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A fault detection and diagnosis strategy for VAV air distribution system.

机译：VAV空气分配系统的故障检测和诊断策略。

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摘要

Variable air volume (VAV) systems and their control strategies become more and more complex in order to meet the increasing demands on indoor environment quality and energy conservation. Automatic monitoring and control of VAV systems are inevitable in modern buildings. Many supervisory VAV control strategies, such as supply air temperature reset, static pressure reset and advanced fresh air flow rate control, have been put into operation as well. Both components and sensors are playing essential roles in operation and control. Components and sensors in VAV air distribution systems often suffer from complete failure (hard fault) and partial failure (soft fault) easily, which result in energy waste, performance degradation or totally out of control. Therefore, fault detection and diagnosis (FDD) for VAV systems, especially for large-scale systems in which dozens of VAV boxes are involved, provides great benefits in improving system control and indoor environment quality, enhancing building energy efficiency, and prolonging components' life. However, studies on FDD for VAV systems are not sufficient and there is no applicable automatic commissioning tool for the whole VAV air distribution systems.; An automatic FDD strategy for VAV air distribution systems is developed in this study. A software package is developed on the basis of the FDD strategy for automatic commissioning. Prior to developing the FDD strategy, a site survey on the faults in practical VAV terminals was conducted. It was about a commercial building with 1251 VAV terminals in total. 20.9% VAV terminals were found ineffective and eleven root faults were identified in pressure-independent VAV systems. The FDD strategy therefore chooses these eleven root faults as the objects to be handled.; The FDD strategy is built up based on system knowledge, qualitative states and object-oriented SPC (statistical process control) models. Eight FDD schemes, organized at two steps, are set up to detect the eleven VAV root faults within the qualitative/quantitative FDD strategy. Ten faults, which would affect the system operation, are handled at Step 1 in parallel using the first seven schemes. The eleventh fault, which would not affect the basic system operation but would lead to imperfection under advanced supervisory control, is analyzed at Step 2 using the eighth scheme. The FDD strategy is tested and validated on typical VAV air-conditioning systems involving multiple faults both in simulation and in-situ tests.; Integrating quantitative models with qualitative knowledge helps to solve decision making problems more effectively and efficiently. Three schemes are developed simply from characteristic equations or based on qualitative states for simple fault detection like controller hard failure or damper stuck. However, other five schemes need further quantitative SPC models for fault detection or identification after the faulty patterns are recognized by characteristic equations or qualitative states.; The eighth scheme is developed for VAV terminal flow sensor bias detection and sensor reconstruction. PCA (Principal Component Analysis) models, at both system level and terminal level, are built and employed in the scheme. Sensor biases are detected using both T2 statistic and SPE (Square Prediction Error) and isolated using SPE contribution plot. As the reliability and sensitivity of fault isolation may be affected by the multiple sensor faults at the system level, terminal level PCA model is designed to further examine the suspicious terminals. The faulty sensor is reconstructed after it is isolated by the scheme and the fault detection process repeats using the latest reconstructed measurements until no further fault can be detected. Thus, the sensitivity and robustness of the scheme are enhanced significantly.; A software package is developed to implement the FDD strategy for automatic commissioning. With the data downloaded from the BMS, the pre-defined root faults could
机译：可变风量（VAV）系统及其控制策略变得越来越复杂，以满足对室内环境质量和节能的日益增长的需求。在现代建筑中，VAV系统的自动监视和控制是不可避免的。许多监督性VAV控制策略，例如送风温度重置，静压重置和先进的新鲜空气流量控制，也已投入运行。组件和传感器在操作和控制中都起着至关重要的作用。 VAV空气分配系统中的组件和传感器通常容易遭受完全故障（硬故障）和部分故障（软故障）的困扰，这会导致能源浪费，性能下降或完全失控。因此，VAV系统的故障检测与诊断（FDD），尤其是涉及数十个VAV盒的大型系统，在改善系统控制和室内环境质量，提高建筑节能效率以及延长组件寿命方面具有巨大优势。 。但是，对VAV系统FDD的研究还不够，并且没有适用于整个VAV空气分配系统的自动调试工具。在这项研究中，开发了一种用于VAV空气分配系统的自动FDD策略。在FDD策略的基础上开发了用于自动调试的软件包。在制定FDD策略之前，对实际的VAV终端中的故障进行了现场调查。这是一栋商业建筑物，总共有1251个VAV终端。发现20.9％的VAV终端无效，并且在与压力无关的VAV系统中发现了11个根故障。因此，FDD策略选择这11个根本故障作为要处理的对象。 FDD策略是基于系统知识，定性状态和面向对象的SPC（统计过程控制）模型构建的。建立了两个步骤组成的八个FDD方案，以检测定性/定量FDD策略中的11个VAV根故障。在步骤1中，使用前七个方案并行处理十个可能影响系统运行的故障。在第2步中，使用第八种方案分析了第十一个故障，该故障不会影响基本的系统操作，但会导致在高级监控控制下的缺陷。 FDD策略是在典型的VAV空调系统上进行测试和验证的，该系统在仿真和现场测试中都涉及多个故障。将定量模型与定性知识相集成有助于更有效地解决决策问题。简单地从特性方程式或基于定性状态开发三种方案，以进行简单的故障检测，例如控制器硬故障或阻尼器卡住。但是，在通过特征方程或定性状态识别出故障模式之后，其他五种方案还需要进一步的定量SPC模型来进行故障检测或识别。开发了第八种方案，用于VAV终端流量传感器偏置检测和传感器重建。在系统级别和终端级别的PCA（主成分分析）模型均已建立并在该方案中采用。使用T2统计量和SPE（平方预测误差）检测传感器偏差，并使用SPE贡献图进行隔离。由于故障隔离的可靠性和敏感性可能会受到系统级别的多个传感器故障的影响，因此设计了终端级别PCA模型以进一步检查可疑终端。通过方案隔离故障传感器后，将对其进行重构，并使用最新重构的测量结果重复进行故障检测过程，直到无法检测到其他故障为止。因此，该方案的灵敏度和鲁棒性大大提高。开发了用于实施FDD策略以进行自动调试的软件包。使用从BMS下载的数据，可以预定义根故障

著录项

  • 作者

    Qin, Jianying.;

  • 作者单位

    Hong Kong Polytechnic University (People's Republic of China).;

  • 授予单位 Hong Kong Polytechnic University (People's Republic of China).;
  • 学科 Engineering Mechanical.
  • 学位 Ph.D.
  • 年度 2006
  • 页码 234 p.
  • 总页数 234
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 机械、仪表工业;
  • 关键词

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