1.School of Power Engineering, Naval University of Engineering, Wuhan 430033, China;
2.First Military Delegate Office of Shanghai under Naval Equipment Department, Shanghai 201913,China
Abstract:Aiming at the non-stationary and nonlinear characteristics of rolling element bearings’ fault signals which are easily interfered by background noise, a fault diagnosis method was proposed in this paper based on convolutional neural network (CNN) due to the advantages of deep learning methods.First, the one dimension (1D) bearing vibration signals collected by multi-sensors under different faults were converted to two dimension (2D) gray images as the input of CNN which are divided into training set and testing set.Then, the CNN was trained by the training set and the representative features can be extracted automatically.Finally, the effectiveness of the trained CNN was verified by the testing set to identify the fault types of bearings.The proposed method does not rely on human experience and signal processing techniques for the pre-extraction of fault features.The analysis results using experimental signals show that the proposed method has higher and more stable prediction accuracy compared with the traditional support vector machine and probabilistic neural network method.
朱丹宸1,张永祥1,潘洋洋2,朱群伟1. 基于多传感器信号和卷积神经网络的滚动轴承故障诊断[J]. 振动与冲击, 2020, 39(4): 172-178.
ZHU Danchen1,ZHANG Yongxiang1,PAN Yangyang2,ZHU Qunwei1. Fault diagnosis for rolling element bearings based on multi-sensor signals and CNN. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(4): 172-178.
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