Off-campus UNL users: To download campus access dissertations, please use the following link to log into our proxy server with your NU ID and password. When you are done browsing please remember to return to this page and log out.
Non-UNL users: Please talk to your librarian about requesting this dissertation through interlibrary loan.
A Generalized Machine Learning-Based Classifier Considering Cost-Effective Features for Automated Fault Detection and Diagnosis (AFDD) of Packaged Rooftop Units
Abstract
Packaged rooftop units (RTUs) are widely used for space conditioning in commercial buildings and manufacturing facilities. The typical soft faults related to RTUs degrade the system's performance in terms of cooling capacity, power consumption, and Coefficient of Performance (COP), detrimentally affecting both the equipment and energy consumption and the environment. Previous research in soft fault detection for rooftop units lacked classifier validation using lab and field data, developing a generalizable algorithm, and analyzing its performance across varying fault intensities.Using a simulated data library for multiple rooftop units, this study proposes a machine-learning classifier with a reduced set of 9 features (8 quantitative and one qualitative) to detect and diagnose typical soft faults in packaged rooftop units equipped with fixed orifice metering devices. An existing lab testing set consisting of the same training systems was utilized to validate the presented data-driven approach, showing significantly better performance than the existing fault detection and diagnosis protocols. In addition, the analyzed classifier’s predicting performance improves with increasing fault severity.In addition to the above lab validation, a manufacturing facility in Omaha, Nebraska, was chosen for field validation of the developed machine-learning algorithm. The proposed approach accurately predicted all the refrigerant undercharge fault cases from an RTU at that facility, although the RTU significantly differs from the RTUs with which the classifier was trained. The lab and field-testing results bolster that the considered machine-learning classifier can be generalizable, with some exceptions, for detecting the common soft faults from any rooftop unit equipped with a fixed orifice metering device. The presented classifier can be used in an industrial assessment for diagnosing common soft faults from an RTU, helping to develop additional energy and cost savings measures for a facility.
Subject Area
Mechanical engineering|Architectural engineering|Mechanics
Recommended Citation
Uddin, Rasel MD., "A Generalized Machine Learning-Based Classifier Considering Cost-Effective Features for Automated Fault Detection and Diagnosis (AFDD) of Packaged Rooftop Units" (2023). ETD collection for University of Nebraska-Lincoln. AAI30813543.
https://digitalcommons.unl.edu/dissertations/AAI30813543