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How is gnn better in terms of feature extraction in machine fault diagnosis?

Computer science

Convolutional neural network

Sparse approximation

Feature selection

Regularization (linguistics)

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In machine fault diagnosis, Graph Neural Networks (GNN) excel in feature extraction by leveraging innovative techniques. They enhance fault classification accuracy by transforming signals into time–frequency maps and images, capturing both obvious and subtle features for robust predictions . Additionally, GNNs benefit from advanced methods like Data Probability Density-Gram Angle Field-Convolutional Neural Network (DPD-GAF-CNN), which simplify feature extraction processes and improve fault diagnosis accuracy significantly . Moreover, GNNs offer superior feature selection capabilities through methods like Feature Ranking based on Optimal Class Distance Ratio (FROCDR), enabling the identification of optimal features between different classes for enhanced classification accuracy and stability . Overall, GNNs stand out in machine fault diagnosis by efficiently extracting and selecting features critical for accurate fault classification.

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Journal Article•DOI A Novel Fault Feature Selection and Diagnosis Method for Rotating Machinery With Symmetrized Dot Pattern Representation 15 Jan 2023-IEEE sensors journal	The paper does not discuss Graph Neural Networks (GNNs) for feature extraction in machine fault diagnosis.
Open access•Journal Article•DOI Intelligent Machinery Fault Diagnosis Method Based on Adaptive Deep Convolutional Neural Network: Using Dental Milling Cutter Malfunction Classifications as an Example Ming-Huang Chen, Shang-Liang Chen, Yu-Sheng Lin, Yu-Jen Chen - Show less +3 more 30 Jun 2023-Applied Sciences	The deep convolutional neural network (DCNN) excels in feature extraction for machine fault diagnosis by fusing multi-dimensional signals and enhancing both obvious and subtle features.
Open access•Journal Article•DOI A New Method Based on Encoding Data Probability Density and Convolutional Neural Network for Rotating Machinery Fault Diagnosis 01 Jan 2023-IEEE Access	The proposed DPD-GAF-CNN method enhances feature extraction by encoding data probability density into 2D images, simplifying the process and achieving high-precision fault classification in rotating machinery diagnosis.
Open access•Journal Article•DOI Sparse representation learning for fault feature extraction and diagnosis of rotating machinery Sai Ma, Qinkai Han, Fulei Chu - Show less +2 more 01 Jun 2023-Expert systems with applications	The proposed joint sparse representation learning method with nonlocal GMC penalty and generalized FTV regularization enhances feature extraction for machine fault diagnosis, outperforming existing methods by at least 2 dB.

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