Information Theoretic Approaches for Equipment Condition Assessment, Diagnostics and Maintenance

9527302 Tomsovic该项目旨在开发一种评估电力系统设备状况的信息论方法，目的是提高电力系统的整体完整性。鉴于对国家民用基础设施系统质量的担忧，这项研究被认为特别重要。就整个电力系统的可靠性和安全性而言，单个设备的状态起着至关重要的作用。电力公用事业公司将大量精力用于检测加速老化和防止灾难性设备故障。传统的维护和状态监测计划主要基于制造商建议的固定时间间隔。近年来，由于更大的经济压力，公用事业公司已经开始转向更长的维护间隔，以及第二，装载远远超出制造商额定值的设备。在正常情况下，有足够的设计余量，以减少维护和一些超载。最近的传感器发展提供了通过监控某些关键变量来进一步扩展设备限制的能力。不幸的是，人们对大多数设备的故障机制只有部分了解，因此开发的设备故障早期检测方法是不准确和不可靠的。要克服这种印象并进行有效的诊断，需要对特定技术有广泛的经验。此外，大多数设备的预期寿命相对较长，因此可能没有重大的故障日期来执行有意义的故障统计模型。综合上述因素，表明公用事业公司正在遵循实践，而对系统可靠性的长期影响知之甚少。他们可能没有完全理解他们的设备可以被推向的极限。此外，他们缺乏计算框架，无法将他们的数据和经验包含在可行的模型中。这项工作的重点是开发一个信息理论框架，用于对设备故障的不完全知识进行建模，并能够随着经验的积累而容易地融入新的信息。长期目标是在一个模型中允许统计、处理和启发式数据，以便改进关于维护间隔、诊断结论和可靠性评估的建议。在开发该模型时，本项目将侧重于电力系统设备的基本一般原理。***

9527302 Tomsovic This project is aimed at developing an information theoretic approach for assessing power system equipment condition with the goal of improving the overall integrity of the power system. This research is felt to be particularly important in light of concerns with the quality of the nation's civil infrastructure system. In terms of overall power system reliability and security, individual equipment condition plays a critical role. Electric utilities direct significant effort towards detecting accelerated aging and preventing catastrophic equipment failures. Traditional maintenance and condition monitoring schedules were based primarily on manufacturer recommended fixed time intervals. In recent years due to greater economic pressures, utilities have begun, one, to shift towards longer maintenance intervals and two, to load equipment significantly beyond manufacturer ratings. Under normal conditions, there is adequate design margin to allow both reduced maintenance and some overloading. Recent sensor developments offer the ability to further stretch equipment limits by monitoring certain key variables. Unfortunately, the breakdown mechanism of most equipment are only partially understood so that developed methods for early detection of equipment failures are imprecise and unreliable. Broad experience with a particular technique is necessary to overcome this impression and perform effective diagnosis. In addition, most equipment has relatively long expected lifetimes so that there may not be significant failure date to perform meaningful statistical models of failures. Together the above factors, suggest that utilities are following practices with limited knowledge of the long term impacts on system reliability. They may not fully understand the limits to which their equipment can be pushed. Further, they lack the computational framework to include their data and the experience in a workable model. This work focuses on developing an info rmation theoretic framework for modeling the imperfect knowledge of equipment breakdown with the ability to easily incorporate new information as experience is gained. The long term objective is to allow both statistical, process and heuristic data in a model which allows for improved suggestions on maintenance intervals, diagnostic conclusions and reliability evaluation. This project will focus on underlying general principles of power system equipment in developing this model. ***