Search Results (482)

With the development of artificial intelligence technology, intelligent fault diagnosis methods based on deep learning have received extensive attention. Among them, convolutional neural network (CNN) has been widely applied in the fault diagnosis of rolling bearings due to its strong feature extraction ability. However, traditional CNN models still have deficiencies in the extraction of early weak fault features and the suppression of high noise. In response to these problems, this paper proposes a convolutional neural network (SAWCA-net) that integrates spectrum-guided dynamic variable-width convolutional kernels and dynamic interactive time-domain–channel attention mechanisms. In this model, the spectrum-adaptive wide convolution is introduced. Combined with the time-domain and frequency-domain statistical characteristics of the input signal, the receptive field of the convolution kernel is adaptively adjusted, and the sampling position is dynamically adjusted, thereby enhancing the model’s modeling ability for periodic weak faults in complex non-stationary vibration signals and improving its anti-noise performance. Meanwhile, the dynamic time–channel attention module was designed to achieve the collaborative modeling of the time-domain periodic structure and the feature dependency between channels, improve the feature utilization efficiency, and suppress redundant interference. The experimental results show that the fault diagnosis accuracy rates of SAWCA-Net on the bearing datasets of Case Western Reserve University (CWRU) and Xi’an Jiaotong University (XJTU-SY) reach 99.15% and 99.64%, respectively, which are superior to the comparison models and have strong generalization and robustness. The visualization results of t-distributed random neighbor embedding (t-SNE) further verified its good feature separability and classification ability. Full article

This paper proposes a rolling bearing fault diagnosis method based on HFMD and a dual-branch parallel network, aiming to address the issue of diagnostic accuracy being compromised by the disparity in data quality across different source domains due to sparse feature separation in rolling bearing acoustic signals. Traditional methods face challenges in feature extraction, sensitivity to noise, and difficulties in handling coupled multi-fault conditions in rolling bearing fault diagnosis. To overcome these challenges, this study first employs the HawkFish Optimization Algorithm to optimize Feature Mode Decomposition (HFMD) parameters, thereby improving modal decomposition accuracy. The optimal modal components are selected based on the minimum Residual Energy Index (REI) criterion, with their time-domain graphs and Continuous Wavelet Transform (CWT) time-frequency diagrams extracted as network inputs. Then, a dual-branch parallel network model is constructed, where the multi-scale residual structure (Res2Net) incorporating the Efficient Channel Attention (ECA) mechanism serves as the temporal branch to extract key features and suppress noise interference, while the Swin Transformer integrating multi-stage cross-scale attention (MSCSA) acts as the time-frequency branch to break through local perception bottlenecks and enhance classification performance under limited resources. Finally, the time-domain graphs and time-frequency graphs are, respectively, input into Res2Net and Swin Transformer, and the features from both branches are fused through a fully connected layer to obtain comprehensive fault diagnosis results. The research results demonstrate that the proposed method achieves 100% accuracy in open-source datasets. In the experimental data, the diagnostic accuracy of this study demonstrates significant advantages over other diagnostic models, achieving an accuracy rate of 98.5%. Under few-shot conditions, this study maintains an accuracy rate no lower than 95%, with only a 2.34% variation in accuracy. HFMD and the dual-branch parallel network exhibit remarkable stability and superiority in the field of rolling bearing fault diagnosis. Full article

To address the challenges of low diagnostic accuracy arising from the non-stationary and nonlinear time-varying characteristics of acoustic signals in rolling bearing fault diagnosis, as well as their susceptibility to noise interference, this paper proposes a fault diagnosis method based on a Weighted Average Algorithm–Feature Mode Decomposition (WAA-FMD) and a Local–Global Adaptive Multi-scale Attention Mechanism (LGAF)–Swin Transformer. First, the WAA is utilized to optimize the key parameters of FMD, thereby enhancing its signal decomposition performance while minimizing noise interference. Next, a bilateral expansion strategy is implemented to extend both the time window and frequency band of the signal, which improves the temporal locality and frequency globality of the time–frequency diagram, significantly enhancing the ability to capture signal features. Ultimately, the introduction of depthwise separable convolution optimizes the receptive field and improves the computational efficiency of shallow networks. When combined with the Swin Transformer, which incorporates LGAF and adaptive feature selection modules, the model further enhances its perceptual capabilities and feature extraction accuracy through dynamic kernel adjustment and deep feature aggregation strategies. The experimental results indicate that the signal denoising performance of WAA-FMD significantly outperforms traditional denoising techniques. In the KAIST dataset (NSK 6205: inner raceway fault and outer raceway fault) and the experimental dataset (FAG 30205: inner raceway fault, outer raceway fault, and rolling element fault), the accuracies of the proposed model reach 100% and 98.62%, respectively, both exceeding that of other deep learning models. In summary, the proposed method demonstrates substantial advantages in noise reduction performance and fault diagnosis accuracy, providing valuable theoretical insights for practical applications. Full article

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 enhancement of weak-fault signal characteristics in rolling bearings under strong background noise interference has always been a challenging problem in rotating machinery fault diagnosis. Research indicates that multivariate statistical indicators such as skewness and kurtosis can characterize the fault features of vibration signals. However, when the fault features in the signal are weak and severely affected by noise, the characterization capability of these indicators diminishes, significantly compromising diagnostic accuracy. To address this issue, this paper proposes a novel multivariate statistical filtering (MSF) method for multi-band filtering, which can effectively screen the target fault information bands in vibration signals during bearing faults. The core idea involves constructing a multivariate matrix of fused-fault multidimensional features by integrating fault and healthy signals, and then utilizing eigenvalue distance metrics to significantly characterize the spectral differences between fault and healthy signals. This enables the selection of frequency bands containing the most informative fault features from the segmented frequency spectrum. To address the inherent in-band residual noise in the MSF-processed signals, this paper further proposes the Hilbert differential Teager energy operator (HDTEO) based on MSF to suppress the filtered in-band noise, thereby enhancing transient fault impulses more effectively. The proposed method has been validated using both public datasets and laboratory datasets. Results demonstrate its effectiveness in accurately identifying fault characteristic frequencies, even under challenging conditions such as incipient bearing faults or severely weak vibration signatures caused by strong background noise. Finally, comparative experiments confirm the superior performance of the proposed approach. Full article

Rolling bearing failures in rotating machinery essential to energy systems (e.g., motors, generators, or turbines) can cause downtime, energy inefficiency, and safety hazards—especially under variable speed conditions common in traction drives. Traditional diagnosis methods struggle with nonstationary signals from speed variations. In response, there is a growing trend toward unsupervised and adaptive signal processing techniques, which offer better generalization in complex operating scenarios. This paper proposes an intelligent fault diagnosis framework combining Adaptive Chirp Mode Decomposition (ACMD)-based order tracking with a novel Shortest Paths Density Peak Search (SP-DPS) clustering algorithm. ACMD is chosen for its proven ability to extract instantaneous speed profiles from nonstationary signals, enabling angular domain resampling and quasi-stationary signal representation. SP-DPS enhances clustering robustness by incorporating global structure awareness into the analysis of statistical features in both the time and frequency domains. The method is validated using both a public bearing dataset and a custom-built metro traction motor test bench, representative of electric traction systems. The results show over 96% diagnostic accuracy under significant speed fluctuations, outperforming several state-of-the-art clustering approaches. This study presents a scalable and accurate unsupervised solution for bearing fault diagnosis, with strong potential to improve reliability, reduce maintenance costs, and prevent energy losses in critical energy conversion machinery. Full article

In rotating machines, any faults in anti-friction bearings occurring during operation can lead to failures that are unacceptable due to considerable downtime losses and maintenance costs. Hence, early fault detection is essential, and different vibration-based methods (VBMs) are explored to recognise incipient fault signatures. Based on rotordynamics, if a bearing defect causes metal-to-metal (MtM) impacts during shaft rotation, the impacts excite high-frequency resonance responses of the bearing assembly. The defect-related frequencies are modulated with the resonance responses and rely on signal demodulation for fault detection. However, the current study highlights that the bearing fault/faults may not be detected if the defect in a bearing is not causing MtM impacts nor exciting the high-frequency resonance of the bearing assembly. In a roller bearing, a localised defect may maintain persistent contact between rolling elements and raceways, thereby preventing the occurrence of impulse vibration responses. Due to contact persistence, such defects may not generate impact and may not be detected by existing VBMs, and the bearing could behave as healthy. This paper investigates such specific cases by exploring the relationship between roller-bearing defect characteristics and their potential to generate impact loads during operation. Using an experimental bearing rig, different roller and inner-race defects are presented while their fault characteristic frequencies remain undetected by the envelope analysis, fast Kurtogram, cyclic spectral coherence, and tensor decomposition methods. This study highlights the significance of both the dimension and location of defects within bearings on their detectability based on the rotordynamics concept. Further, simple roller-beam experiments are carried out to visualise and validate the reliability of the experimental observations made on the roller bearing dynamics. Full article

Rolling bearings are prone to failure, meaning that research on intelligent fault diagnosis is crucial in relation to this key transmission component in rotating machinery. The application of deep learning (DL) has significantly advanced the development of intelligent fault diagnosis. This paper proposes a novel method for rolling bearing fault diagnosis based on the fractional constant Q non-stationary Gabor transform (FCO-NSGT) and VMamba-Conv. Firstly, a rolling bearing fault experimental platform is established and the vibration signals of rolling bearings under various working conditions are collected using an acceleration sensor. Secondly, a kurtosis-to-entropy ratio (KER) method and the rotational kernel function of the fractional Fourier transform (FRFT) are proposed and applied to the original CO-NSGT to overcome the limitations of the original CO-NSGT, such as the unsatisfactory time–frequency representation due to manual parameter setting and the energy dispersion problem of frequency-modulated signals that vary with time. A lightweight fault diagnosis model, VMamba-Conv, is proposed, which is a restructured version of VMamba. It integrates an efficient selective scanning mechanism, a state space model, and a convolutional network based on SimAX into a dual-branch architecture and uses inverted residual blocks to achieve a lightweight design while maintaining strong feature extraction capabilities. Finally, the time–frequency graph is inputted into VMamba-Conv to diagnose rolling bearing faults. This approach reduces the number of parameters, as well as the computational complexity, while ensuring high accuracy and excellent noise resistance. The results show that the proposed method has excellent fault diagnosis capabilities, with an average accuracy of 99.81%. By comparing the Adjusted Rand Index, Normalized Mutual Information, F1 Score, and accuracy, it is concluded that the proposed method outperforms other comparison methods, demonstrating its effectiveness and superiority. Full article

Rolling bearings serve as the most widely utilized general components in drive systems for rotating machinery, and they are susceptible to regular malfunctions. To address the performance degradation encountered by current convolutional neural network-based rolling-bearing-fault diagnosis methods due to significant noise interference and variable working conditions in industrial settings, we propose a rolling-bearing-fault diagnosis method based on dual multi-scale mechanism applicable to noisy-variable operating conditions. The suggested approach begins with the implementation of Variational Mode Decomposition (VMD) on the initial vibration signal. This is succeeded by a denoising process that utilizes the goodness-of-fit test based on the Anderson–Darling (AD) distance for enhanced accuracy. This approach targets the intrinsic mode functions (IMFs), which capture information across multiple scales, to obtain the most precise denoised signal possible. Subsequently, we introduce the Dynamic Weighted Multi-Scale Feature Convolutional Neural Network (DWMFCNN) model, which integrates two structures: multi-scale feature extraction and dynamic weighting of these features. Ultimately, the signal that has been denoised is utilized as input for the DWMFCNN model to recognize different kinds of rolling-bearing faults. Results from the experiments show that the suggested approach shows an improved denoising performance and a greater adaptability to changing working conditions. Full article

To address the challenges of unstable vibration signals, indistinct fault features, and difficulties in feature extraction during rolling bearing operation, this paper presents a novel fault diagnosis method based on the fusion of PSR-CRP and DenseNet. The Phase Space Reconstruction (PSR) method transforms one-dimensional bearing vibration data into a three-dimensional space. Euclidean distances between phase points are calculated and mapped into a Color Recurrence Plot (CRP) to represent the bearings’ operational state. This approach effectively reduces feature extraction ambiguity compared to RP, GAF, and MTF methods. Fault features are extracted and classified using DenseNet’s densely connected topology. Compared with CNN and ViT models, DenseNet improves diagnostic accuracy by reusing limited features across multiple dimensions. The training set accuracy was 99.82% and 99.90%, while the test set accuracy is 97.03% and 95.08% for the CWRU and JNU datasets under five-fold cross-validation; F1 scores were 0.9739 and 0.9537, respectively. This method achieves highly accurate diagnosis under conditions of non-smooth signals and inconspicuous fault characteristics and is applicable to fault diagnosis scenarios for precision components in aerospace, military systems, robotics, and related fields. Full article

To address the challenges of weak fault features and strong non-stationarity in early-stage vibration signals, this study proposes a novel fault diagnosis method combining enhanced variational mode decomposition (VMD) with a structurally improved GoogLeNet. Specifically, an improved wild horse optimizer (IWHO) with tent chaotic mapping is employed to automatically optimize critical VMD parameters, including the number of modes K and the penalty factor α, enabling precise decomposition of non-stationary signals to extract weak fault features. The vibration signal is decomposed, and the top five intrinsic mode functions (IMFs) are selected based on the kurtosis criterion. Time–frequency features are then extracted from these IMFs and input into a modified GoogLeNet classifier. The GoogLeNet structure is improved by replacing standard n × n convolution kernels with cascaded 1 × n and n × 1 kernels, and by substituting the ReLU activation function with a parameterized TReLU function to enhance adaptability and convergence. Experimental results on two public rolling bearing datasets demonstrate that the proposed method effectively handles non-stationary signals, achieving 99.17% accuracy across four fault types and maintaining over 95.80% accuracy under noisy conditions. Full article

Rolling bearings are widely used in rotating machinery, and their health status is crucial for the safe operation of the equipment. The research on relevant fault diagnosis algorithms is a hot topic in the field. As a leading deep learning paradigm, Convolutional Neural Networks (CNNs) have demonstrated remarkable effectiveness in bearing fault diagnosis. However, conventional CNNs encounter significant limitations in accurately identifying and classifying early-stage bearing faults, primarily due to two challenges: (1) the diagnostic accuracy is highly susceptible to variations in the input signal length and segmentation strategies and (2) incipient faults are characterized by extremely low signal-to-noise ratios (SNRs), which obscure fault signatures. To address these challenges, we propose a Waveform Intersection-CNN (WI-CNN)-based intelligent diagnosis method for early faults. This approach integrates Gramian Angular Field theory to construct high-resolution fault signatures, enabling the CNN-based diagnosis of incipient bearing faults. Validation using the Case Western Reserve University dataset demonstrates an average diagnostic accuracy exceeding 98%. Furthermore, we established a custom test platform to develop a hybrid diagnosis strategy for 10 distinct fault types. Comparative studies against two conventional CNN diagnostic methods confirm that our approach delivers superior diagnostic precision, a faster iteration speed, and enhanced algorithmic robustness. The empirical findings demonstrate that the model achieves an accuracy of 99.67% during training and 98.167% in the testing phase. Crucially, the proposed method offers exceptional simplicity, computational efficiency, and practical applicability, facilitating its widespread implementation. Full article

As a core component of machining tools and vehicles, the load-bearing and transmission performance of rolling bearings is directly related to product processing quality and driving safety, highlighting the critical importance of fault detection. To address the nonlinearity, non-stationary modulation, and low signal-to-noise ratio (SNR) observed in bearing vibration signals, we propose a fault feature extraction method based on spectral kurtosis and Hilbert envelope demodulation. First, spectral kurtosis is employed to determine the center frequency and bandwidth of the signal adaptively, and a bandpass filter is constructed to enhance the characteristic frequency components. Subsequently, the envelope spectrum is extracted through the Hilbert transform, allowing for the precise identification of fault characteristic frequencies. In the fault diagnosis stage, a multidimensional feature vector is formed by combining the kurtosis index with the amplitude ratios of inner/outer race characteristic frequencies, and fault pattern classification is accomplished using a Least-Squares Support Vector Machine (LS-SVM). To evaluate the effectiveness of the proposed method, experiments were conducted on the bearing datasets from Case Western Reserve University (CWRU) and the Machine Failure Prevention Technology (MFPT) Society. The experimental results demonstrate that the proposed method surpasses other comparative approaches, achieving identification accuracies of 95% and 100% for the CWRU and MFPT datasets, respectively. Full article

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

In modern industrial systems, establishing the optimal maintenance policy for rotating machinery is essential to improve productivity and prevent catastrophic accidents. To this end, many machinery engineers have been interested in condition-based maintenance strategies, which execute the maintenance activity only when the fault symptoms are detected. For more accurate fault detection of rotating machinery, vibration signals have been widely used. However, the vibration signals collected from most real rotating machinery are noisy and nonstationary, and signals from fault states also rarely exist. To address these issues, we newly propose a fault detection framework with a spectrogram and convolutional autoencoder. Firstly, the raw vibration signals are transformed into spectrograms to represent both time- and frequency-related information. Then, a two-dimensional convolutional autoencoder is trained using only normal signals. The encoder part of the convolutional autoencoder is used as a feature extractor of the vibration signals in that it summarizes information on the input spectrogram into the smaller latent feature vector. Finally, we construct the fault detection model by applying the one-class classification algorithm to the latent feature vectors of training signals. We conducted an experimental study using vibration signals collected from a rolling element bearing experimental platform. The results confirm the superiority of the proposed fault detection framework on rotating machinery. In the experimental study, the proposed fault detection framework yielded AUROC values of almost one, and this implies that the proposed framework can be sufficiently applied to real-world fault signal detection problems. Full article