Acoustic Black Hole (ABH) structures have emerged as a promising passive solution for broadband vibration attenuation by concentrating flexural energy in a power-law taper. Yet, most research studies assume perfectly smooth tapers and undamaged laminates, whereas real composite ABHs inevitably suffer from machining inaccuracies and service-induced defects. This gap is critical for laminated composites, which are especially susceptible to scratches and delaminations during ABH fabrication. In this study, using Born's approximation and Rayleigh-Ritz method, a formulation that quantifies both gradient-induced and defect-induced reflections in tapered beams is developed, showing that the diverging local wavenumber near the ABH tip suppresses scattering provided the taper exponent m > 2. The theoretical framework yields explicit robustness bounds and defect metrics. Experiments on laminated composite ABH beams with intentional damage, subjected to both stepped-sine and broadband random excitation, corroborate the analysis. Major findings include (i) sine and random excitation yield coincident frequency response functions with high coherence, validating the Linear Time Invariance behavior; (ii) undamaged ABHs consistently outperform uniform and equivalent-mass references across measurement points; and (iii) even with realistic defects, ABHs retain a substantial attenuation margin. Overall, the combined theory-experiment study establishes that while taper smoothness is fundamental, the ABH effect is remarkably robust against localized im perfections. The results provide practical design metrics for defect-tolerant composite ABHs and offer guidance for manufacturing and application in real-world vibration control systems.

In industrial equipment condition monitoring, utilizing tri-axial vibration data acquired from a single sensor poses a critical challenge due to hardware and space constraints. Traditional methods often treat multiaxis signals as independent channels or simple concatenations, neglecting the underlying dynamic coupling mechanisms and failing to fully mine latent fault features. To address this, this article proposes a novel fault diagnosis method for rotating machinery based on space-time decoupling graph construction and graph convolutional networks (GCNs). Addressing the topological constraints of single-sensor scenarios, the present article proposes a time-point-based graph construction strategy that shifts from the conventional sensor-as-node paradigm to the reconstruction of high-dimensional dynamic graphs from time-series data. Adhering to the space-time decoupling principle, the method employs local sparse connections to constrain temporal evolution and utilizes Euclidean distance within the tri-axial feature space to quantify state differences; this mechanism facilitates the adaptive transformation of fault-induced transient impulses into high-weight graph edges. By leveraging a GCN model to aggregate nonlinear spatiotemporal features, the proposed method demonstrates superior performance, yielding an average recognition accuracy of over 99% on standard bearing fault datasets. These results validate that the proposed graph construction strategy can effectively capture dynamic correlations and weak features without relying on multisensor arrays, offering a promising solution for equipment maintenance under hardware constraints.

To overcome the inherent weakness in strength and stiffness of embedded Acoustic Black Holes (ABHs), this study proposes an additive ABH cylindrical shell for vibration attenuation in rotating disk-shell coupled structures. A dynamic model is established using the Rayleigh-Ritz method, where Sanders shell theory and Mindlin plate theory describe the strain-displacement relations, and rotational effects are systematically incorporated. The connections between components are simulated via artificial springs. The derived equations of motion are solved using the state-space representation method. The model is validated against Finite Element Analysis and experimental results. The forced vibration responses under a range of rotational speeds are analyzed. Numerical results demonstrate that the additive ABH provides significant vibration reduction across different rotational speeds, with a notable suppression effect in the high-frequency region. It is observed that the effectiveness of vibration attenuation exhibits variation with increasing rotational speed. This work provides a validated modeling framework for applying additive ABHs in rotating machinery.

Significant progress has been made in rolling bearing fault diagnosis based on unsupervised domain adaptation (UDA). However, in complex operating conditions, especially under time-varying speed conditions, these methods still face critical challenges, including severe distribution discrepancies between source and target domains and limited interpretability of the diagnostic process. To address these issues, this paper proposes a novel interpretable dynamic weighted domain adaptation network (DWDAN), which combines a discrete wavelet-guided attention (DW-GA) layer with dynamic weighted joint domain adaptation (DWJDA) to achieve subdomain-level alignment while enhancing diagnostic interpretability. Specifically, the DW-GA layer incorporates physical prior knowledge to guide the model in extracting fault-related features in the wavelet domain, thereby improving the efficiency and effectiveness of feature learning under time-varying speed conditions. The DWJDA further performs a refined dynamic adjustment of marginal and conditional distribution alignment by jointly considering intra-class sample weighting and the relative importance of different distribution discrepancies, leading to enhanced domain adaptation capability. The comparative experiments are conducted on the datasets of Huazhong University of Science and Technology (HUST) and Northeast Forestry University (NEFU). The experimental results show that, compared with other mainstream methods, DWDAN exhibits significant advantages in cross-domain fault diagnosis tasks under time-varying speeds, achieving a maximum average diagnostic accuracy of 99.08%. Further ablation experiments further verify the effectiveness of each key module in improving both diagnostic performance and interpretability, indicating that DWDAN has strong application potential for bearing fault diagnosis under time-varying speed.

Magnetorheological (MR) dampers provide tunable stiffness and damping properties for adaptive rotor systems, yet the underlying rheological transitions governing their performance remain insufficiently characterized. This work introduces a novel dynamic model of an MR damper based on a smooth-transition constitutive equation, accurately capturing progressive yielding and re-yielding behavior of MR fluids under coupled axial-circumferential shear. The model is consistently validated through theoretical analysis and targeted MR-damper rotor experiments, demonstrating reliable predictive capability at both local and system levels. High-resolution simulations reveal that increasing magnetic field strength can induce a topological transformation of the oil-film pressure-field distribution, where broad shear plateaus evolve into localized high-pressure peaks driven by yield-surface contraction and migration. This reconfiguration alters the phase relationship between load and motion, giving rise to a previously unreported non-monotonic damping response as rotor eccentricity grows. At large eccentricities, shear-rate dominance diminishes the relative contribution of field-dependent yield stress, triggering a saturation regime that limits further controllability. These findings provide multiscale insights that connect film-scale yield evolution and flow-field reorganization to the macroscopic dynamic characteristics of MR dampers, offering critical design guidance for smart MR-fluid-based

Aero engines are widely used in modern aviation due to their high thrust-to-weight ratio, high efficiency, and high reliability, placing greater demands on the operational safety of key components such as bearings. Traditional bearing fault diagnosis methods typically rely on vibration signals collected by a single sensor, which makes it difficult to handle challenges such as incomplete information and noise interference in industrial settings. The paper proposes an intelligent fault diagnosis model called the Time-Frequency Attention Network, which is based on a time-frequency-aware convolutional layer and a fused attention mechanism. The goal is to fully exploit the time-frequency feature information from multi-sensor signals. First, a time-frequency-aware convolutional layer is designed using a kernel function constrained by the Short-Time Fourier Transform, leveraging a complex-valued convolution structure to effectively extract non-stationary features and local instantaneous frequency variations. Subsequently, a fused attention module is constructed, introducing a dual-attention mechanism in both channel and spatial dimensions to adaptively adjust the response intensity and frequency-domain focus areas of different sensor signals. The proposed network is experimentally validated on the Harbin Institute of Technology bearing dataset, achieving an accuracy of 99.54%. The results demonstrate that the proposed method outperforms existing benchmark models in terms of fault recognition accuracy and robustness, showcasing excellent diagnostic performance and generalization ability.

Bearing fault diagnosis under varying operating conditions often suffers from insufficient feature representation and unstable performance. Many existing spiking neural network (SNN)-based methods use fixed neuronal dynamic parameters, which may limit their ability to accommodate operating-condition variations. Moreover, some diagnostic frameworks construct two-dimensional time-frequency representations before model training, introducing an additional offline transformation stage. In addition, some SNN-based diagnostic frameworks place less emphasis on explicit frequency-domain information. To address these issues, this paper proposes a time-frequency fusion SNN for bearing fault diagnosis based on learnable neuronal dynamics. First, one-dimensional vibration segments are replicated across four simulation time steps, and spectral information is modeled by an embedded Sinc-based branch. Then, temporal features and frequency-domain information are fused within a unified SNN framework, where learnable neuronal dynamics adjust temporal integration and spike-generation behavior during training to better match vibration patterns at different temporal scales. Experiments on the MFPT and JNU datasets show that the proposed model achieves accuracies of 98.33% and 99.33%, respectively, demonstrating competitive diagnostic performance.

As typical and complex mechatronic system, health state of the wind turbine (WT) is of significant importance to the sustained and reliable service. However, it is noted that influenced by the seasonal or fitful wind, WTs unavoidably serve in the dynamically varying environment. In this event, most of the currently available indicators expose deficiency in regard of the false or missed alarms due to the coupled condition interference. To address this issue and improve the reliability of the mechatronic system, a novel statistic discrepancy oriented cyclo-non-stationary (CNS) indicator is developed in this article. First, characteristics of the recorded degradation samples are revealed by a multiparametric model, during which the consistency is verified and improved by the hypothesis test. Second, a specific speed-dependent slicing (SDS) operator is then designed, aiming to alleviate the varying-speed-induced modulation interference at the different degradation stages. With this developed SDS operator, a CNS indicator, which can well adapt to the dynamically varying environment during the operating process, is subsequently developed by incorporating the resampling-based statistic discrepancy evaluating mechanism. Experiments indicate that the proposed method can effectively characterize the health state of the transmission parts of the industrial WT under varying speed conditions.

As a core component of the aeroengine, the main shaft bearing faces significant challenges in practical fault diagnosis, including the difficulty of simultaneously capturing both local and global features during feature extraction, and the scarcity of high-quality labeled data under varying operating conditions, which limit the generalization ability of diagnostic models. To address these issues, this article proposes a lightweight transfer learning-based fault diagnosis method built upon a Bi-Sandwich architecture and maximum mean square difference (MMSD). A novel Bi-Sandwich model is designed by integrating multiscale separable convolutions and a broadcast attention mechanism, enabling effective extraction of both local and global features while reducing computational complexity. To mitigate domain discrepancies under different working conditions, an MMSD-based domain adaptation metric using second-order statistics is introduced to align feature distributions between the source and target domains. Experimental results on two aero-engine main shaft bearing datasets demonstrate that the proposed method achieves excellent fault identification accuracy and cross-domain generalization across different loads and rotational speeds.

To address the challenges of inaccurate dynamic characterization and insufficient control robustness in damaged active constrained layer damping (ACLD) structures, this paper proposes an integrated framework for high-fidelity modeling and active vibration control of composite laminates with transverse cracks. Built on the higher-order shear deformation theory and layerwise theory, the proposed model introduces a damage coefficient to degrade the stiffness matrix of cracked elements. By capturing the impact of damage evolution on natural frequencies and vibration responses, the model significantly improves the predictive accuracy of the system's structural dynamics. Furthermore, to enhance active control robustness against damage-induced perturbations, an adaptive neuro-fuzzy inference system (ANFIS) is utilized to dynamically optimize the bandwidth parameters of a linear active disturbance rejection controller (LADRC). The controller's robustness is rigorously validated against +/- 10% uncertainties in both the system dynamic matrix and the Kalman filter estimator matrix. Simulation results demonstrate that, compared to the baseline LQR/linear matrix inequalities controller, the ANFIS-LADRC strategy achieves rapid vibration convergence within 0.2 s and an attenuation exceeding 6 dB for the second-order mode. This confirms its superior vibration suppression capability and exceptional robustness under severe parametric uncertainties. The proposed framework provides a reliable theoretical basis for the intelligent vibration control and life extension of damaged ACLD structures.

Bearings are indispensable components in modern industrial systems, whose operating conditions are commonly monitored using vibration-based non-destructive testing (NDT) techniques. In practical applications, fault diagnosis models face challenges such as the difficulty of simultaneously capturing local and global features, as well as variations across operating conditions and equipment. A novel fault diagnosis approach is proposed, which leverages transfer learning and combines convolution with Transformer architectures. A shallow, learnable aggregator is designed to extract local fault information, where local dot-product operations replace token-based computations to reduce model complexity. A deep aggregator captures global dependencies through token interactions. By combining shallow and deep aggregators, the model achieves comprehensive feature representation of fault signals. Furthermore, the Higher-order Joint Maximum Mean Discrepancy algorithm was proposed by combining first-order and second-order statistics to measure the distance between the source domain and the target domain. The experimental findings indicate that the developed method attains high diagnostic accuracy with low computational complexity across multiple transfer tasks, offering dependable support for vibration-based non-destructive bearing fault diagnosis.

In real industrial scenarios, distribution discrepancies in vibration signals across varying working conditions can weaken the generalisation capacity of fault diagnosis models, which negatively impacts diagnostic accuracy. To address this limitation, this paper proposes a multi-scale kernel invariant feature domain adversarial network (Msk-IFDAN). The method consists of three core components: an improved feature extractor, a classifier and a conditional domain discriminator. The feature extractor integrates a multi-scale kernel efficient channel attention (Msk-ECA) mechanism to achieve multi-scale channel interaction feature extraction and reduce feature redundancy. Meanwhile, invariant feature learning (IFL) is applied to learn domain-invariant features, enhancing the adaptability of the model to improve the generalisation capacity of cross-domain diagnosis of this method. The unified domain adaptation module of the design achieves precise alignment of feature distributions by reducing the distribution discrepancies through adversarial training strategies. Comprehensive comparative experiments on the Jiangnan University (JNU) dataset and Northeast Forestry University (NEFU) datasets demonstrate the effectiveness of the proposed method, with diagnostic accuracy reaching over 99%. The method not only enables accurate cross-domain fault diagnosis of rolling bearings but also exhibits good stability, effectively solving the challenges of cross-domain fault diagnosis for rolling bearings in industrial scenarios.

Significant domain-shifts between partial source-target domains heavily hinder domain adaptation and degrade transfer fault diagnosis performance. This paper proposes a multi-source weighted domain adaptation (MSWDA) framework for cross-domain diagnosis. Initially, a multi-source domain weighting strategy based on subspace similarity is designed to guide the model to prioritize learning features from high-weight source domains during domain adaptation. Subsequently, a cross-layer hybrid attention network is developed to enhance essential domain-invariant features. Furthermore, a multi-objective collaborative optimization strategy is proposed to comprehensively enhance the cross-domain diagnostic capability. Finally, target-domain transfer diagnosis is achieved using well-trained MSWDA model. Comparative experiments on two mechanical transmission datasets indicate MSWDA attains the highest diagnostic accuracy of 98.48% and 96.08% compared to advanced methods, respectively, verifying its superior capabilities for cross-domain diagnostics.

Wind turbines (WTs) have become a significant contributor to the renewable and sustainable energy around the world by converting wind energy into electricity with feasible cost production, reliability and efficiency. Therefore, wind turbine blades (WTBs), as the most basic and critical components in WT systems, with good design, reliable vibration suppression and superior performance are the decisive factors to ensure the normal and stable operation of WTs. However, complex and irregular loads, materials and configurations will cause unhealthy nonlinear vibrations of large-scale WTBs, which not only reduce the service life and power generation efficiency of WTs, but also increase monitoring errors, operation risks and maintenance costs. Therefore, vibration analysis of WTBs is an important basis for the study of the failure avoidance, maintenance planning and operation sustainability of WTB structure design. In this paper, the current state-of-the-art computational methods research of WTBs in vibration patterns, dynamic modeling methods, vibration suppression techniques are comprehensively outlined, and the merits and demerits of each vibration analysis methods are discussed. The future challenges and research prospects of WTBs are then provided in detail.

Time-frequency analysis (TFA) technology is an effective tool for revealing hidden transient impulse features in signals. Current TFA methods are modeled based on the continuity assumption of time-domain signals, leading to issues such as time-frequency energy diffusion, inapplicability to impulse components, and over-squeezing of time-frequency features when extracting impulse characteristics. To achieve effective extraction of transient impulses, this paper develops a TFA method called transient scaling extraction CT (TSECT) with scaling-basis chirplet transform as its core. By using the Dirac function, which possesses time-localization capability, as the signal model, the time-frequency energy distribution of the transient structure of impulse components is deeply analyzed with respect to the scale basis function. This allows the construction of a transient scaling extraction operator to reassign the time-frequency coefficients and eliminate diffused time-frequency energy. Comparative analysis of simulated signals with impulse components demonstrates the effectiveness of TSECT. The generalization applicability of TSECT in the fields of mechanical fault diagnosis and biological signals is illustrated through vibration signals from real faulty bearings and echolocation signals of brown bats. Furthermore, while ensuring the complete characterization of time-frequency features, R & eacute;nyi entropy (RE) and Stankovic concentration measure (SCM) are adopted as quantitative measures of time-frequency energy concentration. The numerical results of RE and SCM for TSECT are lower than those of other TFA methods, indicating that TSECT achieves superior energy concentration. Additionally, a comparison of computation times shows that the computational cost of TSECT is comparable to that of many advanced TFA methods.

The self-powered and self-sensing method for bearings enables the timely detection of fault information under constraints such as limited installation space and restricted power supply, thereby enhancing the safety and reliability of machines. To enhance their practical applicability, this study integrates bimodal energy harvesters with a low-power wireless module, thereby proposing an intelligent end cover system (IECS) that enables comprehensive functionalities such as self-sensing, intelligent diagnosis, and wireless data transmission. Specifically, bimodal energy harvesters consist of a triboelectric nanogenerator (TENG) and a variable reluctance generator (VRG) that work together to capture mechanical energy. The TENG was used for self-powered monitoring, and the VRG provided power to the wireless module. The structural parameters of each module were optimized to enhance the output performance while preventing component wear and avoiding interference with the bearing operation. The effectiveness and wireless sensing function of the IECS are validated using a bearing testing platform, where the precise mapping relationship between the frequency characteristics of the output signals and the operational status of the bearing serves as the foundation for condition monitoring. Furthermore, an intelligent diagnostic model based on ResNet18 was constructed to diagnose bearing faults accurately. The IECS offers a viable pathway for the development and practical industrial applications of multifunctional integrated self-powered sensing systems.

To mitigate the degradation in transfer diagnostic performance caused by unseen target domains in complex industrial scenarios, a novel saliency-aware heterogeneous independence decoupling network (SA-HIDN) is proposed for mechanical transfer fault diagnosis. Initially, a heterogeneous decoupled feature extractor is constructed to align the network capacity with the distinct physical attributes of signal components. It employs a deep branch with deep resonance shrinkage to perform soft-thresholding denoising for domain-invariant fault impulses, and a shallow branch with a statistical feature layer to explicitly capture global statistical moments as domain-specific condition features. Subsequently, a statistical independence constraint based on the Hilbert-Schmidt Independence Criterion (HSIC) is introduced to mathematically force the fault and condition subspaces to be independent in the reproducing kernel Hilbert space, effectively suppressing high-order information leakage between decoupled branches. Finally, a saliency-aware adaptive fusion (SAAF) module is developed to proactively protect subtle fault signatures and achieve high-fidelity feature reconstruction through a saliency-masking compensation mechanism. Extensive experiments on planetary transmission and train transmission system datasets demonstrate that the proposed SA-HIDN achieves transfer diagnostic accuracies of 98.13% and 94.48% on two datasets, respectively. These quantitative results and mechanistic analyses confirm that the proposed method significantly outperforms state-of-the-art methods while maintaining a favorable balance between diagnostic precision and computational cost.

Aiming at the limitations of traditional graph convolutional networks (GCNs) in dynamic feature capture and multi-dimensional feature fusion, this paper proposes a novel method called multi-level adaptive fusion and edge-enhanced attention GCN (ME-GCN). The ME-GCN integrates the multi-level dynamic collaborative fusion mechanism, which combines feature-space and topology-space graph convolution modules, enabling comprehensive extraction of local features and global topological relations. By introducing the edge-enhanced graph attention mechanism, the association weights between nodes are dynamically adjusted, thereby enhancing the modeling capability of complex fault patterns. Moreover, the adaptive fusion strategy dynamically balances and optimizes multi-module features, achieving efficient and robust fault representation. Experimental validation on both public datasets and real industrial datasets demonstrates that ME-GCN achieves superior diagnostic performance with strong robustness and generalization ability under different operating conditions.

Root mean square (RMS) of vibration is one of the most widely used health indicators (HIs) for rotating machinery condition monitoring. However, the RMS is affected by the operation condition (e.g., rotating speed, torque). When the operation condition changes, the RMS usually changes accordingly, leading to possible false alarms. This paper develops a simple yet effective method to attenuate the effects of operation condition on RMS with the use of operation condition data. First, an optimal piecewise linear function is fitted between the RMS and operation condition data when the monitored machine is healthy. The fitted function estimates the effects of operation condition on RMS. For upcoming data stream (including RMS and operation condition data), the fitted function is used to provide the predicted RMS. The ratio of the real RMS and the predicted RMS is the indicator which is named normalized RMS (NRMS) for condition monitoring. The effectiveness of proposed NRMS is validated over a field-collected wind turbine dataset.