Vol. 3 No. 2 (2026) • Articles
Open Access

Audit Retrospektif Kinerja Chiller Tanpa Bas: Validasi Data Ultrasonik Portabel Dan Deteksi Anomali Yang Dapat Dijelaskan

Aldi Cahya Muhammad
Universitas Dian Nusantara
Budiman R Saragih
Pusat Pengembangan Sumber Daya Manusia KEBTKE
Rinto Dwi Pambela
Universitas Dian Nusantara
Published: August 28, 2026 Pages: 64-82
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Abstract

Chillers in government buildings located in tropical climates often operate without a building automation system (BAS), making performance degradation difficult to detect through routine monitoring. This study reanalyzes the 2024 field measurement dataset for a 500 TR (R134a) water-cooled centrifugal chiller in a city government office without new data collection. The Isolation Forest model interpreted with permutation-based SHAP attribution marks instances of zero flow rate and surrounding reading changes, with residuals against local trends as the primary explanatory feature. Extended flatline segments following zero events are overlooked, complementing rather than replacing manual screening. The marking proportion ranges from 6.2% to 6.5% per session, adhering to a contamination parameter of 0.06. Pipe cross-section examinations restore stable diameters at three locations: 240 mm, 97.7 mm, and 450 mm, correcting a labeling error. Recalculated results show evaporator heat of 825.5 ± 79.7 kW and an actual COP of 3.75 ± 0.36 compared to a design COP of 5.76 at a measured load of 47.2% capacity. The energy balance deviation reaches 11.6%, exceeding the 5% threshold. This imbalance reflects combined uncertainty, with specific power at 0.94 kW/TR and a temperature difference of 1.3 °C, consistent with additional thermal resistance yet to be verified.

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Author Biographies
Aldi Cahya Muhammad Universitas Dian Nusantara
Program Studi Teknik Elektro, Fakultas Teknik, Universitas Dian Nusantara, Bekasi, Provinsi Jawa Barat, Indonesia.
Budiman R Saragih Pusat Pengembangan Sumber Daya Manusia KEBTKE

Pusat Pengembangan Sumber Daya Manusia KEBTKE, Daerah Khusus Ibukota Jakarta, Indonesia

Rinto Dwi Pambela Universitas Dian Nusantara

Program Studi Teknik Elektro, Fakultas Teknik, Universitas Dian Nusantara, Bekasi, Provinsi Jawa Barat, Indonesia.

How to Cite
Muhammad, A. C., Saragih, B. R., & Pambela, R. D. (2026). Audit Retrospektif Kinerja Chiller Tanpa Bas: Validasi Data Ultrasonik Portabel Dan Deteksi Anomali Yang Dapat Dijelaskan. Jurnal Ilmu Komputer Dan Teknologi Informasi, 3(2), 64-82. https://doi.org/10.63447/jikti.v3i2.2029
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References
Total: 30 References
  1. Aguilera, J. J., Meesenburg, W., Markussen, W. B., Poulsen, J. L., Zühlsdorf, B., & Elmegaard, B. (2023). Adaptive model-based monitoring of large-scale heat pump prone to evaporator fouling. 0565. https://doi.org/10.18462/IIR.ICR.2023.0565
  2. AHRI. (2023). AHRI Standard 550/590 (I-P): Performance rating of water-chilling and heat pump water-heating packages using the vapor compression cycle. Air-Conditioning, Heating, and Refrigeration Institute. https://www.thenbs.com/publicationindex/documents/details?Pub=AHRI&DocId=342937
  3. Almobarek, M. (2024). Most frequent chiller’s faults: An empirical predictive maintenance study. IEOM Society International, USA. https://doi.org/10.46254/NA09.20240193
  4. ASHRAE. (2016). ANSI/ASHRAE/IES Standard 90.1-2016: Energy standard for buildings except low-rise residential buildings. American Society of Heating, Refrigerating and Air-Conditioning Engineers. www.ashrae.org
  5. Badan Standardisasi Nasional. (2020). SNI 6390:2020 Konservasi energi sistem tata udara pada bangunan gedung (ICS 91.040.01). Badan Standardisasi Nasional. https://akses-sni.bsn.go.id/dokumen/2020/SNI%206390-2020/#p=1
  6. Beghi, A., Cecchinato, L., Peterle, F., Rampazzo, M., & Simmini, F. (2016). Model-based fault detection and diagnosis for centrifugal chillers. In Conference on Control and Fault-Tolerant Systems, SysTol (pp. 158–163). https://doi.org/10.1109/SYSTOL.2016.7739744
  7. Bi, J., Wang, H., Yan, E., Wang, C., Yan, K., Jiang, L., & Yang, B. (2024). AI in HVAC fault detection and diagnosis: A systematic review. Energy Reviews, 3(2), 100071. https://doi.org/10.1016/J.ENREV.2024.100071
  8. Chen, Z., Xiao, F., Guo, F., & Yan, J. (2023). Interpretable machine learning for building energy management: A state-of-the-art review. Advances in Applied Energy, 9, 100123. https://doi.org/10.1016/J.ADAPEN.2023.100123
  9. Emerson Electric Co. (2024). FleximTM FLUXUS G631 EE: Portable clamp-on ultrasonic flow and energy meter for gases and liquids [Product documentation]. Emerson. https://www.emerson.com/en/measurement-instrumentation/products/flexim-fluxus-g631-ee-portable-clamp-on-ultrasonic-flow-and-energy-meter
  10. Gao, Y., Miyata, S., & Akashi, Y. (2023). Automated fault detection and diagnosis of chiller water plants based on convolutional neural network and knowledge distillation. Building and Environment, 245, 110885. https://doi.org/10.1016/J.BUILDENV.2023.110885
  11. Guo, Y., Tian, Z., Wang, H., Chen, M., Chu, P., & Sheng, Y. (2025). Adaptive lift chiller units fault diagnosis model based on machine learning. PLOS ONE, 20(4), e0320563. https://doi.org/10.1371/JOURNAL.PONE.0320563
  12. Han, H., Ren, Z., Cui, X., & Gu, B. (2025). Variable-condition fault diagnosis for building chiller based on deep feature extraction and discrepancy minimization. Energy, 330, 136832. https://doi.org/10.1016/J.ENERGY.2025.136832
  13. Kaushik, K., & Naik, V. (2024). Detecting faults in the cooling systems by monitoring temperature and energy. Energy Informatics, 7(1), 46. https://doi.org/10.1186/S42162-024-00351-1
  14. Keyence Corporation. (2024). The smart guide to flow meters for building automation systems. Keyence. https://www.keyence.com/blog/the-smart-guide-to-flow-meters-for-building-automation-systems.jsp
  15. Liang, A., Hu, Y., & Li, G. (2023). The impact of improved PCA method based on anomaly detection on chiller sensor fault detection. International Journal of Refrigeration, 155, 184–194. https://doi.org/10.1016/J.IJREFRIG.2023.09.002
  16. Liu, F. T., Ting, K. M., & Zhou, Z. H. (2008). Isolation forest. In Proceedings - IEEE International Conference on Data Mining, ICDM (pp. 413–422). https://doi.org/10.1109/ICDM.2008.17
  17. Lu, C., Ma, X., & Yan, K. (2024). Chiller fault diagnosis based on improved variational autoencoder and co-training framework: A case study of insufficient samples. Journal of Building Engineering, 88, 109137. https://doi.org/10.1016/J.JOBE.2024.109137
  18. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 2017-December, 4766–4775. https://arxiv.org/pdf/1705.07874
  19. Marbun, R., Ardiansyah, T., & Nofirman, N. (2025). Performance evaluation and optimization of chiller systems: A data-driven approach to enhancing energy efficiency. SINTEK JURNAL: Jurnal Ilmiah Teknik Mesin, 19(1), 11–24. https://doi.org/10.24853/SINTEK.19.1.11-24
  20. Mauro, A. W., Pelella, F., & Viscito, L. (2023). Performance degradation of air source heat pumps under faulty conditions. Case Studies in Thermal Engineering, 45, 103010. https://doi.org/10.1016/J.CSITE.2023.103010
  21. Meas, M., Machlev, R., Kose, A., Tepljakov, A., Loo, L., Levron, Y., Petlenkov, E., & Belikov, J. (2022). Explainability and transparency of classifiers for air-handling unit faults using explainable artificial intelligence (XAI). Sensors, 22(17), 6338. https://doi.org/10.3390/S22176338
  22. Parsaei, A., Gunay, B., O’Brien, W., & Moromisato, R. (2024). Unsupervised identification of zone-level anomalies in VAV terminal units utilizing autoencoders and PCA. Science and Technology for the Built Environment, 30(10), 1196–1216. https://doi.org/10.1080/23744731.2024.2411161
  23. Ren, Z., Han, H., Cui, X., Lu, H., & Luo, M. (2023). Novel data-pulling-based strategy for chiller fault diagnosis in data-scarce scenarios. Energy, 279, 128019. https://doi.org/10.1016/J.ENERGY.2023.128019
  24. Sharma, V., & Unune, D. (2024). Enhancing reliability and efficiency: An ASHRAE Level 2 energy audit of a regional hospital. https://doi.org/10.20944/preprints202407.1664.v1
  25. Srinivasan, S., Arjunan, P., Jin, B., Sangiovanni-Vincentelli, A. L., Sultan, Z., & Poolla, K. (2021). Explainable AI for chiller fault-detection systems: Gaining human trust. Computer, 54(10), 60–68. https://doi.org/10.1109/MC.2021.3071551
  26. Ssembatya, M., & Claridge, D. E. (2024). Quantitative fault detection and diagnosis methods for vapour compression chillers: Exploring the potential for field-implementation. Renewable and Sustainable Energy Reviews, 197, 114418. https://doi.org/10.1016/J.RSER.2024.114418
  27. Wang, Z., Guo, J., Xia, P., Wang, L., Zhang, C., Leng, Q., & Zheng, K. (2024). Feature selection for chillers fault diagnosis from the perspectives of machine learning and field application. Energy and Buildings, 307, 113937. https://doi.org/10.1016/J.ENBUILD.2024.113937
  28. Wang, Z., Xia, P., Guo, J., Zhou, S., Wang, L., Wang, Y., & Zhang, C. (2025). Efficient feature selection for enhanced chiller fault diagnosis: A multi-source ranking information-driven ensemble approach. Building Simulation, 18(1), 141–159. https://doi.org/10.1007/S12273-024-1199-1/METRICS
  29. Yan, K. (2021). Chiller fault detection and diagnosis with anomaly detective generative adversarial network. Building and Environment, 201, 107982. https://doi.org/10.1016/J.BUILDENV.2021.107982
  30. Yan, K., & Zhou, X. (2022). Chiller faults detection and diagnosis with sensor network and adaptive 1D CNN. Digital Communications and Networks, 8(4), 531–539. https://doi.org/10.1016/J.DCAN.2022.03.023