Search Results (1,244)

Bearing fault diagnosis encounters limitations including insufficient accuracy, elevated model complexity, and demanding hyperparameter optimization. This research introduces a diagnostic framework combining variational mode decomposition (VMD) and fast Fourier transform (FFT) for extracting comprehensive temporal–spectral characteristics from vibration data. The methodology employs a hybrid deep learning architecture integrating convolutional neural networks (CNNs) with Transformers, where CNNs identify local features while Transformers capture extended dependencies. Meta-learning-enhanced Bayesian optimization and HyperBand (Meta-BOHB) is utilized for efficient hyperparameter selection. Evaluation on the Case Western Reserve University (CWRU) dataset using 5-fold cross-validation demonstrates a mean classification accuracy of 99.91% with exceptional stability (±0.08%). Comparative analysis reveals superior performance regarding precision, convergence rate, and loss metrics compared to existing approaches. Cross-dataset validation using Mechanical Fault Prevention Technology (MFPT) and Paderborn University (PU) datasets confirms robust generalization capabilities, achieving 100% and 98.75% accuracy within 5 and 7 iterations, respectively. Ablation studies validate the contribution of each component. Results demonstrate consistent performance across diverse experimental conditions, indicating significant potential for enhancing reliability and reducing operational costs in industrial fault diagnosis applications. The proposed method effectively addresses key challenges in bearing fault detection through advanced signal processing and optimized deep learning techniques. Full article

The planetary bearing looseness fault can cause the planetary gear train to fail. Conventional modeling methods do not consider complex component-coupling relationships for fault feature analysis. As a result, a joint model is developed to examine the dominant relationship between planetary bearings and the planetary gear train. Firstly, the planetary bearing is modeled in the normal and fault states. Then, a refined joint planetary gear train dynamic model is constructed, which is composed of the planetary gears, the ring gear, the carrier, the sun gear, and the planetary bearings. Finally, the simulation results show that, when the planetary bearing is in the looseness fault state, its fault characteristic presents as the rotation frequency of the carrier and its harmonics. The on-site signal of a 2.0 MW wind turbine is used to verify the effectiveness of the model. The proposed model can provide the basis for the fault mechanism analysis and fault diagnosis of rolling bearing outer ring looseness. Full article

This paper presents an experimental method for determining the suitable bore diameter of bearing housings made of polymer designated as PA6, which enables multiple bearing replacement processes. Preceded by analytical calculation, four distinct series of housing samples (each with varying production tolerances) were subjected to testing, where each series comprised three housing samples with identical tolerance specifications. The assembly and disassembly processes of press-fit joints were thoroughly monitored using a force sensor, complemented by equipment for measuring the roughness of contact surfaces. Based on the experimental findings, a recommendation is provided for an appropriate interference fit for the tested bearing housing, providing a suitable solution for multiple maintenance processes. As a summary, the idea of this research is to define the prototype solution for the interference fit of a rolling bearing installed in a PA6 housing. Methods used to examine the proposed solution were surface topography and roundness measuring of PA6 housings, while the press-fitting and dismantling tests of rolling bearings in/from PA6 housings were used to verify it. Full article

Conventional signal-to-image conversion methods often overlook the physical correspondence of vibration signals, limiting diagnostic interpretability. To address this, we propose a physics-guided image construction strategy that incorporates dimensionless indicators to adaptively weight grayscale regions, enhancing the physical consistency and the discriminability among different fault types. Furthermore, a novel Cheap Channel Obfuscation module is introduced to suppress noise, decouple feature channels, and preserve the critical information within lightweight models. Integrated with ShuffleNetV2, our method achieves high diagnostic accuracy. Experimental validation for CWRU and SEU bearing datasets yields accuracies of 100% and 99.91%, respectively, demonstrating superior performance with minimal parameters. This approach offers a technically robust and computationally efficient fault diagnosis solution, with promising potential for deployment in resource-limited industrial environments. Full article

Due to their simple design and versatility, rolling bearings are widely used in various industrial and engineering applications. One of the key parameters characterizing ball bearings is the frictional moment (also referred to as resisting torque). Excessive torque values can increase energy consumption, which is undesirable from an energy efficiency standpoint. In response to the increasing demand for energy-efficient solutions, studies on the frictional moments of ball bearings are gaining particular significance. Numerous research studies have been conducted to investigate the factors that affect this parameter in rolling bearings. However, in the case of rolling bearings with contact seals, accurately evaluating these relationships is challenging due to the instability of frictional moment values observed during measurements. Therefore, this paper presents a study aimed at evaluating the impact of rubber seal geometry (specifically roundness and waviness deviations) on the value of friction torque in 6304-type ball bearings. It is important to note that manufacturers employ various types of seals. This study presents a preliminary qualitative assessments of the manufacturing quality of rubber contact seals from selected producers. Form deviations of the rubber seals were analyzed using a multisensor O-Inspect coordinate measuring machine. The frictional moment of rolling bearings was measured using a dedicated measurement system developed at Kielce University of Technology. Measurements were conducted under two axial load values (70 N and 135 N) and two rotational speeds (50 rpm and 1800 rpm). Based on qualitative observations, the dominant factor influencing the frictional moment magnitude was identified. Full article

This paper presents a fault diagnosis model for rolling bearings that addresses the challenges of establishing long-sequence correlations and extracting spatial features in deep-learning models. The proposed model combines SENet with an improved Informer model. Initially, local features are extracted using the Conv1d method, and input data is optimized through normalization and embedding techniques. Next, the SE-Conv1d network model is employed to enhance key features while suppressing noise interference adaptively. In the improved Informer model, the ProbSparse self-attention mechanism and self-attention distillation technique efficiently capture global dependencies in long sequences within the rolling bearing dataset, significantly reducing computational complexity and improving accuracy. Finally, experiments on the CWRU and HUST datasets demonstrate that the proposed model achieves accuracy rates of 99.78% and 99.45%, respectively. The experimental results show that, compared to other deep learning methods, the proposed model offers superior fault diagnosis accuracy, stability, and generalization ability. Full article

Rolling bearings are critical components in mechanical systems, and their health directly affects operational reliability and safety. However, their exposure to harsh conditions makes accurate fault diagnosis essential. Conventional methods relying on expert knowledge and handcrafted features are inefficient, while deep learning still suffers from data imbalance, which limits generalization. To address this challenge, an Attention-Enhanced Conditional Wasserstein GAN (ACWGAN) is proposed, in which the attention mechanism is incorporated into both the generator and discriminator to capture global dependencies and enhance feature diversity. By combining attention guidance with the Wasserstein distance, the framework achieves more stable training, alleviates mode collapse, and generates high-fidelity fault samples to balance imbalanced datasets. Compared with existing GAN-based methods, this method, combined with wavelet-based ResNet, significantly improves the accuracy of diagnosis, achieving 100% accuracy in the generated dataset. Full article

Metal components such as rolling bearings are prone to wear, cracks, and defects in harsh environments and long-term use, leading to performance degradation and potential equipment failures. Therefore, detecting surface cracks and other defects in rolling bearings is of great significance for ensuring equipment reliability and safety. However, traditional signal decomposition methods like EEMD and FEEMD suffer from residual noise and mode mixing issues, while deconvolution algorithms such as CYCBD are sensitive to parameter settings and struggle in high-noise environments. To mitigate the susceptibility of fault signals to background noise interference, this paper proposes a fault feature extraction method based on fast complementary ensemble empirical mode decomposition (FCEEMD) and adaptive maximum second-order cyclostationarity blind deconvolution (ACYCBD). Firstly, we propose FCEEMD, which effectively eliminates the residual noise of ensemble empirical mode decomposition (EEMD) and fast ensemble empirical mode decomposition (FEEMD) by introducing paired white noise with opposite signs, solving the problems of traditional decomposition methods that are greatly affected by noise, having large reconstruction errors, and being high time-consuming. Subsequently, a new intrinsic mode function (IMF) screening index based on correlation coefficients and energy kurtosis is developed to effectively mitigate noise influence and enhance the quality of signal reconstruction. Secondly, the ACYCBD model is constructed, and the hidden periodic frequency is detected by the enhanced Hilbert phase synchronization (EHPS) estimator, which significantly enhances the extraction effect of the real periodic fault features in the noise. Finally, instantaneous energy tracking of bearing fault characteristic frequency is achieved through Teager energy operator demodulation, thereby accurately extracting fault state features. The experiment shows that the proposed method accurately extracts the fault characteristic frequencies of 164.062 Hz for inner ring faults and 105.469 Hz for outer ring faults, confirming its superior accuracy and efficiency in rolling bearing fault diagnosis. Full article

This study addresses the condition monitoring of rolling bearings by applying robust linear regression to statistically derived features from vibration data. Four datasets of acceleration signals were collected under varying operating conditions: aligned and misaligned bearings at rotational speeds of 1000 rpm and 1500 rpm. From each signal, key statistical indicators were extracted, including root mean square (RMS), skewness, kurtosis and crest factor, to capture signal characteristics that were relevant to fault detection. To follow-up, we applied the Kolmogorov–Smirnov test to assess data normality and the results confirmed significant deviations from a Gaussian distribution, motivating the use of robust regression techniques for further investigations. The regression model created incorporated rotational speed and alignment conditions as predictors of acceleration and the results indicated that while the coefficient associated with misalignment suggested a possible increase in acceleration (~1.115 units), statistical testing (p = 0.5233) indicated that neither speed nor alignment had a significant influence on the measured vibration levels within the dataset. The findings suggest that under the tested conditions, misalignment does not manifest as a strong linear change in acceleration magnitude, and the study underscores the importance of robust modeling techniques and feature selection in the condition monitoring of rotating machinery. Full article

This study systematically investigates the surface nanocrystallization of 35CrMo steel induced by Ultrasonic Surface Rolling Processing (USRP). It reveals the formation of a gradient nanostructure, where martensite lath fragmentation under high-frequency impacts leads to a surface layer of equiaxed nanocrystals and high-density dislocations. This novel microstructure yields exceptional surface integrity: roughness is minimized to 0.029 μm due to plastic flow, residual stress is transformed into high compressive stress, and surface microhardness is significantly enhanced by 32.3%, primarily governed by grain refinement and dislocation strengthening. Consequently, the treated material exhibits a 28.9% reduction in wear mass loss, which is directly attributed to the combined effects of the strengthened gradient layer’s improved load-bearing capacity and the effective suppression of crack initiation by compressive residual stresses. Our findings not only provide direct microstructural evidence for classic strengthening theories but also offer a practical guide for optimizing the surface performance of high-strength alloy components. Full article

Recent advancements in deep learning have significantly improved fault diagnosis methods. However, challenges such as insufficient feature extraction, limited long-range dependency modeling, and environmental noise continue to hinder their effectiveness. This paper presents a novel mixture of multi-view convolutional (MOM-Conv) layers integrating the Mixture of Experts (MOE) mechanism. This design effectively captures and fuses both local and contextual information, thereby enhancing feature extraction and representation. This proposed approach aims to improve prediction accuracy under varying noise conditions, particularly in rolling ball bearing systems characterized by noisy signals. Additionally, we propose the Mamba-enhanced adaptive self-attention long short-term memory (MASA-LSTM) model, which effectively captures both global and local dependencies in ultra-long time series data. This model addresses the limitations of traditional models in extracting long-range dependencies from such signals. The architecture also integrates a multi-step temporal state fusion mechanism to optimize information flow and incorporates adaptive parameter tuning, thereby improving dynamic adaptability within the LSTM framework. To further mitigate the impact of noise, we transform vibration signals into denoised multi-channel representations, enhancing model stability in noisy environments. Experimental results show that our proposed model outperforms existing state-of-the-art approaches on both the Paderborn and Case Western Reserve University bearing datasets, demonstrating remarkable robustness and effectiveness across various noise levels. Full article

The vibration signals of gas turbine rolling bearings exhibit significant non-stationarity under complex operating conditions such as frequent start-stop cycles and variable speeds, posing a major challenge for fault diagnosis. To address this issue, this paper proposes a multi-channel variable-speed attention framework (MC-VSAttn). The method first constructs multi-channel inputs to capture rich fault information, then introduces a dynamic time-varying response module to adaptively model non-stationary features, and combines channel and spatial joint attention mechanisms to enhance selective attention to critical information, thereby achieving robust fault identification under complex operating conditions. Compared with existing methods, the proposed framework explicitly models the time-varying characteristics of non-stationary signals and jointly integrates multi-channel fusion with hierarchical attention, enabling more accurate and stable fault diagnosis across variable-speed scenarios. Experimental results based on the variable-speed datasets from Tsinghua University and Huazhong University of Science and Technology show that MC-VSAttn achieves accuracy rates of 99.14% and 98.23%, respectively. Further ablation experiments validate the key role of the dynamic time-varying response module and the joint attention mechanism in performance improvement. Full article

In recent years, deep learning has been increasingly applied in the field of fault diagnosis, but it currently faces two challenges: (1) data privacy issues prevent the aggregation of data from different users to form a large training dataset; (2) the limited memory of edge devices or handheld detection devices restricts the application of some larger structural models. To address these issues, this article proposes a lightweight federated learning method with transformer network for intelligent fault diagnosis. A federated learning architecture is constructed to achieve distributed learning of different user data, which not only ensures the privacy and security of user data, but also enables feature learning of different user data. In addition, the lightweight transformer network is built locally for different users to achieve the applicability of the model on different devices. An experimental case was implemented to demonstrate the effectiveness of the proposed method, and the results showed that the proposed method can achieve effective fault diagnosis while preserving data privacy. Compared with other methods, the proposed diagnostic model requires less computing resources. In addition, even under noisy conditions, the method maintains significant robustness against acoustic interference. Full article

Rolling bearings constitute critical rotating components within rolling mill equipment. Production efficiency and the operational safety of the whole mechanical system are directly governed by their operational health state. To address the dual challenges of the over-reliance of conventional diagnostic methods on expert experience and the scarcity of fault samples in industrial scenarios, we propose a virtual–physical data fusion-optimized intelligent fault diagnosis framework. Initially, a dynamics-based digital twin model for rolling bearings is developed by leveraging their geometric symmetry. It is capable of generating comprehensive fault datasets through parametric adjustments of bearing dimensions and operational environments in virtual space. Subsequently, a symmetry-informed architecture is constructed, which integrates multi-scale convolutional neural networks with attention mechanisms and bidirectional gated recurrent units (MCNN-AT-BiGRU). This architecture enables spatiotemporal feature extraction and enhances critical fault characteristics. The experimental results demonstrate 99.5% fault identification accuracy under single operating conditions. It maintains stable performance under low SNR conditions. Furthermore, the framework exhibits superior generalization capability and transferability across the different bearing types. Full article

Rolling element bearings are commonly used in rotating machines. Bearing fault detection and diagnosis play a critical role in machine operations to recognize bearing faults at their early stage and prevent machine performance degradation, improve operation quality, and reduce maintenance costs. Although many fault detection techniques are proposed in the literature for bearing condition monitoring, reliable bearing fault detection remains a challenging task in this research and development field. This study proposes an enhanced Teager–Kaiser (eTK) technique for bearing fault detection and diagnosis. Vibration signals are used for analysis. The eTK technique is novel in two aspects: Firstly, an empirical mode decomposition analysis is suggested to recognize representative intrinsic mode functions (IMFs) with different frequency components. Secondly, an eTK denoising filter is proposed to improve the signal-to-noise ratio of the selected IMF features. The analytical signal spectrum analysis is conducted to identify representative features for bearing fault detection. The effectiveness of the proposed eTK technique is verified by experimental tests corresponding to different bearing conditions. Full article