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Diagnostics and Prognostics of Energy Conversion Processes via Knowledge-Based Systems

机译:基于知识的系统的能量转换过程的诊断和预测

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This paper presents a critical and analytical description of an ongoing research program aimed at the implementation of an expert system capable of monitoring, through an Intelligent Health Control procedure, the instantaneous performance of a cogeneration plant. The expert system is implemented in the CLIPS environment and is denominated PROMISA as the acronym for Prognostic Module for Intelligent System Analysis. It generates, in real time and in a form directly useful to the plant manager, information on the existence and severity of faults, forecasts on the future time history of both detected and likely faults, and suggestions on how to control the problem. The expert procedure, working where and if necessary with the support of a process simulator, derives from the available real-time data a list of selected performance indicators for each plant component. For a set of faults, pre-defined with the help of the plant operator (Domain Expert), proper rules are defined in order to establish whether the component is working correctly; in several instances, since one single failure (symptom) can originate from more than one fault (cause), complex sets of rules expressing the combination of multiple indices have been introduced in the knowledge base as well. Creeping faults are detected by analyzing the trend of the variation of an indicator over a pre-assigned interval of time. Whenever the value of this ‘‘discrete time derivative’’ becomes ‘‘high’’ with respect to a specified limit value, a ‘‘latent creeping fault’’ condition is prognosticated. The expert system architecture is based on an object-oriented paradigm. The knowledge base (facts and rules) is clustered—the chunks of knowledge pertain to individual components. A graphic user interface (GUI) allows the user to interrogate PROMISA about its rules, procedures, classes and objects, and about its inference path. The paper also presents the results of some simulation tests.
机译:本文介绍了持续的研究计划的关键和分析描述,旨在通过智能健康控制程序,通过智能健康控制程序,热电联产厂的瞬时性能进行监测的专家系统。专家系统是在剪辑环境中实现的,并作为智能系统分析的预后模块作为缩写。它实时和以直接对工厂经理直接有用的表单生成有关故障存在和严重性的信息,预测未来检测到和可能的故障的未来时间历史,以及如何控制问题的建议。专家过程,在使用过程模拟器的支持之后,在必要时,从可用的实时数据派生每个工厂组件的可用实时数据列表。对于一组故障,在植物运算符(域专家)的帮助下预定义,定义了适当的规则,以便建立组件是否正常工作;在几个实例中,由于一个单一失败(症状)可以源自一个以上的故障(原因),因此在知识库中也引入了表达多个指标组合的复杂规则。通过在预先分配的时间间隔内分析指示器变化的趋势来检测爬行故障。每当这个''离散时间导数'''的值变为“高”的“高”',相对于指定的限制值,“潜在的爬行故障”的条件是预后的。专家系统体系结构基于面向对象的范例。知识库(事实和规则)是集群 - 知识量与各个组件有关。图形用户界面(GUI)允许用户询问ProMISA关于其规则,过程,类和对象以及其推理路径。本文还介绍了一些模拟测试的结果。

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