Adaptive variational mode extraction method for bearing fault diagnosis based on window fusion

Liu, Chuliang; Tan, Jianping; Huang, Zhonghe

doi:10.1016/j.measurement.2022.111856

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…Using fuzzy entropy as the fitness function of vibration signal, the optimal parameters ρ and k of VMD are determined by the improved WOA proposed in this paper. Optimal parameter combination [1958,10]. In order to verify the rationality of the optimal parameter combination determined by the proposed algorithm, Figure 5 and Figure 6 show the time domain waveform diagram and spectrum diagram of the bearing vibration signal after using the optimal parameter VMD.…”

Section: Results Analysismentioning

confidence: 99%

“…a time-frequency spectral amplitude modulation (TFSAM) method is proposed. The proposed method obtains the amplitude in the timefrequency domain using short-time Fourier transform and modifies them with different weights [8].Parameter optimization VMD is introduced to realize the composite fault diagnosis of bearings with fault components located in different frequency bands, and it is not combined with other methods [9].To address these limitations, we propose an adaptive variational pattern extraction (AVME) method.Extract the specific mode that contains the most abundant fault information in the signal [10].Therefore, to address the parameter selection issue in the aforementioned methods and the presence of mode mixing in EMD components' envelope spectra, this paper proposes a rolling bearing fault diagnosis method based on an improved Whale Optimization Algorithm (WOA).…”

Section: Introductionmentioning

confidence: 99%

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An early bearing fault analysis based on improved whale algorithm to optimize variational mode decomposition

Gao,

Chen,

Liu

2023

Third International Conference on Control and Intelligent Robotics (ICCIR 2023)

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Section: Results Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An early bearing fault analysis based on improved whale algorithm to optimize variational mode decomposition

Gao,

Chen,

Liu

2023

Third International Conference on Control and Intelligent Robotics (ICCIR 2023)

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“…At the top level of the feature extraction network, attention mechanism is used to adaptively match different weights for temporal features, achieving high-precision quality prediction. 21 Obtain the output φ i of the temporal feature…”

Section: Siae Modelmentioning

confidence: 99%

Adaptive deep fusion neural network based soft sensor for industrial process

Guo,

Chong,

2024

Journal of Chemometrics

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Deep neural networks have become an important tool for soft sensor modeling. However, common deep autoencoder networks are limited to mining the effective information of each input layer during hierarchical training, ignoring the loss of effective information in the original input data and accumulating it layer‐by‐layer, resulting in incomplete feature representation of the original input. At the same time, there is a lack of mining for temporal correlation between process samples and an adaptive mechanism to strengthen temporal related features, resulting in insufficient process information mining. In addition, deep neural networks generally have overfitting problems. To this end, an adaptive deep fusion neural network (ADFNN) method is proposed. This method reconstructs the original input data at each layer of the feature extraction network. By using the reconstructed original input error in pre‐training loss, it reduces the loss of effective information from the original input. Simultaneously, incorporating sliding windows and self‐attention mechanisms to select and calculate the contribution of historical samples to the current sample, integrating temporal related information, and overcoming dependence on high‐dimensional local features by minimizing Kullback‐Leibier (KL) divergence penalty terms. Finally, the temporal features are adaptively weighted and connected to a fully connected network to achieve quality prediction. Simulation experiments were conducted in cases of debutanizer and industrial polyethylene production to verify the effectiveness of the proposed method. The experimental results show that compared to the stacked autoencoder (SAE), target dependent stacked autoencoder (TSAE), and stacked isomorphic autoencoder (SIAE) models, the proposed method ADFNN has improved prediction accuracy by 2.4%, 1.7%, and 0.5% in the case of a debutanizer, respectively. In the industrial polyethylene production case, it has increased by 3.6%, 3.3%, and 1.8%, respectively.

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“…Yan et al [27] employed the whale optimization algorithm to refine the parameters of VME, integrating this improved algorithm with the k-nearest neighbor algorithm (KNN). Liu et al [28] proposed a window fusion strategy that adaptively determines the center frequency ω and penalty factor α. Despite this innovation, their method still necessitates manual intervention for fault identification, highlighting a gap in the development of fully automated, intelligent diagnostic systems.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Based on Parameter-Optimized Variational Mode Extraction and an Improved One-Dimensional Convolutional Neural Network

Zhang,

Tao

2024

Applied Sciences

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When faults occur in rolling bearings, vibration signals exhibit sensitivity to periodic impact components, susceptibility to complex background noise, and non-stationary and nonlinear characteristics. Consequently, using traditional signal processing methods to effectively identify bearing faults presents significant challenges. To facilitate the accurate fault diagnosis of bearings in noisy conditions, we propose an intelligent fault diagnosis method using the Archimedes optimization algorithm (AOA), coupled with a one-dimensional multi-scale residual convolutional neural network (1D-MRCNN), to optimize the variational mode extraction (VME) parameters. First, we introduce a weighted correlated kurtosis (WCK) indicator, formulated using the correlation coefficient and correlated kurtosis as the objective function, to optimize the VME’s center frequency ω and penalty factor α, enabling targeted signal extraction. Second, deep learning techniques are employed to construct the 1D-MRCNN. The neural network then processes the extracted signal for feature extraction and automated fault-type identification. Our simulation results show that the WCK objective function effectively isolates impact components under fault conditions, and our experimental validation confirms that the proposed method accurately identifies diverse fault types across multiple noise levels.

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Adaptive variational mode extraction method for bearing fault diagnosis based on window fusion

Cited by 6 publications

References 29 publications

An early bearing fault analysis based on improved whale algorithm to optimize variational mode decomposition

An early bearing fault analysis based on improved whale algorithm to optimize variational mode decomposition

Adaptive deep fusion neural network based soft sensor for industrial process

Bearing Fault Diagnosis Based on Parameter-Optimized Variational Mode Extraction and an Improved One-Dimensional Convolutional Neural Network

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