module. Guided by the attention mechanism to concentrate on discriminative feature regions and to suppress noise, the convolutional component efficiently extracts hierarchical features in parallel through the multi-scale receptive fields. Furthermore, the trained model serves as a pre-trained network and is transferred to novel variable-condition environments to enhance diagnostic accuracy in few-shot scenarios. The effectiveness of the proposed model was evaluated using bearing datasets and field-collected industrial data. Experimental results confirm that the proposed model offers outstanding fault recognition performance and generalization capability across diverse working conditions, small-sample scenarios, and real industrial environments. Full article

Aiming at the limitations of existing multisensor fault diagnosis methods for rolling bearings in real industrial scenarios, this paper proposes an innovative intuitionistic fuzzy weighted least squares twin support higher-order tensor machine (IFW-LSTSHTM) model, which realizes a breakthrough in the noise robustness, adaptability to the working conditions, and the class imbalance processing capability. First, the multimodal feature tensor is constructed: the fourier synchro-squeezed transform is used to convert the multisensor time-domain signals into time–frequency images, and then the tensor is reconstructed to retain the three-dimensional structural information of the sensor coupling relationship and time–frequency features. The nonlinear feature mapping strategy combined with Tucker decomposition effectively maintains the high-order correlation of the feature tensor. Second, the adaptive sample-weighting mechanism is developed: an intuitionistic fuzzy membership score assignment scheme with global–local information fusion is proposed. At the global level, the class contribution is assessed based on the relative position of the samples to the classification boundary; at the local level, the topological structural features of the sample distribution are captured by K-nearest neighbor analysis; this mechanism significantly improves the recognition of noisy samples and the handling of class-imbalanced data. Finally, a dual hyperplane classifier is constructed in tensor space: a structural risk regularization term is introduced to enhance the model generalization ability and a dynamic penalty factor is set to set adaptive weights for different categories. A linear equation system solving strategy is adopted: the nonparallel hyperplane optimization is converted into matrix operations to improve the computational efficiency. The extensive experimental results on the two rolling bearing datasets have verified that the proposed method outperforms existing solutions in diagnostic accuracy and stability. Full article

Terrain conductivity meters (TCMs) are efficient devices for different sorts of subsurface investigations, including detecting and tracing buried utilities, such as metallic pipes and cables. However, data collected using TCMs are usually ambiguous and hard to interpret. This ambiguity originates from the complex shape of apparent conductivity anomalies, the influence of irrelevant conductive bodies, and the interference of random noise with the collected data. To overcome this ambiguity and produce more interpretable apparent conductivity maps, a three-step filtering routine is proposed and tested using different real datasets. The filtering routine begins with applying a Savitzky–Golay (SG) filter to reduce the effect of random noise. This is followed by a modified rolling ball (MRB) filter to convert the complex M-shape of the anomaly into a single trough pointing to the underground utility. Finally, a virtual resolution enhancement (VRE) filter is applied to enhance the pinpointing apex of the trough. The application of the proposed filtering routine to apparent conductivity data collected using different terrain conductivity meters over different utilities in different urban environments shows a significant improvement of the data and an effective ability to reveal masked underground utilities. The proposed triple filtering routine can be a starting point for a new generation of TCMs with a built-in operation mode for instantaneous delineation and characterization of underground utilities in real time. Full article

Rolling bearings are essential components of a rotating machinery system. Surface imperfections on bearings can alter vibration patterns, and monitoring these changes allows for the precise prediction of the bearing’s remaining useful life (RUL). To address the limitations, such as inadequate sensitivity to features and constrained time–frequency feature extraction capabilities, in conventional methods for predicting the RUL of rolling bearings in the early stages of degradation, this paper introduces a novel predictive framework that combines dynamic weighting mechanisms with hybrid deep learning. This framework incorporates a continuous wavelet transform to generate two-dimensional time–frequency feature maps as degradation indicators, employs CNN for extracting local detailed features, integrates iTransformer modules with dynamic weighting mechanisms to enhance the focus on early subtle features, and leverages the time-dependent modeling capabilities of BiLSTM. The experimental findings using truncated samples from IEEE-PHM2012 datasets show a 71.82% reduction in errors compared with traditional CNN in the early prediction stages, where it effectively mitigated the challenge of early degradation features being overshadowed by noise. Ablation experiments on model components further validated the effectiveness of the model architecture design, where the dynamic weighting mechanism contributed significantly (29.92%) to improving the mean absolute error (MAE). Full article

With the increasing complexity of mechanical equipment and diversification of deep learning models, vibration signals collected from such equipment are susceptible to noise interference. Moreover, traditional neural network models struggle to be effectively deployed in production environments with limited computational resources, severely impacting the accurate extraction and effective diagnosis of FK fault characteristics. In response to this challenge, this study proposes a fault diagnosis method for rolling bearings, integrating a lightweight ShuffleNetV2 network with variational mode decomposition (VMD) and the fast kurtogram (FK) algorithm. Initially, this paper introduces an enhanced FK method where the VMD algorithm is employed for data denoising, extracting FK post-denoising. These feature maps not only preserve critical signal information but also simplify data complexity. Subsequently, these feature maps are utilized to train and test the ShuffleNetV2 model, facilitating effective fault identification and classification. Ultimately, by conducting experimental comparisons with several mainstream lightweight network models, such as MobileNet and SqueezeNet, as well as traditional convolutional neural network models, this study validates the effectiveness of the proposed method in extracting fault characteristics from vibration signals, demonstrating superior diagnostic accuracy and computational efficiency. This provides a novel technical approach for health monitoring and fault diagnosis of industrial bearings and offers theoretical and experimental support for the deployment of lightweight networks in industrial applications. Full article

The vibration signals collected by acceleration sensors are interspersed with noise interference, which increases the difficulty of fault diagnosis for rolling bearings. For this reason, a rolling bearing fault diagnosis method based on complete ensemble empirical model decomposition with adaptive noise (CEEMDAN) and improved convolutional neural network (CNN) is proposed. Firstly, the original vibration signal is decomposed into a series of intrinsic modal function (IMF) components using the CEEMDAN algorithm, the components are filtered according to the correlation coefficients and the signals are reconstructed. Secondly, the reconstructed signals are converted into a two-dimensional grey-scale map and input into a convolutional neural network to extract the features. Lastly, the features are inputted into a support vector machine (SVM) with the optimised parameters of the grey wolf optimiser (GWO) to perform the identification and classification. The experimental results show that the rolling bearing fault diagnosis method based on CEEMDAN and CNN-SVM proposed in this paper can significantly reduce the noise interference, and its average fault diagnosis accuracy is as high as 99.25%. Therefore, it is feasible to apply it in the field of rolling bearing fault diagnosis. Full article

(1) Background: Rolling bearings are important components in mechanical equipment, but they are also components with a high failure rate. Once a malfunction occurs, it will cause mechanical equipment to malfunction and may even affect personnel safety. Therefore, studying the fault diagnosis methods for rolling bearings is of great significance and is also a current research hotspot and frontier. However, the vibration signals of rolling bearings usually exhibit nonlinear and non-stationary characteristics, and are easily affected by industrial environmental noise, making it difficult to accurately diagnose bearing faults. (2) Methods: Therefore, this article proposes a rolling bearing fault diagnosis model based on an improved dung beetle optimizer (DBO) algorithm-optimized variational mode decomposition-convolutional neural network-bidirectional long short-term memory (VMD-CNN-BiLSTM). Firstly, an improved DBO algorithm named CSADBO is proposed by integrating multiple strategies such as chaotic mapping and cooperative search. Secondly, the optimal parameter combination of VMD was adaptively determined through the CSADBO algorithm, and the optimized VMD algorithm was used to perform modal decomposition on the bearing vibration signal. Then, CNN-BiLSTM was used as the model for fault classification, and hyperparameters of the model were optimized using the CSADBO algorithm. (3) Results: Finally, multiple experiments were conducted on the bearing dataset of Case Western Reserve University, and the proposed method achieved an average diagnostic accuracy of 99.6%. (4) Conclusions: Experimental comparisons were made with other models to verify the effectiveness of the proposed model. The experimental results show that the proposed model based on an improved DBO algorithm optimized VMD-CNN-BiLSTM can effectively be used for rolling bearing fault diagnosis, with high diagnostic accuracy, and can provide a theoretical reference for other related fault diagnosis problems. Full article