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首页> 外文会议>ASME(American Society of Mechanical Engineers) International Mechanical Engineering Congress: Advanced Energy Systems Division; 20031115-20031121; Washington,DC; US >AN IMPROVED THERMOECONOMIC DIAGNOSIS PROCEDURE FOR THE DETECTION OF DIFFERENT MALFUNCTIONS OF COMPLEX ENERGY SYSTEMS
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AN IMPROVED THERMOECONOMIC DIAGNOSIS PROCEDURE FOR THE DETECTION OF DIFFERENT MALFUNCTIONS OF COMPLEX ENERGY SYSTEMS

机译：改进的热经济诊断方法，用于检测复杂能量系统的不同功能

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摘要

Diagnosis of energy systems mainly consists of detecting and locating anomalies that cause reduction in the system efficiency or can cause major failures. This is an important task due to its economic implications. The attention is here focused on the anomalies that affect the system efficiency. The problem of their location is not easy to solve, due to some 'disturbs' that make propagate the effects of an anomaly throughout the system. These effects are caused by the dependence of the components' behavior on their operating conditions. Moreover they can be amplified by the intervention of the control system and the variations in ambient conditions, fuel quality and plant load. A technique for highly complex systems has been proposed in [1]. This procedure, based on the hypothesis of small malfunctions, consists of the progressive elimination of the disturbs, so that the anomalies could be more clearly highlighted. In this paper, a procedure particularly suitable for the application to operating plants is adopted to overcome the hypothesis of small malfunction. It consists of a combination of two techniques: 1) the use of neural networks for the elimination of the malfunctions induced by the dependence of efficiency of components on the operating conditions and 2) the successive application of the analysis to several operating conditions selected within the plant case history.
机译：能源系统的诊断主要包括检测和定位导致系统效率降低或可能导致重大故障的异常。由于其经济意义，这是一项重要任务。这里的注意力集中在影响系统效率的异常上。由于某些“干扰”使异常的影响在整个系统中传播，因此它们的位置问题不容易解决。这些影响是由组件的行为对其操作条件的依赖性引起的。此外，可以通过控制系统的干预以及周围环境，燃料质量和工厂负荷的变化来放大它们。在[1]中已经提出了一种用于高度复杂系统的技术。基于小故障的假设，此过程包括逐步消除干扰，以便可以更清楚地突出异常。在本文中，采用了一种特别适用于运行工厂的程序，以克服小故障的假设。它由两种技术的组合组成：1）使用神经网络消除因组件效率对运行条件的依赖性而引起的故障，以及2）将分析连续应用于应用程序中选择的几种运行条件植物病历。

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