Automatic diagnostics and prognostics of energy conversion processes via knowledge-based systems
Automatic diagnostics and prognostics of energy conversion processes via knowledge-based systems
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DOI：
10.1016/j.energy.2004.03.031
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发表时间：
2004-10
期刊：
影响因子：
9
通讯作者：
T. Biagetti;E. Sciubba
中科院分区：
文献类型：
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作者：
T. Biagetti;E. Sciubba
本文对一项正在进行的研究计划进行了批判性和分析性的描述，该研究计划旨在实施一个能够通过智能健康控制程序监测热电联产厂瞬时性能的专家系统。一个应用程序已经在位于ENEA-Casaccia能源实验室的一个真实工厂上进行了测试。该专家系统名为PROMISE，是意大利语中“预测和智能监测专家系统”的首字母缩略词，它以一种对工厂经理直接有用的形式实时生成有关故障存在和严重程度的信息，预测已检测到的和可能出现的故障的未来时间历史，并就如何控制问题提出建议。专家程序在必要时在过程模拟器的支持下工作，从实时数据中得出每个工厂组件的选定性能指标列表。对于一组故障，在工厂操作员的帮助下预先定义，定义适当的规则，以确定组件是否正常工作；在一些情况下，由于单个故障（症状）可能源于多个故障（原因），因此在知识库中还引入了表示多个指标组合的复杂规则集。蠕变故障是通过分析某一指标在预先设定的时间段内的变化趋势来检测的。每当这个“离散时间导数”的值相对于一个指定的极限值变得“高”时，就预示着一个“潜在的蠕变故障”状态。专家系统体系结构基于面向对象的范式。知识库（事实和规则）是聚类的：知识块属于单个组件。图形用户界面（GUI）允许用户询问PROMISE关于它的规则、过程、类和对象，以及它的推理路径。本文还介绍了在真实植物上进行的一些试验结果。
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. An application has been tested on a real plant, located on the grounds of the ENEA-Casaccia Energy Laboratories. The expert system, denominated PROMISE as the Italian acronym for PROgnostic and Intelligent Monitoring Expert System, 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 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, 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 in 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 prognosed. 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 PROMISE about its rules, procedures, classes and objects, and about its inference path. The paper also presents the results of some tests conducted on the real plant.