Artificial intelligent fault diagnosis system of complex electronic equipment

Issue title: Special Section: Iteration, Dynamics and Nonlinearity

Guest editors: Manuel Fernández-Martínez and Juan L.G. Guirao

Article type: Research Article

Authors: Wang, Xiangmin^{a; *} | Wang, Jun^a | Privault, M.^b

Affiliations: [a] School of Automation, Nanjing University of Science and Technology, Nanjing, China | [b] National Science Foundation, Computer and Information Science and Engineering Directorate, Arlington, VA, USA

Correspondence: [*] Corresponding author. Xiangmin Wang, School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China. E-mail: [email protected].

Abstract: The traditional fault detection system of complex electronic equipment based on image analysis theory only analyzes the image characteristics of complex electronic equipment for artificial intelligent fault diagnosis. It cannot deal with the system diagnosis problem of qualitative fault data and has the problems of low accuracy and long time consuming of fault detection. To address these problems, an artificial intelligent fault diagnosis system of complex electronic equipment based on BP neural network is designed in this paper. BP neural network model for artificial intelligent fault diagnosis of complex electronic equipment is built based on system overall structure. The structure of BP neural network and learning algorithm is determined according to the actual fault problem. Learning and training of BP neural network are carried out by using sample data of fault. Artificial intelligent fault diagnosis algorithm of complex electronic equipment based on BP neural network and qualitative fault data is used, which combines the BP neural network and qualitative fault data. The preprocessing method is applied to quantify the fault data. Fault diagnosis is achieved by BP neural network technology. The system database and the implementation process of the BP neural network are designed. Experimental results show that the designed system can significantly improve the accuracy of fault detection of complex electronic equipment, improve the effect of fault detection, and reduce the time consuming of fault detection.

Keywords: Complex electronics, equipment fault, artificial intelligence, diagnostic system, BP neural network, residual signal

DOI: 10.3233/JIFS-169735

Journal: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 4, pp. 4141-4151, 2018