Audit Retrospektif Kinerja Chiller Tanpa Bas: Validasi Data Ultrasonik Portabel Dan Deteksi Anomali Yang Dapat Dijelaskan
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Aug 28, 2026
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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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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
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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
Almobarek, M. (2024). Most frequent chiller’s faults: An empirical predictive maintenance study. IEOM Society International, USA. https://doi.org/10.46254/NA09.20240193
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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