Research on fault diagnosis method based on knowledge-data fuzzy fusion
TL;DR
A fault diagnosis method based on knowledge-data fuzzy fusion (KDFF) is proposed, which can control the diagnostic error within 3%, effectively improving the fault diagnosis accuracy, and laying the foundation for predictive maintenance of such devices.
Abstract
The recoil suppression device is a key component of modern artillery systems. Its dynamic performance directly affects the stability and control accuracy of the machinery operation, and subsequently influences work efficiency and operational safety. However, traditional fault diagnosis methods are unable to handle the ambiguity issues arising from multi-source faults, resulting in low accuracy of the diagnosis results. Therefore, this paper proposes a fault diagnosis method based on knowledge-data fuzzy fusion (KDFF). Firstly, the expert experience knowledge(EEK) is introduced into the operating data of the recoil suppression device under different operating conditions, and the fault features are extracted through knowledge and data fusion-driven processing. Then, these knowledge are refined into membership function(MF) and confidence function(CF), and the fuzzy theory(FT) is used to improve the neural network. Finally, comparative experiments under three fault modes are conducted to verify the effectiveness of the algorithm. The verification results show that the KDFF can control the diagnostic error within 3%, effectively improving the fault diagnosis accuracy, and laying the foundation for predictive maintenance of such devices.