Journal of Trends in Computer Science and Smart Technology is accepted for inclusion in Scopus. click here
Home / Archives / Volume-3 / Issue-1 / Article-5

Volume - 3 | Issue - 1 | march 2021

Fault Detection and Diagnosis in Air Handling Units with a Novel Integrated Decision Tree Algorithm
Pages: 49-58
DOI
10.36548/jtcsst.2021.1.005
Published
06 May, 2021
Abstract

In air handling units (AHUs), wide attention has been attracted by data-driven fault detection and diagnosis techniques as the need for high-level expert knowledge of the concerned system is eliminated. In AHUs, decision tree induction is performed by means of classification and regression tree algorithm which is a data-driven diagnostic strategy based on decision tree. Expert knowledge as well as testing data may be used for validation of fault diagnosis reliability with easy interpretation and understanding ability offered by the decision tree. The diagnostic strategy established and its interpretability are increased by incorporating a regression model and steady-state detector with the model. ASHRAE, Oak Ridge National Lab (ORNL), National Renewable Energy Lab (NREL), Pacific Northwest National Lab (PNNL) and Lawrence Berkeley National Lab (LBNL) datasets are used for validation of the proposed strategy. High average F-measure and improved diagnostic performance may be achieved with this strategy. There is a compliance between the expert knowledge and certain diagnostic rules generated in the decision tree as seen from the expert knowledge implemented diagnostic decision tree interpretation. Based on the interpretation, it is evident that certain diagnostic rules are valid only under specific operating conditions and some of the generated diagnostic rules are not reliable. Data driven models are used for emphasizing the significance of interpretability of fault diagnostic models.

Keywords

Interpretation Fault Diagnosis Air Handling Unit Decision Tree Feature selection Fault Detection

×
Article Processing Charges

Journal of Trends in Computer Science and Smart Technology (jtcsst) is an open access journal. When a paper is accepted for publication, authors are required to pay Article Processing Charges (APCs) to cover its editorial and production costs. The APC for each submission is 400 USD. There are no additional charges based on color, length, figures, or other elements.

Category Fee
Article Access Charge 30 USD
Article Processing Charge 400 USD
Annual Subscription Fee 200 USD
Payment Gateway
Paypal: click here
Townscript: click here
Razorpay: click here
After payment,
please send an email to irojournals.contact@gmail.com / journals@iroglobal.com requesting article access.
Subscription form: click here