消极者

W. Elliot, M. Schneider
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引用次数: 1

摘要

FAULT FINDER专家系统对任何可由最低可替换单元(以下称为lru)建模的目标系统或设备实现故障隔离决策。术语“目标系统”将用于指被故障隔离的系统。Fault Finder专家系统故障隔离目标系统的lru。该专家系统使用数据库来表示每个LRU,使用状态接口来获取LRU的状态,并使用知识库来存储目标系统的故障隔离规则。专家系统在数据库、知识库和推理过程中具有多种“学习”能力。影响知识库结构的学习的另一个方面是,每个规则都有与之相关的参数,以存储作为用户反馈和推理过程的结果而学习到的信息。随着系统的运行和经验的积累,与每条规则结论相关的确定性或可能性也会随之调整。推理过程采用模糊逻辑实现前提匹配确定性,采用前提确定性组合实现规则触发确定性。该专家系统首次将一个具有独特知识表示、推理处理、模糊逻辑和多种学习能力的故障隔离系统整合在一起。讨论了故障隔离知识的结构和可能的知识类型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fault finder
The FAULT FINDER Expert System implements fault isolation decisions for any target system or equipment that can be modeled by lowest replaceable units (hereafter called LRUs). The term “Target System” will be used to refer to the system being fault isolated. The Fault Finder expert system fault isolates the target system's LRUs. This expert system utilizes a data base to represent each LRU, a status interface to obtain LRU status, and a knowledge base to store the rules of fault isolation for the target system. The expert system has multiple “learning” capabilities in the data base, the knowledge base and the inference procedure. Another aspect of learning which influences the structure of the knowledge base is that each rule has parameters associated with it to store the information learned as a result of user feedback and the inference process. The certainty or possibility associated with the conclusion of each rule is adjusted as the system runs and gains experience. The inference procedure uses fuzzy logic for premise matching certainty, and combining of premise certainties for the rule firing certainty. This expert system brings together for the first time a fault isolation system with unique knowledge representation, inference processing, fuzzy logic, and multiple learning capabilities in one design. Also presented are issues of knowledge structure, and possible types of fault isolation knowledge.
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