Search Results (30)

Wind turbine bearings operate long-term under complex and variable operating conditions, where fault impulse characteristics are easily submerged by strong noise. Traditional graph signal processing-based bearing fault diagnosis methods are limited by fixed graph topology, empirical feature selection and poor noise robustness. This paper proposes an adaptive frequency graph spectrum (AFGS) model for bearing fault diagnosis. The model constructs graph signals in the frequency domain and determines the core analysis interval adaptively via eigenvalue sequences, which eliminates fixed topology constraints. Combined with a fast bisection search framework and a correlation spectral negative entropy (CSNE) index sensitive to periodic fault impulses, the proposed method realizes fully automatic optimal band selection without manual intervention and improves noise resistance. The AFGS method first transforms vibration signals via fast Fourier transform and constructs frequency-domain graph features based on Laplacian matrix decomposition. The optimal fault characteristic band is adaptively determined using the bisection framework and CSNE criterion. Finally, signal reconstruction and envelope spectrum analysis are implemented for fault identification. Simulation results under −3 dB low signal-to-noise ratio show that AFGS can effectively extract the 1st to 8th fault harmonics. Further validation on the measured inner and outer race fault signals of 6205 bearings demonstrates that the proposed method can clearly identify fault characteristic frequencies and their multi-order harmonics. Comparative tests with Fast Kurtogram and Autogram indicate that the two benchmark algorithms only extract limited low-order harmonics under simulated noise and completely fail in practical strong noise environments. Experimental results verify that AFGS outperforms conventional methods in band localization accuracy, noise suppression, and fault feature extraction completeness, providing a reliable solution for the bearing fault diagnosis of rotating machinery under complex working conditions. Full article

Rolling bearings, as a key component of rotating machinery, require precise fault diagnosis to ensure the safe and reliable operation of industrial systems. Nevertheless, the performance of traditional narrowband filtering demodulation (NFD) methods is constrained by inherent limitations of spectral segmentation frameworks and insufficient discriminative capability of feature indicators (FIs). To address these limitations, this paper proposes a new NFD method based on an adaptive multi-level spectra segmentation strategy. Firstly, using power spectral density (PSD) as the analysis basis, an iterative framework is constructed to obtain multi-level spectral trend lines (STLs), which achieves multi-perspective characterization of spectral features. Secondly, the local minimum points of the STLs are used as the segmentation boundaries to extract the demodulation frequency band. Subsequently, a robust blind feature indicator, synergistic characterization criterion (SCC), is proposed, which can simultaneously fully evaluate periodicity and impulsiveness, guiding the selection of the optimal demodulation frequency band (ODFB). Finally, based on the enhanced demodulation spectrum, power exponent transformation is introduced to construct a generalized spectral family, and the adaptive determination of the optimal transformation parameter is guided by frequency-domain signal-to-noise ratio (FDSNR), thereby obtaining the generalized enhanced demodulation spectrum (GEDS). Validation experiments on laboratory and public datasets demonstrate that the proposed method outperforms Fast Kurtogram, Autogram, and CFFsgram, with average improvements of 63.86% and 89.06% in mean-peak ratio (MPR) and fault feature coefficient (FFC), respectively, and provides a new perspective for NFD and expands its application potential in bearing fault diagnosis and condition monitoring. Full article

To address the difficulty of extracting weak fault features of rolling bearings in wind turbines under strong background noise, a fault feature extraction method based on the collaborative filtering correlation spectrum, named CESIgram, is proposed. The collaborative filtering correlation spectrum (CFCS) based on Block Matching 3D is designed to suppress random noise while preserving cyclostationary structures, resulting in a clearer cyclic spectral representation. A projection method along the cyclic frequency axis is proposed to obtain the carrier-based enhanced envelope spectrum. An integrated envelope spectrum index combining harmonic significance and periodic impact is proposed to quantify fault feature enrichment in different enhanced envelope spectra. The method works in three stages: spectral representation via Fast-SC, reformulation of the spectral correlation via CFCS, and adaptive band selection via CESI. The method successfully extracted fault characteristic frequencies and their harmonics in simulation and experimental signals under various strong noise conditions, while Fast Kurtogram, Autogram, Infogram, and Fast Entrogram failed to detect any fault-related peaks. Comparative analysis shows that the proposed method has significant advantages in noise suppression and fault feature extraction. The effectiveness is verified using simulation and experimental signals of rolling bearing faults in wind power equipment. Full article

Early bearing faults are often difficult to identify because their characteristic components are weak and easily masked by strong interference. To improve weak-fault feature extraction, this paper proposes a particle-swarm-optimization-based FitzHugh–Nagumo stochastic resonance (FHN-SR) method for bearing vibration signals. The raw signal is first preprocessed by de-meaning, Hilbert envelope demodulation, and standardization to construct a stable stochastic resonance (SR) input. Then, the key model parameters are adaptively optimized by maximizing the output signal-to-noise ratio around the target fault characteristic frequency. To evaluate the proposed method comprehensively, comparisons are carried out with classical SR, underdamped bistable stochastic resonance (UBSR), and a Fast-Kurtogram-based envelope-analysis scheme. Experimental validation is performed on three fault cases, including the rolling element fault case from the Case Western Reserve University (CWRU) dataset and the inner-race and outer-race fault cases from the Machinery Comprehensive Diagnostics Simulator (MCDS) platform. The results show that FHN-SR produces a clearer concentration of fault-related energy and achieves a higher output signal-to-noise ratio (SNR) than the compared methods in most cases. In particular, under degraded noise conditions, FHN-SR maintains more stable enhancement performance, indicating stronger robustness to interference. These results demonstrate that the proposed method provides an effective approach for extracting weak bearing fault features under complex noise backgrounds. Full article

To address the challenge of effectively extracting early-stage failure features in rolling bearings, this paper proposes a two-stage harmonic optimization-gram method based on spectral amplitude modulation (SAM-TSHOgram). The method first employs amplitude spectra with varying weighting exponents to preprocess the signal, performing nonlinear adjustments to the vibration signal’s spectrum to enhance weak periodic impact characteristics. Subsequently, a two-stage evaluation strategy based on spectral coherence (SCoh) was designed to adaptively identify the optimal frequency band (OFB). The first stage employs the Periodic Harmonic Correlation Strength (PHCS) metric, based on autocorrelation, to coarsely screen candidate bands with strong periodic structures. The second stage utilizes the Sparse Harmonic Significance (SHS) metric, based on spectral negative entropy, to refine the candidate set, selecting bands with the most prominent harmonic features. Finally, SCoh is integrated over the selected OFB to generate an Improved Envelope Spectrum (IES). The proposed method was validated using both simulated and experimental vibration signals from bearings and gearboxes. The results demonstrate that SAM-TSHOgram significantly outperforms conventional approaches such as EES, Fast Kurtogram, and IESFOgram in terms of signal-to-noise ratio (SNR) enhancement, harmonic clarity, and diagnostic robustness. These findings confirm its potential for reliable early fault detection in rolling bearings. 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

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

Synchrosqueezed transform (SST) is a time–frequency analysis method that can improve energy aggregation and reconstruct signals, which has been applied in the fields of medical treatment, fault diagnosis, and seismic wave processing. However, when dealing with time-varying signals, SST suffers from poor time–frequency resolution and is unable to deal with long signals. In order to accurately extract the characteristic frequency of variable speed rolling bearing faults, this paper proposes a synchrosqueezed transform method based on fast kurtogram and demodulation and piecewise aggregate approximation (PAA). The method firstly filters and demodulates the original signal using fast kurtogram and Hilbert transform to reduce the influence of background noise and improve the time–frequency resolution. Then, it compresses the signal by using piecewise aggregate approximation, so that the SST can deal with long signals and, thus, extract the fault characteristic frequency. The experimental data verification results indicate that the method can effectively identify the fault characteristic frequency of variable-speed rolling bearings. Full article

This paper proposes an improved fault diagnosis algorithm that combines a modified fast kurtogram (FK) method with the lightweight convolutional neural network GhostNet. The FK algorithm can adaptively select resonance demodulation bands for envelope demodulation to extract fault features, but it may be disturbed by non-Gaussian noise. Hence, the fast average kurtogram (FAK) method based on sub-band averaging was introduced. This method effectively weakens the impact of pulse noise on the kurtosis graph by splitting the signal into equal-length sub-signals and calculating the average kurtosis value of all sub-signal filters. Simultaneously, to fully utilize the advantages of deep learning technology in feature extraction and classification, this study used the FAK to convert vibration signals from one-dimensional to two-dimensional kurtosis graphs as the input for the GhostNet model. This combination not only achieved accurate fault diagnosis and classification but also showed significant advantages in processing efficiency and resource utilization. The experimental results indicate that the algorithm excelled in extracting features and diagnosing periodic transient impact faults, and compared with traditional methods, it exhibited noticeable improvements in computational efficiency and resource management. Full article

Resonance demodulation of vibration signals is a common method for extracting fault information from rolling bearings. Nonetheless, demodulation quality is dependent on frequency band location. Established methods such as the Fast Kurtogram, Autogram, SKRgram, etc. have achieved satisfactory results in some cases, but the results are not good in the presence of strong white Gaussian noise and random impulses. To solve these issues, an algorithm that selects the optimal demodulation frequency band (ODFB) based on the ratio of the logarithmic envelope spectrum Gini coefficient (LESGIRgram) is proposed. The core idea of this paper is to capture the difference between the LESGIgrams of health and fault signals and accordingly locate the frequency bands that contain the most fault information. Initially, the baseline is constructed by calculating the logarithmic envelope spectrum Gini coefficient matrix of the health bearing (LESGI_baseline). Next, the LESGI matrix of the fault bearing (LESGI_measured) is computed. The ratio of LESGI_measured to LESGI_baseline is calculated, and the ODFB can be selected with the maximum LESGIR. The fault signal is then filtered using this derived ODFB, and envelope analysis is performed to extract fault features. The proposed algorithm for detecting rolling bearing faults has been verified for accuracy and effectiveness through simulation and experimental data. Full article

Rolling element bearings (REBs) are an essential part of rotating machinery. A localised defect in a REB typically results in periodic impulses in vibration signals at bearing characteristic frequencies (BCFs), and these are widely used for bearing fault detection and diagnosis. One of the most powerful methods for BCF detection in noisy signals is envelope analysis. However, the selection of an effective band-pass filtering region presents significant challenges in moving towards automated bearing fault diagnosis due to the variable nature of the resonant frequencies present in bearing systems and rotating machinery. Cepstrum Pre-Whitening (CPW) is a technique that can effectively eliminate discrete frequency components in the signal whilst detecting the impulsive features related to the bearing defect(s). Nevertheless, CPW is ineffective for detecting incipient bearing defects with weak signatures. In this study, a novel hybrid method based on an improved CPW (ICPW) and high-pass filtering (ICPW-HPF) is developed that shows improved detection of BCFs under a wide range of conditions when compared with existing BCF detection methods, such as Fast Kurtogram (FK). Combined with machine learning techniques, this novel hybrid method provides the capability for automated bearing defect detection and diagnosis without the need for manual selection of the resonant frequencies. The results from this novel hybrid method are compared with a number of established BCF detection methods, including Fast Kurtogram (FK), on vibration signals collected from the project I2BS (An EU Clean Sky 2 project ‘Integrated Intelligent Bearing Systems’ collaboration between Schaeffler Technologies and the University of Southampton. Safran Aero Engines was the topic manager for this project) and those from three databases available in the public domain—Case Western Reserve University (CWRU), Intelligent Maintenance Systems (IMS) datasets, and Safran jet engine data—all of which have been widely used in studies of this kind. By calculating the Signal-to-Noise Ratio (SNR) of each case, the new method is shown to be effective for a much lower SNR (with an average of 30.21) compared with that achieved using the FK method (average of 14.4) and thus is much more effective in detecting incipient bearing faults. The results also show that it is effective in detecting a combination of several bearing faults that occur simultaneously under a wide range of bearing configurations and test conditions and without the requirement of further human intervention such as extra screening or manual selection of filters. Full article

Composite fault diagnosis of rolling bearings is very challenging work, especially when the characteristic frequency ranges of different fault types overlap. To solve this problem, an enhanced harmonic vector analysis (EHVA) method was proposed. Firstly, the wavelet threshold (WT) denoising method is used to denoise the collected vibration signals to reduce the influence of noise. Next, harmonic vector analysis (HVA) is used to remove the convolution effect of the signal transmission path, and blind separation of fault signals is carried out. The cepstrum threshold is used in HVA to enhance the harmonic structure of the signal, and a Wiener-like mask will be constructed to make the separated signals more independent in each iteration. Then, the backward projection technique is used to align the frequency scale of the separated signals, and each fault signal can be obtained from composite fault diagnosis signals. Finally, to make the fault characteristics more prominent, a kurtogram was used to find the resonant frequency band of the separated signals by calculating its spectral kurtosis. Semi-physical simulation experiments are conducted using the rolling bearing fault experiment data to verify the effectiveness of the proposed method. The results show that the proposed method, EHVA, can effectively extract the composite faults of rolling bearings. Compared to fast independent component analysis (FICA) and traditional HVA, EHVA improves separation accuracy, enhances fault characteristics, and has higher accuracy and efficiency compared to fast multichannel blind deconvolution (FMBD). Full article

Although stochastic resonance (SR) has been widely used to enhance weak fault signatures in machinery and has obtained remarkable achievements in engineering application, the parameter optimization of the existing SR-based methods requires the quantification indicators dependent on prior knowledge of the defects to be detected; for example, the widely used signal-to-noise ratio easily results in a false SR and decreases the detection performance of SR further. These indicators dependent on prior knowledge would not be suitable for real-world fault diagnosis of machinery where their structure parameters are unknown or are not able to be obtained. Therefore, it is necessary for us to design a type of SR method with parameter estimation, and such a method can estimate these parameters of SR adaptively by virtue of the signals to be processed or detected in place of the prior knowledge of the machinery. In this method, the triggered SR condition in second-order nonlinear systems and the synergic relationship among weak periodic signals, background noise and nonlinear systems can be considered to decide parameter estimation for enhancing unknown weak fault characteristics of machinery. Bearing fault experiments were performed to demonstrate the feasibility of the proposed method. The experimental results indicate that the proposed method is able to enhance weak fault characteristics and diagnose weak compound faults of bearings at an early stage without prior knowledge and any quantification indicators, and it presents the same detection performance as the SR methods based on prior knowledge. Furthermore, the proposed method is more simple and less time-consuming than other SR methods based on prior knowledge where a large number of parameters need to be optimized. Moreover, the proposed method is superior to the fast kurtogram method for early fault detection of bearings. Full article

Optimal demodulation band extraction is a significant step in rolling bearing fault analysis. However, existing methods, primarily based on global indexes and neglecting negative local outliers, cannot identify compound faults in intense noise environments. To address this problem, a novel demodulation band extraction method based on weighted geometric cyclic relative entropy (WGCRE) is proposed. WGCRE is defined on the cyclic sub-bands model of the logarithmic envelope spectrum (LES) to fully consider the bearing characteristic frequency of pseudo-cyclostationarity. In detail, local and global thresholds are separately set by the white noise parameter and harmonic-to-noise ratio to exclude the exogenous noise outliers. On this basis, the WGCRE is defined as a geometrically weighted index of several different fault types to avoid harmonic interference and improve the identification of composite faults. WGCRE–gram, similar to fast kurtogram (FK), is then constructed by replacing kurtosis with WGCRE to extract the optimal demodulation band. Compared with FK and another LES-based method, logarithmic-cycligram, the proposed method is more robust for accurately identifying single and compound faults under external noise. The effectiveness of this method is verified through simulations and actual tests. Simulation experiments of different kinds and intensities of exogenous noise interference preliminarily determine the superior robustness of WGCRE in the face of solid noise. The inner ring, outer ring, and composite fault experiments further confirmed the robust adaptability of WGCRE in the face of complex working conditions. Full article

In industry, early fault signals of rolling bearings are submerged in strong background noise, causing a low signal-to-noise ratio (SNR) and difficult diagnosis. This paper proposes a fault feature extraction method based on an optimized Laplacian wavelet dictionary (LWD) and the feature symbol search (FSS) algorithm to extract early fault characteristic frequencies of bearings under low SNR. As the morphological parameters of the Laplace wavelet dictionary and sparse coefficients are not easy to obtain, this method uses the adaptive empirical wavelet transform (AEWT) to determine the morphological parameters of the Laplace wavelet. Firstly, AEWT is applied to obtain the different frequency components, and the combination index is utilized for optimal component selection. Then, the morphological parameters of LWD are determined by AEWT processing, by which the overcomplete dictionary that best matches the signal can be obtained. Finally, the optimal sparse representation of the component signal in the dictionary is calculated by FSS, which helps to achieve sparse denoising and enhance the impact features. The effectiveness of the method is verified by simulation. The effectiveness and advantages of LWDFSS-AEWT are verified by experiment in comparison with methods such as fast spectral kurtosis (FSK), correlation filtering (CF), shift-invariant sparse coding (SISC), base pursuit denoising (BPDN) and wavelet packet transform Kurtogram (WPT Kurtogram). Full article