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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火箭发动机智能故障诊断策略

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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：本文重点研究了基于浅层知识和深层知识的融合和融合的液体推进火箭发动机（LRE）的定性故障诊断方法。本文首先阐明了LRE诊断知识的概念和类型。随后，从同构变换的角度，分析了不同知识与知识表示的相关性，并提出了LRE故障诊断方法。然后，给出了由有向有向图，规则，介词逻辑模型和定性偏差模型构成的基于知识的混合模型的获取，表示和组织方式。提出了针对LRE的智能诊断策略，该策略可通过综合运用各种诊断知识（例如经验，因果关系，系统结构和模型）来推理和做出决策。

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