AGV Status Monitoring and Fault Diagnosis based on CNN. Issue 1 (1st June 2022)

• Record Type: Journal Article
• Authors: Wang, Baiyang; Huo, Dongyue; Kang, Yuyun; Sun, Jie
• Is Part Of: Journal of physics. Volume 2281:Issue 1(2022)
• Publication Date: 2022-06-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2281/1/012019
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: In order to solve the problem of AGV fault detection system's complexion and low accuracy, a convolutional neural network (CNN) based on the status monitoring and fault diagnosis method for automatic guided vehicle (AGV) is proposed. Firstly, the vibration signals of the core components of AGV are converted into two-dimensional (2D) images. Secondly, 2D images are input into convolution neural network for training. Finally, the trained model is used to monitor the running status of AGV and identify faults. The results show that the proposed method can effectively monitor the status of AGV in operation.