Fault Detection and Diagnosis in Air-Handling Unit (AHU) Using Improved Hybrid 1D Convolutional Neural Network DOI Creative Commons

P Kumar,

Byungun Yoon, Prashant Kumar

et al.

Systems, Journal Year: 2025, Volume and Issue: 13(5), P. 330 - 330

Published: May 1, 2025

The air-handling unit (AHU) is an essential component of heating, ventilation, and air-conditioning (HVAC) systems. Hence, detecting the faults in AHUs for maintaining continuous HVAC operation preventing system breakdowns. advent artificial intelligence has transformed AHU fault diagnosis techniques. Specifically, deep learning obviated necessity manual feature extraction selection, thereby streamlining process. While conventional convolutional neural networks (CNNs) effectively detect defects, incorporating more spatial variables could enhance their performance further. This paper presents a hybrid architecture combining CNN model with long short-term memory (LSTM) to diagnose AHUs. advantages LSTM layers are combined identify significant patterns input data, which considerably facilitates detection defects. design enhances network’s capability capture both local global characteristics, thus improving its ability differentiate between normal abnormal circumstances. proposed approach achieves strong diagnostic accuracy, exhibiting high sensitivity nuanced patterns. Furthermore, efficacy corroborated through comparisons state-of-the-art identification

Language: Английский

Fault Detection and Diagnosis in Air-Handling Unit (AHU) Using Improved Hybrid 1D Convolutional Neural Network DOI Creative Commons

P Kumar,

Byungun Yoon, Prashant Kumar

et al.

Systems, Journal Year: 2025, Volume and Issue: 13(5), P. 330 - 330

Published: May 1, 2025

The air-handling unit (AHU) is an essential component of heating, ventilation, and air-conditioning (HVAC) systems. Hence, detecting the faults in AHUs for maintaining continuous HVAC operation preventing system breakdowns. advent artificial intelligence has transformed AHU fault diagnosis techniques. Specifically, deep learning obviated necessity manual feature extraction selection, thereby streamlining process. While conventional convolutional neural networks (CNNs) effectively detect defects, incorporating more spatial variables could enhance their performance further. This paper presents a hybrid architecture combining CNN model with long short-term memory (LSTM) to diagnose AHUs. advantages LSTM layers are combined identify significant patterns input data, which considerably facilitates detection defects. design enhances network’s capability capture both local global characteristics, thus improving its ability differentiate between normal abnormal circumstances. proposed approach achieves strong diagnostic accuracy, exhibiting high sensitivity nuanced patterns. Furthermore, efficacy corroborated through comparisons state-of-the-art identification

Language: Английский

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