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 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. Introduction Significant progresses have been made towards the development of formal intelligent fault diagnosis methods for dynamic systems since 1980s. Such methods offer the prospect of improving system reliability and providing the basis for health monitoring in a large range of applications. In particular, modeling (qualitative or quantitative) methods and reasoning strategies form the heart of automated diagnosis systems. However, a number of important technical problems remain to be solved to make intelligent fault diagnosis techniques viable for real industrial applications [1]. For a dynamic system, if its diagnostic knowledge is complete, then all of its faults can be strictly diagnosed. But, because of the complexity of practical systems, it is often impossible to construct a complete diagnostic knowledge base for a problem. So, at present the limitations of intelligent fault diagnosis can fall into one main class: the incompleteness of diagnostic knowledge. In qualitative reasoning such as qualitative simulation [2,3], this means the generation of numerous spurious behaviors except the true. There are three approaches to solve the above problem as following: the first one is the development of more qualitative representations that allow more detailed information on the system, e.g., qualitative curvature [4] and fuzzy membership functions [5,6], or additional knowledge from other sources, e.g., observations [7], to be incorporated into qualitative reasoning, thereby reducing the qualitative ambiguity. The second is the integration of qualitative reasoning and quantitative simulation, in which qualitative reasoning is used to produce primitive solutions and quantitative simulation is then used as a filtering technique to eliminate ambiguous Key Engineering Materials Vols. 245-246 (2003) pp. 149-157 online at http://www.scientific.net © (2003) Trans Tech Publications, Switzerland Online available since 2003/07/15 All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of the publisher: Trans Tech Publications Ltd, Switzerland, www.ttp.net. (ID: 130.203.154.169-01/02/08,05:37:14) behaviors [8,9]. The third is the development of model-based diagnostic reasoning [10,11,12], which uses the system’s models and observations to reason and produce the set of components that may be abnormal. However, in these methods two problems, which are important to construct a complete knowledge base for an intelligent fault diagnosis system, are obviously neglected:(1) In fault diagnosis, what kind of knowledge is needed and how deep the knowledge should be; (2) How to acquire the knowledge required and how to represent, manage and coordinate between the acquired knowledge in diagnosis. This paper, based on the earlier work reported by the authors [13], presents an intelligent fault diagnosis method that utilizes a hybrid knowledge model and reasoning strategy. It includes two main aspects: one is the diagnostic knowledge strategy that comprises the acquisition, representation, and organization of different knowledge and information such as shallow and deep knowledge, qualitative and quantitative knowledge, fuzzy knowledge and time information. Another is the intelligent diagnostic reasoning strategy based on the integration of different knowledge with different depth. Firstly, besides the correlation of different knowledge, the concept and the types of diagnostic knowledge are systematically described. Secondly, the graphic and modeling ways of different knowledge acquisition, are discussed and studied. Thirdly, the objectoriented way of knowledge representation for causality, structure, and behavior is presented, and an integrated knowledge model based on the component structure and in the form of node knowledge base is given. Finally, the intelligent fault diagnostic strategy, which utilizes synthetically different knowledge and multiple reasoning methods, such as rule-based, model-based, fuzzy-knowledge -based, and dynamic-knowledge-based, are discussed and developed. The results verified with simulated fault samples and test-firing data of the LRE show that the method studied can be available for reference in constructing automated fault diagnostic systems. The problem statement Diagnosis knowledge strategy focuses on the acquisition, representation and organization of diagnosis knowledge. It is one of the aims in the diagnosis strategy to construct a complete knowledge base. However, it is also difficult in the way of shallow or deep knowledge. Meantime, in almost all diagnosis methods for dynamic systems, a similar knowledge description is usually utilized in the idea of the following mapping principle. Definition1 Define the fault diagnosis problem of a physical system as an original problem P0. Definition2 Let U be the domain, Xi=(X1 ,...,Xn ) be the vector of fault parameters in the i-th (i=1,...,m) component of the system (denote as Componenti), and n=dimension(Xi) be the dimension of the vector Xi, then define a vector space with n×1dimension Spacei={(X1 ,...,Xn )|Xj i ∈U, j=1,...,n} (1) as the fault space of Componenti, and define the product Space= Space1×Space2 ×...×Spacem (2) as the fault space of the system. Definition3 Define as an abstract expression of the fault diagnosis problem, where K is the constructed knowledge base, R is the set of reasoning methods which utilize K to diagnose. If K and R is given in a fault diagnosis problem P: , then P is called a given diagnosis problem. Definition4 Let the original problem P0=and a given problem P1= are two descriptions for fault diagnosis problem, if there exists a full mapping from K0 to K1: h1: K0→K1 (3) if and only if there also exists a full mapping from R0 to R1: Damage Assessment of Structures V 150