An ameliorated African vulture optimization algorithm to diagnose the rolling bearing defects

Vashishtha, Govind; Chauhan, Sumika; Kumar, Anil; Kumar, Rajesh

doi:10.1088/1361-6501/ac656a

Cited by 39 publications

(8 citation statements)

References 50 publications

(75 reference statements)

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“…All details of the contrastive methods are illustrated as follows: FFT [ 34 ]: The basic idea of FFT (Fast Fourier Transform) is to decompose the original sequence of N points into a series of short sequences. EMD [ 35 ]: EMD (Empirical Mode Decomposition) is a signal processing method in the time-frequency domain, which is based on the time-scale characteristics of the data itself, without setting any basis function in advance. EMD has obvious advantages in processing non-stationary and nonlinear data and is suitable for analyzing nonlinear non-stationary signal sequences with a high signal-to-noise ratio.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…EMD [ 35 ]: EMD (Empirical Mode Decomposition) is a signal processing method in the time-frequency domain, which is based on the time-scale characteristics of the data itself, without setting any basis function in advance. EMD has obvious advantages in processing non-stationary and nonlinear data and is suitable for analyzing nonlinear non-stationary signal sequences with a high signal-to-noise ratio.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

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A Novel Deep Transfer Learning Method for Intelligent Fault Diagnosis Based on Variational Mode Decomposition and Efficient Channel Attention

Liu

Zheng

Bao

et al. 2022

Entropy

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In recent years, deep learning has been applied to intelligent fault diagnosis and has achieved great success. However, the fault diagnosis method of deep learning assumes that the training dataset and the test dataset are obtained under the same operating conditions. This condition can hardly be met in real application scenarios. Additionally, signal preprocessing technology also has an important influence on intelligent fault diagnosis. How to effectively relate signal preprocessing to a transfer diagnostic model is a challenge. To solve the above problems, we propose a novel deep transfer learning method for intelligent fault diagnosis based on Variational Mode Decomposition (VMD) and Efficient Channel Attention (ECA). In the proposed method, the VMD adaptively matches the optimal center frequency and finite bandwidth of each mode to achieve effective separation of signals. To fuse the mode features more effectively after VMD decomposition, ECA is used to learn channel attention. The experimental results show that the proposed signal preprocessing and feature fusion module can increase the accuracy and generality of the transfer diagnostic model. Moreover, we comprehensively analyze and compare our method with state-of-the-art methods at different noise levels, and the results show that our proposed method has better robustness and generalization performance.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Experimental Results and Analysismentioning

confidence: 99%

A Novel Deep Transfer Learning Method for Intelligent Fault Diagnosis Based on Variational Mode Decomposition and Efficient Channel Attention

Liu

Zheng

Bao

et al. 2022

Entropy

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“…Meng et al [24] used the EMD algorithm is used to extract the characteristics of the vibration signal of the rolling bearing. Vashishtha et al [25] proposed a deep learning-based scheme using time-varying filter based EMD for bearing fault identification. Wang et al [26] proposed the FI-EEMD to suppress mode mixing and fundamental attenuation by injecting noise and analytical signal.…”

Section: Introductionmentioning

confidence: 99%

Bearing fault diagnosis under variable speed conditions on adaptive time frequency extraction mode decomposition

Huo,

Yang,

Yao

et al. 2024

Meas. Sci. Technol.

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Fault diagnosis of rolling bearings is crucial for their operation in variable working conditions in mechanical systems. Time-frequency distribution (TFD) methods are widely used in variable-speed rolling bearing fault diagnosis due to their ability to capture the time-frequency characteristics of signals. However, they suffer from weak robustness, poor resolution, and lack of feature information concentration. To address these issues, we construct a method: adaptive time frequency extraction mode decomposition (ATFEMD) by utilizing the characteristic of instantaneous frequency (IF) energy concentration in TFD. The method first enhances the adaptive time-frequency distribution (ADTFD) via an iterative algorithm, applying random sample consensus (RANSAC) algorithm enables ridges extraction from the TFD. The harmonics of the extracted ridges are then verified by passing the proxy data method, followed by a noise test to determine if the harmonics are true. From the above steps, ATFEMD decomposing the signal into physically meaningful adaptive mode (AM). This article analyzes the simulated bearing vibration and test bench bearing vibration signals to demonstrate the performance of ATFEMD. Results indicated that the proposed method is characterized by strong robustness, and good feature extraction results compared to other methods.

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“…Traditional intelligent diagnosis methods mainly include two processes: feature extraction and pattern classification. Feature extraction usually uses a variety of signal processing methods such as mode decomposition (Vashishtha et al, 2022a(Vashishtha et al, , 2022b(Vashishtha et al, , and 2022cVashishtha and Kumar, 2021), spectral analysis (Vashishtha and Kumar, 2022), wavelet transform and statistical features (Meng et al, 2019;Zadkarami et al, 2017) to extract time-domain, frequency-domain and time-frequency domain features from original data. The extracted features are then input into support vector machine (SVM), particle swarm optimization (PSO), and artificial neural network (ANN), and Bayesian network algorithms are used to classify the faults.…”

Section: Introductionmentioning

confidence: 99%

Acoustic detection and localization of gas pipeline leak based on residual connection and one-dimensional separable convolutional neural network

Yan

Liu

et al. 2023

Transactions of the Institute of Measurement and Control

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The leakage of natural gas pipelines may cause significant safety accidents and cause severe economic losses to society. Therefore, timely detection and location of pipeline leak is an effective measure to reduce losses. Traditional methods need to extract the leak features from signals and then input them into convolutional neural networks to realize leakage diagnosis, which is a tedious process. This paper proposes a pipeline leak detection method based on residual connections and one-dimensional separable convolutional neural network (1D-RC-SCNN) and optimizes the structure and parameters of the model. The model uses one-dimensional separable convolution to replace the traditional convolutional neural network, which effectively reduces the number of model parameters. The improved residual connection can prevent the training data from overfitting and speed up the model convergence. The leakage signal after segmentation and preprocessing is directly sent to the 1D-RC-SCNN model, the leakage features are adaptively extracted, and then, the results are output through the Softmax classifier. The method directly performs end-to-end processing on the leaked signal, which improves the data processing speed. Finally, the cross-correlation method is used to locate the pipeline leak. The effectiveness of the proposed method is verified by building an experimental platform and compared with related models. The experimental results show that the method has high accuracy for the detection and localization of pipeline leak.

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An ameliorated African vulture optimization algorithm to diagnose the rolling bearing defects

Cited by 39 publications

References 50 publications

A Novel Deep Transfer Learning Method for Intelligent Fault Diagnosis Based on Variational Mode Decomposition and Efficient Channel Attention

A Novel Deep Transfer Learning Method for Intelligent Fault Diagnosis Based on Variational Mode Decomposition and Efficient Channel Attention

Bearing fault diagnosis under variable speed conditions on adaptive time frequency extraction mode decomposition

Acoustic detection and localization of gas pipeline leak based on residual connection and one-dimensional separable convolutional neural network

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