A Gradient-Based Optimizer Method for Improving the Feature Extraction Capability of Network Architecture and Its Application to Condition Monitoring of Rotating Machinery

Fengshuo Cao

doi:10.54097/rn9ty436

Authors

Fengshuo Cao

DOI:

https://doi.org/10.54097/rn9ty436

Keywords:

Gradient-Based Optimizer, Condition monitoring, Rotating machinery, CNN.

Abstract

Aiming at the problem of inaccurate feature extraction under the massive data of rotating machinery, this paper proposes a feature extraction capability enhancement method based on Gradient-Based Optimizer for trusted network architecture to monitor the conditions of bearings, which firstly transforms the original signal into a wavelet time-frequency image and visualizes the high-dimensional and difficult-to-visualize data in low dimensions. Then, different convolutional neural network architectures are tested by implanting different noise intensities to determine the most robust network architecture. Secondly, this paper adopts the grid method as well as the swarm optimization algorithm to guide the parameters of the model to achieve the best feature extraction effect, compares the optimization effect of several swarm optimization algorithms horizontally, and determines the most effective GBO algorithm among them. Finally, to improve the credibility of the best network model, this paper combines the grad-cam method with the trusted fusion method to improve the accuracy and reliability of the model. Validated by the bearing data of Case Western Reserve University, it shows that the proposed model in this paper has good credibility and accuracy.

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References

Wang H, Zhang W, Han C, et al. A bearing fault diagnosis method with an improved residual Unet diffusion model under extreme data imbalance [J]. Measurement Science and Technology, 2024, 35 (4).

Qu H, Zhang W, Zhang G. A novel fault diagnosis scheme for a ballast system [J]. Automatica, 2023.

Zhao W, Lv Y, Liu J, et al. Early fault diagnosis based on reinforcement learning optimized-SVM model with vibration-monitored signals [J]. Quality Engineering, 2023 (4): 35.

Gholaminejad A, Jorkesh S, Poshtan J. A comparative case study between shallow and deep neural networks in induction motor's fault diagnosis [J]. IET Science, Measurement & Technology, 2023.

Agahi H M A. Decision fusion scheme for bearing defects diagnosis in induction motors [J]. Electrical engineering, 2020, 102 (4).

Liu X, Aldrich C. Recognition of flotation froth conditions with k-shot learning and convolutional neural networks [J]. Journal of Process Control, 2023: 128.

Shen J, Song D, Ma T, Xu F. Blade crack detection based on domain adaptation and autoencoder of multidimensional vibro-acoustic feature fusion. Structural Health Monitoring. 2023 Sep; 22 (5): 3498-513.

Shen J, Ma T, Song D, Xu F. Quantitative blade damage detection based on multisource domain and multistage joint transfer. Structural Health Monitoring. 2024 Feb 12: 14759217231226381.

Mirjalili S, Lewis A. The Whale Optimization Algorithm [J]. Advances in Engineering Software, 2016, 95: 51-67.

Van N L, Jeyaprakash N, Yang C H. Nondestructive characterization of elastic modulus under different tensile stresses through laser ultrasound combined inversion technique [J]. International Journal of Modern Physics B, 2024, 38 (04).

Jia Y H, Mei Y, Zhang M. Confidence-Based Ant Colony Optimization for Capacitated Electric Vehicle Routing Problem with Comparison of Different Encoding Schemes [J]. IEEE Transactions on Evolutionary Computation, 2022, 26: 1394-1408.

Abdel-Basset M, El-Shahat D, Sallam K M, et al. Parameter extraction of photovoltaic models using a memory-based improved gorilla troops optimizer [J]. Energy conversion & management, 2022 (Jan.): 252.

GAI J B, SHEN J X, HU Y F, et al. An integrated method based on hybrid grey wolf optimizer improved variational mode decomposition and deep neural network for fault diagnosis of rolling bearing [J]. Measurement, 2020, 162 (10): 1-12.

Mohamed A, Reda M, M. K S, et al. Light Spectrum Optimizer: A Novel Physics-Inspired Metaheuristic Optimization Algorithm [J]. Mathematics, 2022, 10 (19): 3466-3466.

Ahmadianfar I, Bozorg-Haddad O, Chu X. Gradient-Based Optimizer: A New Metaheuristic Optimization Algorithm [J]. Information Sciences, 2020, 540. DOI: 10.1016/j.ins.2020.06.037.

Selvaraju R R, Cogswell M, Das A, et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization [J]. International Journal of Computer Vision, 2020, 128 (2): 336-359.