Deep expert network: A unified method toward knowledge-informed fault diagnosis via fully interpretable neuro-symbolic AI

Abstract

In recent years, intelligent fault diagnosis (IFD) based on Artificial Intelligence (AI) has gained significant attention and achieved remarkable breakthroughs. However, the black-box property of AI-enabled IFD may render it non-interpretable, which is essential for safety-critical industrial assets. In this paper, we propose a fully interpretable IFD approach that incorporates expert knowledge using neuro-symbolic AI. The proposed approach, named Deep Expert Network, defines neuro-symbolic node, including signal processing operators, statistical operators, and logical operators to establish a clear semantic space for the network. All operators are connected with trainable weights that decide the connections. End-to-end and gradient-based learning are utilized to optimize both the model structure weights and parameters to fit the fault signal and obtain a fully interpretable decision route. The transparency of model, generalization ability toward unseen working conditions, and robustness to noise attack are demonstrated through case study of rotating machinery, paving the way for future industrial applications.