Fuzzy RBF neural network fault diagnosis method based on knowledge and data fusion for the recoil system

Article type: Research Article

Authors: Yang, Xiangfei | Zhang, Faping^{; *} | Wei, Jianfeng

Affiliations: Laboratory of Industrial and Intelligent System Engineering, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China

Correspondence: [*] Corresponding author. Faping Zhang. Tel.: +010 68912708; Email: zhangfaping@bit.edu.cn.

Abstract: To improve the accuracy of fault diagnosis for recoil systems under multiple operating conditions, a fuzzy RBF neural network (Radial Basis Function, RBF) fault diagnosis method based on knowledge and data fusion is proposed. A kinetic model for the recoil system is first established to describe the system’s behavior. Next, fuzzy RBF neural network is used to establish the relationship between abnormal operating parameters and fault causes, achieving a fault cause diagnosis accurately based on the integration of expert experience knowledge and system operation data. A study case demonstrate that the algorithm has strong knowledge and data fusion capabilities and can effectively identify faults in recoil system.

Keywords: Fuzzy method, RBF neural network, knowledge and data fusion, the recoil system, fault diagnosis

DOI: 10.3233/JIFS-230683

Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4981-4994, 2024