基于模型诊断的抽象分层过程

Hierarchical Abstraction Process in Model-Based Diagnosis

分层诊断是降低基于模型诊断计算复杂性的一个重要方法,在分层诊断中很重要的一步是构造系统的抽象分层模型,以往的分层方法都是建立在某一种特定抽象模型基础上,没有将抽象的一般理论与基于模型诊断中的分层过程相联系,并且对自动生成系统的分层表示方法也没有形式化的分析.KRA(Knowledge Reformulationand Abstraction)模型从构造化的角度定义了表示抽象的一般框架,文中在KRA模型框架下建立系统模型,给出了抽象算子在系统的基本框架Rg上进行的非独立的运算过程,使得不会在抽象过程中生成重复的部件类型,不仅降低了存储空间,还增加了在进一步的抽象运算中算子重用的概率,提高了整个抽象运算的效率.同时描述了基于模型诊断中的抽象分层过程,提出了动态和静态构造算子库两种方法,并对其优缺点进行分析,给出了一个应用抽象算子集合自动生成待诊断系统分层表示的一般算法.

Hierarchical diagnosis is an important method for reducing the complexity of model-based diagnosis.It is a significant step for hierarchical diagnosis to construct the hierarchical model of the system to be diagnosed.The previous methods have been based on some particular abstraction model.And they have not related the generic notion of abstraction to hierarchical process in model-based diagnosis and not formally analyzed the methods of automatically building the hierarchy of the system.The KRA（Knowledge Reformulation Abstraction） model provides a general framework from a view of constructive point which has potential to unite the former abstraction methods.The authors constructed the system model within the KRA model framework.This model provides a dependent operating process based on the basic framework Rg of the system.The process avoids producing a repetitious basic type and thus not only reduces space complexity but also makes the abstract operators reused more frequently to improve the performance of the whole abstraction operating.In this paper the authors describe the hierarchical abstraction process in model-based diagnosis and provide a general algorithm to automatically construct the hierarchical representation of the system to be diagnosed.The algorithm runs by building the operators database in two ways： Dynamic and Static whose advantage and disadvantage are also analyzed.In addition,the authors also discuss the validity and complexity of the proposed algorithm.