Diagnosis Based on Explicit Means-End Models
Diagnosis Based on Explicit Means-End Models
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DOI：
10.1016/0004-3702(94)00043-3
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发表时间：
1996
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影响因子：
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通讯作者：
J. Larsson
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文献类型：
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作者：
J. Larsson
本文介绍了用于工业过程的三种诊断方法。它们是测量验证，即使用任何仪器冗余来检查传感器和测量值的一致性；告警分析，即对多种告警情况进行分析，找出哪些告警与主要故障直接相关，哪些告警是主要故障的间接影响；故障诊断，即查找故障原因并采取补救措施。这三种方法都使用多级流模型（MFM）来描述目标过程。它们已在实时专家系统工具 G2、C 和 Common Lisp 中实现，并在多个流程的模拟中成功进行了测试。所使用的知识表示本体基于用于描述物理系统的流、质量、能量和信息的概念。系统的结构和功能之间的关系通过目的论关系来描述，该目的论关系将流程结构连接成在模型构建时构建的图形。这使得诊断推理可以作为静态图结构中的搜索来实现，因此可以非常快速地执行。与其他基于模型的方法一样，通用算法用于具有生成能力的表示。该表示通过非常抽象的物理层面的功能而获得力量，比大多数定性物理模型更抽象。它适用于可以使用流程描述的系统，但目前缺乏捕获其他类型系统（例如电子电路）的重要方面的能力。
This article describes three diagnostic methods for use with industrial processes. They are measurement validation, i.e., consistency checking of sensor and measurement values using any redundancy of instrumentation; alarm analysis, i.e., analysis of multiple alarm situations to find which alarms are directly connected to primary faults and which alarms are consequential effects of the primary ones; and fault diagnosis, i.e., a search for the causes of and remedies for faults. The three methods use multilevel flow models (MFM), to describe the target process. They have been implemented in the real-time expert system tool G2, in C, and in Common Lisp, and successfully tested on simulations of several processes. The knowledge representation ontology used is based on the notion of flows, of mass, energy, and information, which are used to describe physical systems. The relationships between structure and function of a system is described by teleological relations, which connect the flow structures into a graph, built at model construction time. This allows the diagnostic reasoning to be implemented as searches in a static graph structure, and it can thus be performed extremely rapidly. As with other model-based approaches, general algorithms are used over a representation with generative capacities. The representation gains strength from being functional with a very abstract physical level, more abstract than most qualitative physics models. It works well with systems that can be described using flows, while it currently lacks the capability of capturing important aspects of other types of systems, for example, electronic circuits.