Fault diagnosis of spraying workshop based on BP neural network. Issue 3 (June 2021)

• Record Type: Journal Article
• Authors: Zhou, Kai; Zhang, Jingtao; Qi, Xing; Xu, Wenlong; Long, Xiaojun
• Is Part Of: Journal of physics. Volume 1952:Issue 3(2021)
• Publication Date: 2021-06
• Subjects: BP (Back Propagation) neural network -- Fault diagnosis -- Spraying workshop; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1952/3/032074
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: In this paper, for the failure of spraying workshop door switch, exhaust fan failure and other faults in the spraying workshop, a fault diagnosis algorithm based on the BP (Back Propagation) neural network is proposed to solve the fault diagnosis problem in the spraying workshop. First, the sensor network of the spraying workshop was built. Multi-source sensors were used to realize the dynamic perception of the on-site environment and equipment status. Through the preprocessing of multi-source heterogeneous data, a unified data template and interface were established. Secondly, the BP neural network algorithm is used to simulate and diagnose the possible faults in the spraying process. Finally, MATLAB test simulation proves its high accuracy of fault diagnosis.