An optimized variational mode extraction method for rolling bearing fault diagnosis

Pang, Bin; Nazari, Mojtaba; Sun, Zhenduo; Tang, Guiji

doi:10.1177/14759217211006637

Cited by 31 publications

(26 citation statements)

References 30 publications

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“…At this time, the corresponding spectrum distribution and amplitude will also change. Bearing fault diagnosis often uses statistical parameters in the time domain and frequency domain to characterize fault characteristics [26][27][28][29]. e statistical parameters selected in the paper are shown in Table 1.…”

Section: Det Methodmentioning

confidence: 99%

Fault Identification of Low-Speed Hub Bearing of Crane Based on MBMD and BP Neural Network

Guo

Yang

Cheng

et al. 2022

Shock and Vibration

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As the key bearing part of the crane, the low-speed hub bearing of the crane exists in special working conditions of low-speed and alternating heavy load. It is difficult to extract its fault characteristics accurately by existing analysis methods. The main idea of the broadband mode decomposition (BMD) method previously proposed is to search in the association dictionary library containing broadband and narrowband signals. However, when it is applied to the broadband signals interfered by strong noise, the decomposition is easy to produce modal confusion, so the modulated broadband mode decomposition (MBMD) method is proposed. The fault signal just can be analyzed by MBMD, so it is applied to the fault diagnosis of low-speed hub bearing of the crane. To realize the fault identification of low-speed hub bearing of the crane, firstly, the original signal is decomposed by MBMD. Secondly, the eigenvalues of the first three-component signals are calculated, the eigenvalue matrix is constructed, and the marked features are selected by the distance evaluation technique (DET). Finally, the marked features are input into BP neural network for training and testing to identify the types of bearing fault. Compared with EEMD, VMD, and BMD, the MBMD method combined with BP neural network has good performance in feature extraction and fault identification.

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Section: Det Methodmentioning

confidence: 99%

Fault Identification of Low-Speed Hub Bearing of Crane Based on MBMD and BP Neural Network

Guo

Yang

Cheng

et al. 2022

Shock and Vibration

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“…When the center frequency and penalty factor are selected reasonably, the performance of VME is better than VMD. 22,23 Hence, a reasonable choice of these two parameters is essential to apply VME to obtain the fault characteristic components. When the preset center frequency ω deviates from the frequency of the fault feature component in the spectrum, the separation accuracy of VME may be decreased.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…OVME is able to extract fault feature components with the optimal parameters, but it has low computational efficiency because of the redundant particle swarm optimization process. 22 Subsequently, Pang et al developed the recursive VME RVME method inspired by the recursive decomposition framework of EMD. The center frequencies of the sub-components of the bearing fault signal can be adaptively determined and the RVME method performs fast running speed.…”

Section: Introductionmentioning

confidence: 99%

Spectral variational mode extraction and its application in fault detection of rolling bearing

Pang

Zhang

Cheng

et al. 2022

Structural Health Monitoring

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The core of fault diagnosis of rolling bearing is to extract the narrowband sub-components containing fault feature information from the bearing fault signal. Variational mode extraction (VME), a novel single sub-component separation algorithm originated from variational mode decomposition (VMD), provides a promising solution to bearing fault detection. However, its performance is closely related to the hyperparameter selection, including the center frequency ω d and the penalty factor α. This paper proposes a non-recursive and adaptive signal decomposition algorithm termed spectral variational mode extraction (SVME). SVME can be seen as a spectral decomposition technique whose framework is composed of the adaptive spectral boundary division and boundary constrained VME. In the adaptive spectral boundary division, an adaptive iterative spectral envelope method referring to the continuous envelope correlation (CCE) index is developed to integrate with the parameterless scale-space division to adaptively locate the frequency band boundary. The presented adaptive spectral boundary division approach can effectively inhibit the spectral boundary over-division. In the boundary constrained VME, the dominant frequency of each frequency band determined by the optimal spectral division is distinguished as the preset center frequency. Meanwhile, the optimal penalty factor is determined based on the envelope spectral kurtosis (ESK) index and the boundary-constraint principle. The SVME method is utilized in the simulation and experimental case studies to investigate its capability. Furthermore, its superiority is highlighted through the comparison with the variational mode decomposition (VMD) and Autogram methods.

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“… Population initialization: The optimal parameters should be bounded, and the correlation between quality factors should be as low as possible. The value range of

takes as [ 8 , 15 ], the value range of

takes as [ 1 , 3 ], and the value range of redundancy factors

and

take as [ 2 , 5 ]. Then, to reduce the calculation, the accuracy of the four parameters is reserved to one single decimal.…”

Section: The Parallel Parameter Optimized Rssd Base On Woamentioning

confidence: 99%

“…Over the past two decades, many scholars have explored the rotating machinery fault diagnosis field and introduced many diagnostic theories and methods. For example, empirical mode decomposition (EMD) [ 3 , 4 ], wavelet transform (WT) [ 5 , 6 ], variational mode decomposition fault (VMD) [ 7 , 8 ], and so on. Although these methods and their combination perform well on single faults, there are some limitations: for example, the existence of modal mixing in EMD [ 9 , 10 ], the diagnosis effectiveness of wavelet transform, which depends on the constant quality factor, and the choice of wavelet basis [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Compound Fault Diagnosis of a Wind Turbine Gearbox Based on MOMEDA and Parallel Parameter Optimized Resonant Sparse Decomposition

Feng

Zhang

Hong

et al. 2022

Sensors

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Wind turbines usually operate in harsh environments. The gearbox, the key component of the transmission chain in wind turbines, can easily be affected by multiple factors during the operation process and develop compound faults. Different types of faults can occur, coupled with each other and staggered interference. Thus, a challenge is to extract the fault characteristics from the composite fault signal to improve the reliability and the accuracy of compound fault diagnosis. To address the above problems, we propose a compound fault diagnosis method for wind turbine gearboxes based on multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) and parallel parameter optimized resonant sparse decomposition (RSSD). Firstly, the MOMEDA is applied to the preprocess, setting the deconvolution period with different fault frequency types to eliminate the interference of the transmission path and environmental noise, while decoupling and separating the different types of single faults. Then, the RSSD method with parallel parameter optimization is applied for decomposing the preprocessed signal to obtain the low resonance components, further suppressing the interference components and enhancing the periodic fault characteristics. Finally, envelope demodulation of the enhanced signal is applied to extract the fault features and identify the different fault types. The effectiveness of the proposed method was verified using the actual data from the wind turbine gearbox. In addition, a comparison with some existing methods demonstrates the superiority of this method for decoupling composite fault characteristics.

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An optimized variational mode extraction method for rolling bearing fault diagnosis

Cited by 31 publications

References 30 publications

Fault Identification of Low-Speed Hub Bearing of Crane Based on MBMD and BP Neural Network

Fault Identification of Low-Speed Hub Bearing of Crane Based on MBMD and BP Neural Network

Spectral variational mode extraction and its application in fault detection of rolling bearing

Compound Fault Diagnosis of a Wind Turbine Gearbox Based on MOMEDA and Parallel Parameter Optimized Resonant Sparse Decomposition

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