IRO Journals

Journal of Trends in Computer Science and Smart Technology

A Review on Finding Efficient Approach to Detect Customer Emotion Analysis using Deep Learning Analysis
Volume-3 | Issue-2

A Comparative Analysis of Prediction of Student Results Using Decision Trees and Random Forest
Volume-4 | Issue-3

A Review on Microstrip Patch Antenna Performance Improvement Techniques on Various Applications
Volume-3 | Issue-3

Computer Vision on IOT Based Patient Preference Management System
Volume-2 | Issue-2

Study of Security Mechanisms to Create a Secure Cloud in a Virtual Environment with the Support of Cloud Service Providers
Volume-2 | Issue-3

Secure and Optimized Cloud-Based Cyber-Physical Systems with Memory-Aware Scheduling Scheme
Volume-2 | Issue-3

Construction of Black Box to Detect the Location of Road Mishap in Remote Area in the IoT Domain
Volume-3 | Issue-2

DELAY DIMINISHED EFFICIENT TASK SCHEDULING AND ALLOCATION FOR HETEROGENEOUS CLOUD ENVIRONMENT
Volume-1 | Issue-1

Fault Diagnosis in Hybrid Renewable Energy Sources with Machine Learning Approach
Volume-3 | Issue-3

Green IoT: Current Scenario & Future Prospects
Volume-2 | Issue-4

A Comparative Analysis of Prediction of Student Results Using Decision Trees and Random Forest
Volume-4 | Issue-3

Fake News Detection using Data Mining Techniques
Volume-3 | Issue-4

Speedy Detection Module for Abandoned Belongings in Airport Using Improved Image Processing Technique
Volume-3 | Issue-4

SDN Controller and Blockchain to Secure Information Transaction in a Cluster Structure
Volume-3 | Issue-2

Design an Early Detection and Classification for Diabetic Retinopathy by Deep Feature Extraction based Convolution Neural Network
Volume-3 | Issue-2

Deployment of Artificial Intelligence with Bootstrapped Meta-Learning in Cyber Security
Volume-4 | Issue-3

A Review on Finding Efficient Approach to Detect Customer Emotion Analysis using Deep Learning Analysis
Volume-3 | Issue-2

Efficient Routing Algorithm using MLP and RBX in a Four Model Neural Networks
Volume-3 | Issue-3

Construction of reliable image captioning system for web camera based traffic analysis on road transport application
Volume-3 | Issue-2

Design of an Intelligent Approach on Capsule Networks to Detect Forged Images
Volume-3 | Issue-3

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
Vivekanadam Balasubramaniam  223  145
Pages: 49-58
Cite this article
Balasubramaniam, V. (2021). Fault Detection and Diagnosis in Air Handling Units with a Novel Integrated Decision Tree Algorithm. Journal of Trends in Computer Science and Smart Technology, 3(1), 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

Full Article PDF Download Article PDF 
×

Currently, subscription is the only source of revenue. The subscription resource covers the operating expenses such as web presence, online version, pre-press preparations, and staff wages.

Subscription Payment Details

payumoney (INR): https://pmny.in/Er7GXftJ2HVB

paypal (USD): https://www.paypal.me/InventiveResOrg

Subscription Fee

Quarterly Subscription 2000 INR / 30 USD
Annual Subscription 5000 INR / 100 USD
Subscription form: click here