An Image Recognition Method for Industrial Bearing Faults Based on Network Parameter Optimization

Bearing is the key part of mechanical equipment, and its state of health directly affects the property and safety of equipment traditional bearing health monitoring methods are usually based on vibration analysis and vibration signal processing, but there are limitations in complex working conditions and early fault warning. The rise of deep learning technology offers new possibilities for bearing health monitoring. To solve the above problems, this paper proposes a deep learning method based on hyperparameter optimization for bearing health monitoring. Firstly, the bask principle of deep learning and its application in bearing health monitoring are introduced. Then, the adopted deep learning models (such as convolutional neural networks, recurrent neural networks, etc) and their training and optimization process on bearing vibration data are described in detail. Through the experimental verification, the proposed method shows good accuracy and stability in bearing fault diagnosis and health monitoring, and provides strong support for arming health monitoring under complex working conditions.