Sensor fault detection and diagnosis of air handling units

HVAC systems in modern buildings are heavily instrumented to realize automatic monitoring and control. Measurements from sensors not only indicate operation conditions, but also take part in control and optimization. Accurate measurements are essential for reliable monitoring, control and energy management. On the other hand, the performance of fault detection and diagnosis (FDD) methods applied in HVAC systems depends strongly on the quality and reliability of sensor measurements. However, sensors may suffer from various faults, including drift, bias, accuracy decrease and complete failure. Although a great deal of research has been carried on the FDD of HVAC components, not much has concerned sensor faults and sensor validation in HVAC systems. This thesis developed a robust strategy, which consists of a basic scheme and a condition-based adaptive scheme, for online detection and diagnosis of sensor faults in air handling units (AHUs).; The basic scheme includes three major steps: data pre-process, sensor fault detection, and sensor fault diagnosis. Transient data and outliers are removed from the training data and new samples using a data pre-processor. The PCA method is adopted and improved for sensor FDD of AHUs. Correlation matrixes are used to depict correlations among variables in air handling processes. Statistics are used to measure the variance of the correlations, and their upper limits usually define the normal ranges of the variance. If the statistics exceed the normal ranges, it indicates that the correlations among variables are disturbed and something abnormal has happened. Two kinds of statistics are used in this FDD application, i.e. the Hotelling T 2 and the Q-statistic. The Hotelling T 2 is mainly used to preprocess the training data and measurement data to be analyzed to improve their quality. The Q-statistic is used to detect abnormalities. Independent heat balance and pressure-flow balance PCA models were developed to make variables in each PCA model more closely correlate, therefore enhance the stability of PCA models. The usage of two PCA models in parallel also helps to isolate faulty sensors.; In order to enhance the ability of the PCA method in isolating faulty sensors, the Signed Diagraph (SDG) is adopted to supplement the Q-contribution plot which is commonly used for fault isolation in PCA-based FDD methods. (Abstract shortened by UMI.)...