ELECTRICAL SIGNAL BASED FAULT DETECTION AND DIAGNOSIS FOR ROOFTOP UNITS
Document Type Article
A DISSERTATION Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Doctor of Philosophy, Major: Architectural Engineering, Under the Supervision of Professors Josephine Lau and Mingsheng Liu. Lincoln, Nebraska: November, 2012
Copyright (c) 2012 Yunhua Li
Fault detection and diagnosis (FDD) technology, as an effective approach, could identify the occurrence of common faults on rooftop units (RTUs) at the initial stage and prevent them from becoming severe. Although great progress have been made on the approaches, algorithms, control integration and commercialization, the issues of reliability and cost‐effectiveness have not been completely solved yet. In this study, an electrical signal based fault detection and diagnosis method is proposed, which can be applied to the new and retrofitted RTUs with a variable frequency drive (VFD) installed for both indoor fan and compressor. This method could be implemented in the existing or add‐on controller without intruding into the refrigeration system and interrupting the system functioning. There are four steps to implement this method. The first thing is developing a performance baseline by trending the VFD speed, power, supply air temperature, outside air temperature and space temperature. The second one is real‐time monitoring the system operations. The occurrence of faults can be detected by comparing the actual performance data with the baseline. The next step is identifying one or multiple faults using their unique signatures. The last step is evaluating the system performance and giving a response based on the fault level: tolerate or give an alarm for repair. In this study, a literature review was presented firstly. The principles were introduced to provide theoretical foundation. Then, a series of experiments were conducted to investigate the relationships between the VFD power and common faults and other driving conditions. The signatures of individual fault were summarized. In order to build the system performance baseline, a series of models were developed including motor efficiency, belt efficiency, normal state fan power, compressor power, total power, and supply air temperature. Next, the FDD method was developed using the proposed models. Finally, a group of field tests were carried out to demonstrate the feasibility and validity of the proposed method.
Adviser: Josephine Lau, Mingsheng Liu