Rolling Bearing Fault Diagnosis Based on Weighted Variational Mode Decomposition and Cyclic Spectrum Slice Energy

Li, Dongkai; Liu, Xiaoang; You, Yancheng; Zhen, Dong; Hu, Wei; Lu, Kuihua; Gu, Fengshou

doi:10.1007/978-3-030-99075-6_52

Cited by 6 publications

(10 citation statements)

References 11 publications

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“…Time-frequency analysis methods that combine time and frequency domains are usually short-time Fourier analysis, wavelet analysis [25,26], empirical mode decomposition combined with order statistics filter (OSF), and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) [27][28][29], variational modal decomposition combined with cyclic spectrum slice energy (CSSE) or comprehensive impact coefficient (CIC) based fitness function of the sparrow search algorithm (the fitness function of the sparrow search algorithm) [30,31] and other methods. Therefore, the advantage of the time-frequency analysis method in bearing fault diagnosis is reflected in its ability to provide information in both the time and frequency domains, enabling a more comprehensive understanding of the signal changes, which is particularly important for capturing and analyzing non-stationary signals (e.g., transient vibrations caused by bearing faults).…”

Section: Introductionmentioning

confidence: 99%

ADAMS Simulation and HHT Feature Extraction Method for Bearing Faults of Coal Shearer

Qin,

Fu,

et al. 2024

Processes

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Aiming at the problem of difficult fault diagnosis work caused by the difficulty of data acquisition of the bearing in the traction part of a coal mining machine, a method of ADAMS simulation and HHT feature extraction of the bearing fault of a coal mining machine is proposed. First of all, take the traction section bearing as the research object, use the virtual prototype in the establishment of the healthy state of coal mining machine traction section model based on the establishment of the bearing inner ring fault, rolling body fault, outer ring fault of the coal mining machine traction section dynamics model, and then after the EMD decomposition, each IMF component of the Hilbert transform, to obtain the signal in the time-frequency plane of the time-frequency joint characteristics, to get the HHT marginal spectra and to different Under different working conditions, the bearing vibration signal features are mined by quantitative feature extraction. Finally, a variety of mainstream machine learning algorithms are introduced to classify the features, and the results show that the feature extraction method in this paper is universal and provides valuable theoretical support and technical guidance for the field application of coal mining machine-bearing fault diagnosis.

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Section: Introductionmentioning

confidence: 99%

ADAMS Simulation and HHT Feature Extraction Method for Bearing Faults of Coal Shearer

Qin,

Fu,

et al. 2024

Processes

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“…Additionally, some researchers have employed modal analysis to extract vibration signal features from rolling bearings. For example, Liu et al extracted features from bearing fault signals by using a combination of Variational Mode Decomposition (VMD) and Singular Value Decomposition (SVD), which yielded favorable signal decomposition results [6]. Hu et al utilized Ensemble Empirical Mode Decomposition (EEMD) to extract features from rolling bearing fault signals, exhibiting excellent performance in their proposed fault diagnosis method [7].…”

Section: Introductionmentioning

confidence: 99%

An approach to bearing fault diagnosis based on ensemble learning and case-based reasoning

Li,

Guo,

Dou

et al. 2024

J. Phys.: Conf. Ser.

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In response to the challenge of multiple fault types and complex diagnostic criteria in bearing fault diagnosis, a case reasoning method based on ensemble learning is proposed. The approach utilizes Case-Based Reasoning (CBR) to construct a case library for vibration-based features of rolling bearings and perform fault diagnosis. Moreover, addressing the issue of determining optimal feature weight ratios when retrieving similar cases in traditional case reasoning methods, a Random Forest algorithm combined with Bayesian Optimization is introduced. This integration allows for adaptive retrieval of similar cases, thereby enhancing the diagnostic capability for bearing faults. The effectiveness of this approach is validated through experimental analysis.

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“…Amin et al 22 present a machine-learning framework for early damage detection in gearboxes based on the cyclostationary and Kurtogram analysis of sensor data. Li et al 23 proposed a fault diagnosis method based on weighted variational mode decomposition (VMD) and cyclic spectrum slice energy (CSSE). The signal is decomposed into intrinsic mode functions (IMFs) through VMD, and the amount of information contained in each IMF is measured using sparsity.…”

Section: Introductionmentioning

confidence: 99%

“…When the partial derivative is 0, the sum of squares of the errors is taken as the minimum, and the polynomial coefficients can be expressed by Equation (23).…”

Section: Introductionmentioning

confidence: 99%

TSCK guided parameter convex optimization tunable Q-factor wavelet transform and its application in wheelset bearing fault diagnosis

Zhang

et al. 2023

Structural Health Monitoring

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Wheelset bearing is a typical vulnerable structural component in high-speed trains and heavy haul vehicles. In addition to the typical nonlinear and nonstationary characteristics, the vibration signal of wheelset bearing also contains track subgrade vibration and transmission path coupling interference components. To solve this problem, this paper proposes a new feature extraction method for wheelset bearing faults. This method constructs the Teager energy spectrum correlation kurtosis, which is purposely sensitive to periodic fault impulse components, as the objective function. The Q-factor and redundancy of tunable Q-factor wavelet transform are selected by using the parameter convex optimization method, which makes the signal decomposition have better sparsity, so as to extract fault information accurately. Simulated analysis, experimental signal analysis of QPZZ-II test-bed, and experimental signal analysis of wheelset bearing test-bed show that the proposed method can suppress the influence of nonperiodic transient impulse components, harmonic components, and noise components in the signal and accurately extract the periodic impact characteristics of bearings.

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Rolling Bearing Fault Diagnosis Based on Weighted Variational Mode Decomposition and Cyclic Spectrum Slice Energy

Cited by 6 publications

References 11 publications

ADAMS Simulation and HHT Feature Extraction Method for Bearing Faults of Coal Shearer

ADAMS Simulation and HHT Feature Extraction Method for Bearing Faults of Coal Shearer

An approach to bearing fault diagnosis based on ensemble learning and case-based reasoning

TSCK guided parameter convex optimization tunable Q-factor wavelet transform and its application in wheelset bearing fault diagnosis

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