TY  - EJOU
AU  - Anitha, G. 
AU  - Supriya, N. 
AU  - Alenezi, Fayadh 
AU  - Lydia, E. Laxmi 
AU  - Joshi, Gyanendra Prasad 
AU  - You, Jinsang 

TI  - Chicken Swarm Optimization with Deep Learning Based Packaged Rooftop Units Fault Diagnosis Model
T2  - Computer Systems Science and Engineering

PY  - 2023
VL  - 47
IS  - 1
SN  - 

AB  - Rooftop units (RTUs) were commonly employed in small commercial buildings that represent that can frequently do not take the higher level maintenance that chillers receive. Fault detection and diagnosis (FDD) tools can be employed for RTU methods to ensure essential faults are addressed promptly. In this aspect, this article presents an Optimal Deep Belief Network based Fault Detection and Classification on Packaged Rooftop Units (ODBNFDC-PRTU) model. The ODBNFDC-PRTU technique considers fault diagnosis as a multi-class classification problem and is handled using DL models. For fault diagnosis in RTUs, the ODBNFDC-PRTU model exploits the deep belief network (DBN) classification model, which identifies seven distinct types of faults. At the same time, the chicken swarm optimization (CSO) algorithm-based hyperparameter tuning technique is utilized for resolving the trial and error hyperparameter selection process, showing the novelty of the work. To illustrate the enhanced performance of the ODBNFDC-PRTU algorithm, a comprehensive set of simulations are applied. The comparison study described the improvement of the ODBNFDC-PRTU method over other recent FDD algorithms with maximum accuracy of 99.30% and TPR of 93.09%.
KW  - Rooftop units; chicken swarm optimization; hyperparameter; metaheuristics; deep learning; fault diagnosis

DO  - 10.32604/csse.2023.036479