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Makale detayı · 2024 · article

ARTIFICIAL NEURAL NETWORK BASED FAULT DETECTION AND CLASSIFICATION METHOD FOR AIR CONDITIONERS

Dergi Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering
ISSN2667-4211
YÖKSİS OpenAlex Açık erişim · diamond TR Index
Yıl2024
Atıf1OpenAlex
Yüzdelik%44,7
FWCI0,171,00 = dünya ortalaması

Veri kaynağı ayrımı

  • YÖKSİSYÖKSİS makale kaydı
  • YÖKSİS dergi adıEskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering
  • OpenAlexOpenAlex zenginleştirmesi (özet, atıf, konular)
  • Semantic Scholaratıf sayısı (OpenAlex ile birleştirilmez)

Özet

OpenAlex İngilizce

Air Conditioners (AC)s are devices that balance the air exchange and humidity rate as well as provide heating/cooling functions in order to keep the temperature of the environment within the desired conditions and needs. In this study, a new fault detection and classification method for AC is proposed. The method is based on the fact that power consumptions of appliances imply significant information about the appliances’ health. Hence, according to the proposed method, power profiles of considered AC are created during its operations. Artificial Neural Network (ANN) configuration of AC is specifically designed and trained by created power profiles. Trained ANN is used to detect and classify faults in the present power profile before major malfunctions occur. Taking action against detected faults helps prevent increased power consumption and serious security issues. Performance and efficiency of the Method designed for classification and detection of errors is between 95.1% - 97.01%

Konular

Atıflar

OpenAlex cited_by_count. WoS veya Scopus atıf sayısı değildir; o kaynaklar için ayrı kolon yoktur.

1atıfOpenAlex · cited_by_count (önbellek / veritabanı)

Yazarlar

2
  1. HANİFE APAYDIN ÖZKAN ESKİŞEHİR TEKNİK ÜNİVERSİTESİ 1
  2. CENGİZHAN ABAY 2