Fault detection and diagnosis of air-conditioning system using long short-term memory recurrent neural network

• Autorzy: Sulaiman Noor Asyikin , Menanti Nur Amalina Sabal , Yusop Azdiana Md , Zainudin Muhammad Noorazlan Shah , Yatim Norhidayah Mohamad , Razak Norazlina Abd , Abdullah Md Pauzi

Identyfikatory

DOI

10.15199/48.2023.09.21

Warianty tytułu

PL

Wykrywanie usterek i diagnostyka układu klimatyzacji z wykorzystaniem powtarzającej się sieci neuronowej z pamięcią długookresową

• Języki publikacji: EN

Abstrakty

EN

In this project, a fault detection and diagnosis (FDD) system was developed using Long Short-Term Memory Recurrent Neural Network (LSTM RNN), to detect and classify six common faults in a centralised chilled water air conditioning system. Datasets from a lab-scale centralised chilled water air conditioning system were used in the developed model. Results showed that the classifier model demonstrated a classification accuracy of over 99.3% for all six classes.

PL

W ramach tego projektu opracowano system wykrywania i diagnozowania usterek (FDD) z wykorzystaniem powtarzającej się sieci neuronowej długookresowej pamięci (LSTM RNN) w celu wykrycia i sklasyfikowania sześciu powszechnych usterek w scentralizowanym systemie klimatyzacji wody lodowej. W opracowanym modelu wykorzystano zestawy danych ze scentralizowanego systemu klimatyzacji wody lodowej w skali laboratoryjnej. Wyniki pokazały, że model klasyfikatora wykazał dokładność klasyfikacji na poziomie ponad 99,3% dla wszystkich sześciu klas.

Słowa kluczowe

EN

chilled water system; fault detection; LTSM-RNN

PL

system wody lodowej; diagnostyka usterek; wykrywanie usterek; LTSM-RNN

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2023
• Tom: R. 99, nr 9
• Strony: 113--117
• Opis fizyczny: Bibliogr. 20 poz., rys., tab.

Twórcy

autor

Sulaiman Noor Asyikin

• Centre for Telecommunication Research & Innovation (CeTRI), Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

• https://orcid.org/0000-0003-3126-7309

autor

Menanti Nur Amalina Sabal

• Mybrush Industries, Lot 210, Jalan Seelong, 81400 Senai, Johor, Malaysia

autor

Yusop Azdiana Md

• Centre for Telecommunication Research & Innovation (CeTRI), Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

• https://orcid.org/0000-0002-1864-1952

autor

Zainudin Muhammad Noorazlan Shah

• Centre for Telecommunication Research & Innovation (CeTRI), Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

• https://orcid.org/0000-0001-5621-9632

autor

Yatim Norhidayah Mohamad

• Centre for Telecommunication Research & Innovation (CeTRI), Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

• https://orcid.org/0000-0002-6464-8354

autor

Razak Norazlina Abd

• Centre for Telecommunication Research & Innovation (CeTRI), Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

autor

Abdullah Md Pauzi

• Centre of Electrical Energy Systems (CEES), School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), 81310 Skudai, Johor, Malaysia

• https://orcid.org/0000-0002-8357-731X

Bibliografia

• [1] J. Kim, J. Cai, and J. E. Braun, “Common Faults and Their Prioritization in Small Commercial Buildings Common Faults and Their Prioritization in Small Commercial Buildings,” Golden, CO: National Renewable Energy Laboratory, 2018.
• [2] M. F. Othman, H. Abdullah, N. A. Sulaiman, and M. Y. Hassan, “Performance evaluation of an actual building air conditioning system,” IOP Conf. Ser. Mater. Sci. Eng., vol. 50, no. 1, 2013.
• [3] W. Kim and S. Katipamula, “A review of fault detection and diagnostics methods for building systems,” Sci. Technol. Built Environ., vol. 24, no. 1, pp. 3–21, 2018.
• [4] Y. Zhao, C. Zhang, Y. Zhang, Z. Wang, and J. Li, “A review of data mining technologies in building energy systems: Load prediction, pattern identification, fault detection and diagnosis,” Energy Built Environ., vol. 1, no. 2, pp. 149–164, 2020.
• [5] N. A. Sulaiman, “Fault Detection And Diagnosis In Centralised Chilled Water Air Conditioning System,” Universiti Teknologi Malaysia, 2021.
• [6] Z. Li et al., “An Effective Fault Detection and Diagnosis Approach for Chiller System,” IFAC-PapersOnLine, vol. 52, no. 10, pp. 55–60, 2019.
• [7] M. E. S. Trothe, H. R. Shaker, M. Jradi, and K. Arendt, “Fault isolability analysis and optimal sensor placement for fault diagnosis in smart buildings,” Energies, vol. 12, no. 9, 2019.
• [8] B. Li, F. Cheng, X. Zhang, C. Cui, and W. Cai, “A Novel Semi supervised Data-driven Method for Chiller Fault Diagnosis with Unlabeled Data,” Appl. Energy, vol. 285, pp. 1–13, 2021.
• [9] W. S. Yun, W. H. Hong, and H. Seo, “A data-driven fault detection and diagnosis scheme for air handling units in building HVAC systems considering undefined states,” J. Build. Eng., vol. 35, 2021.
• [10] N. A. Sulaiman, K. W. Chuink, M. N. S. Zainudin, A. M. Yusop, S. F. Sulaiman, and M. P. Abdullah, “Data-driven fault detection and diagnosis for centralised chilled water air conditioning system,” Prz. Elektrotechniczny, vol. 2022, no. 1, 2022.
• [11] G. Van Houdt, C. Mosquera, and G. Nápoles, “A review on the long short-term memory model,” Artif. Intell. Rev., vol. 53, no. 8, pp. 5929–5955, 2020.
• [12] K. Yan and J. Hua, “Deep learning technology for chiller faults diagnosis,” Proc. - IEEE 17th Int. Conf. Dependable, Auton. Secur. Comput. IEEE 17th Int. Conf. Pervasive Intell. Comput. IEEE 5th Int. Conf. Cloud Big Data Comput. 4th Cyber Sci., pp. 72–79, 2019.
• [13] P. Park, P. Di Marco, H. Shin, and J. Bang, “Fault detection and diagnosis using combined autoencoder and long short term memory network,” Sensors (Switzerland), vol. 19, no. 21, pp. 1–17, 2019.
• [14] M. Abboush, D. Bamal, C. Knieke, and A. Rausch, “Intelligent Fault Detection and Classification Based on Hybrid Deep Learning Methods for Hardware-in-the-Loop Test of Automotive Software Systems.,” Sensors (Basel)., vol. 22, no. 11, 2022.
• [15] P. E. Jebarani and N. Umadevi, “Grey Wolf Optimization Based Breast Cancer Detection using 1D Convolution LSTM Classifier,” Prz. Elektrotechniczny, vol. 99, no. 1, pp. 1–9, 2023.
• [16] N. A. Sulaiman, P. M. Abdullah, H. Abdullah, M. N. S. Zainudin, A. M. Y. Yusop, and S. F. Sulaiman, “Parameter selection in data-driven fault detection and diagnosis of the air conditioning system,” Indones. J. Electr. Eng. Comput. Sci., vol. 25, no. 1, pp. 59–67, 2022.
• [17] N. A. Sulaiman, P. Abdullah, H. Abdullah, M. N. S. Zainuddin, and A. Md Yusop, “Fault detection for air conditioning system using machine learning,” IAES Int. J. Artif. Intell., vol. 9, no. 1, pp. 109–116, 2020.
• [18] N. A. Sulaiman, M. F. Othman, and H. Abdullah, “Fuzzy logic control of centralized chilled water system,” J. Teknol., vol. 72, no. 2, pp. 57–62, 2015.
• [19] N. A. Sulaiman, M. F. Othman, and H. Abdullah, “Fuzzy logic control and fault detection in centralized chilled water system,” Proc. - 2015 IEEE Symp. Ser. Comput. Intell. SSCI 2015, pp. 8–13, 2015.
• [20] A. Burkov, The hundred-page machine learning book. True Positive Inc., 2020.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).