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|Title:||A fault detection and diagnosis strategy for VAV air distribution system||Authors:||Qin, Jianying||Keywords:||Variable air volume systems (Air conditioning)
Hong Kong Polytechnic University -- Dissertations
|Issue Date:||2006||Publisher:||The Hong Kong Polytechnic University||Abstract:||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 be detected and faulty sensor(s) could be reconstructed by the software. The main FDD report presents a list of major information of commissioning and a few other reports give other detailed information related to the characteristic parameters of the system concerned.||Description:||xvii, 234 leaves : ill. ; 30 cm.
PolyU Library Call No.: [THS] LG51 .H577P BSE 2006 Qin
|URI:||http://hdl.handle.net/10397/904||Rights:||All rights reserved.|
|Appears in Collections:||Thesis|
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