Inference-based decentralized prognosis in discrete event systems
Inference-based decentralized prognosis in discrete event systems
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DOI：
10.1109/tac.2010.2085590
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发表时间：
2008-12
期刊：
影响因子：
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通讯作者：
S. Takai;Ratnesh Kumar
中科院分区：
文献类型：
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作者：
S. Takai;Ratnesh Kumar
对于离散事件系统，我们研究了在故障发生之前预测故障的问题，也称为预后，在基于推理的分散框架中，多个决策者相互作用，以提出全局预后决策。由于感知能力有限，每个决策者在决策过程中都会遇到模糊问题。在我们以前的工作中，我们观察到这种模糊性是不同的层次，并提出了一个框架，用于推断不同模糊度水平的局部控制决策，以达到一个全球控制决策。在这里，我们提出了一个基于推理的分散式决策框架的故障预测：对于每个事件跟踪执行的系统被监视，每个本地的预测器发出自己的预测决定（故障是或不是不可避免的，或不确定）标记有一定的模糊度水平（零是最小值），这是通过评估的模糊性的自我和他人。全局预测决策被认为是“获胜”的局部预测决策，即，具有最小模糊度的一个。我们的系统，其中有没有遗漏的检测（所有的故障都可以在其发生之前被发现）和没有假警报（所有的预后决策是正确的）的特点，通过引入的概念N-推理可预测性，其中参数N表示的最大模糊度水平的任何获胜的预后决策。给出了一个N-推理可推理性的验证算法。我们还表明，概念的cohesisability引进是相同的0-推断，cohesisability，并作为参数N的增加，一个更大的类的可推断系统。
For discrete event systems, we study the problem of predicting failures prior to their occurrence, also referred to as prognosis, in the inference-based decentralized framework where multiple decision-makers interact to come up with the global prognostic decisions. Due to the limited sensing capabilities, each decision-maker is subjected to ambiguities during the process of decision-making. In our prior work we made an observation that such ambiguities are of differing gradations and presented a framework for inferencing over the local control decisions of varying ambiguity levels to arrive at a global control decision. Here we present an inference-based decentralized decision-making framework for prognosis of failures: For each event-trace executed by a system being monitored, each local prognoser issues its own prognostic decision (failure is or is not inevitable, or unsure) tagged with a certain ambiguity level (zero being the minimum) that is computed by assessing the ambiguities of the self and the others. A global prognostic decision is taken to be the ¿winning¿ local prognostic decision, i.e., one with the minimum ambiguity level. We characterize the class of systems for which there are no missed detections (all failures can be prognosed prior to their occurrence) and no false alarms (all prognostic decisions are correct) by introducing the notion of N-inference-prognosability, where the parameter N represents the maximum ambiguity level of any winning prognostic decision. An algorithm for verifying N-inference-prognosability is presented. We also show that the notion of coprognosability introduced is the same as 0-inference-prognosability, and as the parameter N is increased, a larger class of prognosable systems is obtained.