A Novel Fault Diagnosis of a Rolling Bearing Method Based on Variational Mode Decomposition and an Artificial Neural Network

Liang, Xiaobei; Yao, Jinyong; Zhang, Weifang; Wang, Yanrong

doi:10.3390/app13063413

Cited by 11 publications

(6 citation statements)

References 33 publications

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“…𝛾 𝛽 (𝑛 𝑎 (𝑥), 𝑛 𝑏 (𝑥)) = (𝑝 1 − 𝑝 The meta ACON-C is proposed based on ACON-C [24]. In terms of 1D-signal, a novel switching factor 𝛽 is designed in Eq.…”

Section: Meta-acon-c-based Activation Functionmentioning

confidence: 99%

An intelligent bearing fault diagnosis framework: one-dimensional improved self-attention-enhanced CNN and empirical wavelet transform

Dong,

Zhao,

Cui

2024

Nonlinear Dyn

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：The complexity of the internal structure of rolling bearings and the harshness of their operating environment result in strong non-stationarity and nonlinear of the vibration signals. It remains a challenging and attractive task to accomplish more accurate classification through signal processing techniques and pattern recognition methods. To realize this aim, a novel one-dimensional improved self-attentionenhanced convolutional neural network (1D-ISACNN) with empirical wavelet transform (EWT) is proposed for rolling bearing fault classification. Firstly, the EWT algorithm is employed to decompose the raw signal into three frequency components, allowing for further extraction of multi-frequency components to enhance signal characteristics. Subsequently, a creative1D-ISACNN, which leverages the merits of new developed attention mechanism and an optimized meta-activation concatenation function in feature learning to better capture and map crucial information within the signal. Furthermore, label smoothing regularization is designed as the loss function of the 1D-ISACNN, which takes into account not only the loss of correctly labeled positions in the training samples but also the loss of other mislabeled positions. Finally, the adaptive moment projection estimation is designed to ensure a more robust gradient update strategy for updating the parameters of the proposed model. The developed model tested on three different sets of bearing data, has achieved a classification accuracy of 100%. The proposed method demonstrates higher recognition accuracy and more robust generalization capabilities compared to other state-of-the-art approaches to ablative experiments.

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“…𝛾 𝛽 (𝑛 𝑎 (𝑥), 𝑛 𝑏 (𝑥)) = (𝑝 1 − 𝑝 The meta ACON-C is proposed based on ACON-C [24]. In terms of 1D-signal, a novel switching factor 𝛽 is designed in Eq.…”

Section: Meta-acon-c-based Activation Functionmentioning

confidence: 99%

An intelligent bearing fault diagnosis framework: one-dimensional improved self-attention-enhanced CNN and empirical wavelet transform

Dong,

Zhao,

Cui

2024

Nonlinear Dyn

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“…www.nature.com/scientificreports/ In Fig. 3, firstly, power system fault data is input, and the input data is denoised by Variational Mode Decomposition (VMD) 25 . Then, the parameters of DBN 26 and PSO algorithm 27 are initialized.…”

Section: Application Of Dbn In Power System Fault Prediction and Anal...mentioning

confidence: 99%

ESG guidance and artificial intelligence support for power systems analytics in the energy industry

Li,

Zou,

Zeng

et al. 2024

Sci Rep

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In order to increase the precision and effectiveness of power system analysis and fault diagnosis, this study aims to assess the power systems in the energy sector while utilizing artificial intelligence (AI) and environmental social governance (ESG). First, the ESG framework is presented in this study to fully account for the effects of the power system on the environment, society, and governance. Second, to coordinate the operation of various components and guarantee the balance and security of the power system, the CNN-BiLSTM power load demand forecasting model is built by merging convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM). Lastly, the particle swarm optimization (PSO) algorithm is used to introduce and optimize the deep belief network (DBN), and a power grid fault diagnostic model is implemented using the PSO technique and DBN. The model’s performance is assessed through experimentation. The outcomes demonstrate how the CNN-BiLSTM algorithm significantly increases forecasting accuracy while overcoming the drawback of just having one dimension of power load data. The values of 0.054, 0.076, and 0.102, respectively, are the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Effective processing of large-scale nonlinear data is achieved in the area of power grid fault diagnosis, resulting in prediction accuracy of 96.22% and prediction time of only 129.94 s. This is clearly better than other algorithms and increases fault prediction efficiency and accuracy. Consequently, the model presented in this study not only produces impressive results in fault diagnosis and load demand forecasting, but also advances the field of power system analysis in the energy industry and offers a significant amount of support for the sustainable and intelligent growth of the energy industry.

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“…Common shallow artificial intelligence algorithms include artificial neural networks (ANN), support vector machines (SVM), relevance vector machine (RVM) and cluster analysis, etc. Liang et al [5] devised the bearing fault diagnosis model based on variational mode decomposition (VMD) and ANN, optimizing fault feature extraction and neural network structural parameters to ensure high fault diagnosis accuracy with less computation time. But the structural parameters of ANN were difficult to determine.…”

Section: Introductionmentioning

confidence: 99%

A review on convolutional neural network in rolling bearing fault diagnosis

Li,

Ma,

Yuan

et al. 2024

Meas. Sci. Technol.

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The health condition of rolling bearings has a direct impact on the safe operation of high-speed trains. And their working environment is harsh and the working condition is complex, which brings challenges to fault diagnosis. With the development of computer technology, deep learning has been applied in the field of fault diagnosis and has rapidly developed. Among them, convolutional neural network (CNN) has received great attention from researchers due to its powerful data mining ability and feature adaptive learning ability. Based on recent research hotspots, the development history and trend of CNN is summarized and analyzed. Firstly, the basic structure of CNN is introduced and the important progress of classical CNN models for rolling bearing fault diagnosis in recent years is studied. The problems with the classic CNN algorithm have been pointed out. Secondly, to solve the above problems, combined with recent research achievements, various methods and principles for optimizing CNN are introduced and compared from the perspectives of deep feature extraction, hyperparameter optimization, network structure optimization. Although significant progress has been made in the research of fault diagnosis of rolling bearings based on CNN, there is still room for improvement and development in addressing issues such as low accuracy of imbalanced data, weak model generalization, and poor network interpretability. Therefore, the future development trend of CNN networks is discussed finally. And transfer learning models are introduced to improve the generalization ability of CNN and interpretable CNN is used to increase the interpretability of CNN networks.

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A Novel Fault Diagnosis of a Rolling Bearing Method Based on Variational Mode Decomposition and an Artificial Neural Network

Cited by 11 publications

References 33 publications

An intelligent bearing fault diagnosis framework: one-dimensional improved self-attention-enhanced CNN and empirical wavelet transform

An intelligent bearing fault diagnosis framework: one-dimensional improved self-attention-enhanced CNN and empirical wavelet transform

ESG guidance and artificial intelligence support for power systems analytics in the energy industry

A review on convolutional neural network in rolling bearing fault diagnosis

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