Hybrid-knowledge-models-based intelligent fault diagnosis strategies for liquid-propellant rocket engines

This paper focuses on a qualitative fault diagnosis method based on the integration and fusion of shallow and deep knowledge for liquid-propellant rocket engines (LRE). The paper firstly clarifies the concept and the types of LRE diagnosis. knowledge. Later, from the isomorphic transform point of view, the paper analyses the correlation of different knowledge and knowledge representation, and formulate the LRE fault diagnosis. Then, the ways of acquisition, representation and organization for knowledge-based hybrid models constructed by signed directed graphs, rules, prepositional logic models, and qualitative deviation models a-re given. The intelligent diagnosis strategies for L RE, which reason and make a decision by multiple and synthetically utilizing all kinds of diagnosis knowledge such as experience, causality, system structure, and models, are presented.