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Data Reliability Analysis for Early Fault Diagnosis of Air Handling Unit (AHU)

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Renewable Power for Sustainable Growth (ICRP 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1086))

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Abstract

The objective of this chapter is to analyze the reliability of the dataset for early fault diagnosis of air handling unit (AHU) of air conditioning and mechanical ventilation/heating, ventilation, and air conditioning (ACMV/HVAC) system. In this chapter, data reliability analysis for early fault diagnosis is performed for AHU only, which includes fan degradation and return air duct leakage fault conditions. Data of the said faults are generated through the use of an expert system platform, OpenStudio (OS) and sensitivity analysis is performed to identify the most sensitive parameters with respect to the fault severity level starting from zero to 30% of deviation from healthy condition. The most sensitive parameters are selected based on the rank of sensitivity analysis. A similar procedure was performed with real data obtained from measurement. The effect of parameters due to fault conditions in AHU is analyzed in terms of consistency of increasing, decreasing, both increasing and decreasing, and no correlation. The results of the analyzed data are documented and compared.

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Acknowledgements

This study was supported by the Universiti Teknologi, Malaysia—“Development of Adaptive and Predictive ACMV/HVAC Health Monitoring System Using IoT, Advanced FDD, and Weather Forecast Algorithms” (Q.J130000.3823.31J06).

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Authors and Affiliations

  1. Department of Electrical Power Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia

    Hasmat Malik, Shahrin Md Ayob, Nik Rumzi Nik Idris & Awang Jusoh

  2. Ingenium Research Group, Universidad Castilla-La Mancha, 13071, Ciudad Real, Spain

    Fausto Pedro García Márquez

  3. Department of Electrical Engineering, College of Engineering, Majmaah University, Al Majma’ah, 11952, Saudi Arabia

    Abdulaziz Almutairi

Authors
  1. Hasmat Malik
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  2. Shahrin Md Ayob
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  3. Nik Rumzi Nik Idris
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  4. Awang Jusoh
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  5. Fausto Pedro García Márquez
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  6. Abdulaziz Almutairi
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Correspondence to Hasmat Malik .

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Editors and Affiliations

  1. Department of Electrical Power Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia

    Hasmat Malik

  2. Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India

    Sukumar Mishra

  3. Department of Electrical Engineering, National Institute of Technology Hamirpur, Hamirpur, Himachal Pradesh, India

    Y. R. Sood

  4. Department of Electrical Engineering, Qatar University, Doha, Qatar

    Atif Iqbal

  5. Advanced Industrial Science and Technology (FREA), Fukushima Renewable Energy Institute, Koriyama, Japan

    Taha Selim Ustun

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Malik, H., Ayob, S.M., Idris, N.R.N., Jusoh, A., Márquez, F.P.G., Almutairi, A. (2024). Data Reliability Analysis for Early Fault Diagnosis of Air Handling Unit (AHU). In: Malik, H., Mishra, S., Sood, Y.R., Iqbal, A., Ustun, T.S. (eds) Renewable Power for Sustainable Growth. ICRP 2023. Lecture Notes in Electrical Engineering, vol 1086. Springer, Singapore. https://doi.org/10.1007/978-981-99-6749-0_43

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  • DOI: https://doi.org/10.1007/978-981-99-6749-0_43

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  • Print ISBN: 978-981-99-6748-3

  • Online ISBN: 978-981-99-6749-0

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