Research on bearing health state prediction based on multidimensional information average convolutional neural network

Jiawen Yu; Hao Huang; Xiali Liu; Yaohua Deng

doi:10.1117/12.2611269

14 October 2021 Research on bearing health state prediction based on multidimensional information average convolutional neural network

Jiawen Yu, Hao Huang, Xiali Liu, Yaohua Deng

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Proceedings Volume 11930, International Conference on Mechanical Engineering, Measurement Control, and Instrumentation; 1193037 (2021) https://doi.org/10.1117/12.2611269
Event: International Conference on Mechanical Engineering, Measurement Control, and Instrumentation (MEMCI 2021), 2021, Guangzhou, China

Abstract

Injection molding machine can shape all kinds of precise size, complex shape of plastic products, related to all aspects of people's life, such as the national defense field, civil fields such as electronic industry, daily use industry, medical industry and medical industry. Therefore, it is of great significance to establish a health prediction model for injection molding machine and carry out condition monitoring. Bearing is one of the most important parts in the rotating parts of the injection molding machine. Analysis of the health state of the bearing can reflect the performance state of the injection molding machine to a certain extent. Taking bearings as the research object, this paper introduces deep learning and information average convolutional neural network (AICNN), and proposes a health state prediction method of rotating equipment based on multi-feature variable fusion.

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Jiawen Yu, Hao Huang, Xiali Liu, and Yaohua Deng "Research on bearing health state prediction based on multidimensional information average convolutional neural network", Proc. SPIE 11930, International Conference on Mechanical Engineering, Measurement Control, and Instrumentation, 1193037 (14 October 2021); https://doi.org/10.1117/12.2611269

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KEYWORDS

Convolution

Data modeling

Convolutional neural networks

Feature extraction

Model-based design

Defense technologies

Electrical engineering

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