On-Line Models for Use in Automated Fault Detection and Diagnosis for HVACR Equipment

摘要

To improve overall fault detection and diagnostic (FDD) performance, many of the techniques proposed for automated FDD rely on the use of steady-state models for expected values of operating states under normal operating conditions. Furthermore, on-line measurements and models for overall performance (e.g., EER) are useful in evaluating whether faults are serious enough for service to be performed. This paper presents a modeling approach for operating temperatures that combines a low-order polynomial with a general regression neural network (GRNN) to achieve both good interpolating and extrapolating performance. Using experimental data for a rooftop air conditioner, the performance of this modeling approach was evaluated and compared with several other black-box modeling approaches, including simple polynomials, pure general regression neural networks (GRNN), back-propagation neural networks, and radial-basis functions. This approach was further validated using data collected from a field site at Purdue University. In addition, a simple method for estimating cooling capacity, power consumption, and EER using low-cost measurements is presented and evaluated.