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Hybrid-Knowledge-Models-Based Intelligent Fault Diagnosis Strategies for Liquid-Propellant Rocket Engines

机译：基于混合知识模型的液体推进式火箭发动机的智能故障诊断策略

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摘要

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 are given. The intelligent diagnosis strategies for LRE, 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.
机译：本文重点介绍了一种基于浅层和深层知识对液相推进火箭发动机（LRE）的集成故障诊断方法。本文首先阐明了LRE诊断知识的概念和类型。后来，从同义变换的观点来看，本文分析了不同知识和知识表示的相关性，并制定了LRE故障诊断。然后，给出了通过签名的针对图，规则，介词逻辑模型和定性偏差模型构建的知识的混合模型的获取，表示和组织的方式。 LRE的智能诊断策略，其原因和作出多次和合成使用各种诊断知识（如经验，因果关系，系统结构和模型）。

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