A Comparison Study of Data-Driven Anomaly Detection Approaches for Industrial Chillers


  • Constantin Falk Technical University of Applied Sciences Wildau image/svg+xml
  • Ron van de Sand Technical University of Applied Sciences Wildau image/svg+xml
  • Sandra Corasaniti University of Rome Tor Vergata image/svg+xml
  • Jörg Reiff-Stephan Technical University of Applied Sciences Wildau image/svg+xml




Fault Detection, Refrigeration System, Data-Driven Machine-Learning


Faults in industrial chiller systems can lead to higher energy consumption, increasing wear of system components and shorten equipment life. While they gradually cause anomalous system operating conditions, modern automatic fault detection models aim to detect them at low severity by using real-time sensor data. Many scientific contributions addressed this topic in the past and presented data-driven approaches to detect faulty system states. Although many promising results were presented to date, there is lack of suitable comparison studies that show the effectiveness of the proposed models by use of data stemming from different chiller systems. Therefore this study aims at detecting a suitable data-driven approach to detect faults reliable in different domains of industrial chillers. Thus, a unified procedure is developed, to train all algorithms in an identical way with same data-basis. Since most of the reviewed papers used only one dataset for training and testing, the selected approaches are trained and validated on two different datasets from real refrigeration systems. The data-driven approaches are evaluated based on their accuracy and true negative rate, from which the most suitable approach is derived as a conclusion.


Download data is not yet available.


VDMA e.V. Allgemeine Lufttechnik, Energiebedarf für Kälteltetechnik in Deutschland: Eine Abschätzung des Energiebedarfs von Kältetechnik in Deutschland nach Einsatzgebieten, 2017, VDMA, Ed., Frankfurt (a.M.)

Y.-h. Song, Y. Akashi, and J.-J. Yee, “A development of easy-to-use tool for fault detection and diagnosis in building air-conditioning systems,” Energy and Buildings, vol. 40, no. 2, pp. 71–82, 2008.

M. A. Piette, S. K. Kinney, and P. Haves, “Analysis of an information monitoring and diagnostic system to improve building operations,” Energy and Buildings, vol. 33, no. 8, pp. 783–791, 2001.

A. Beghi, R. Brignoli, L. Cecchinato, G. Menegazzo, M. Rampazzo, and F. Simmini, “Data-driven fault detection and diagnosis for hvac water chillers,” Control Engineering Practice, vol. 53, pp. 79–91, 2016.

G. Li, Y. Hu, H. Chen, L. Shen, H. Li, M. Hu, J. Liu, and K. Sun, “An improved faultdetection method for incipient centrifugal chiller faults using the pca-r-svdd algorithm,” Energy and Buildings, vol. 116, pp. 104–113, 2016.

D. Li, G. Hu, and C. J. Spanos, “A data-driven strategy for detection and diagnosis of building chiller faults using linear discriminant analysis,” Energy and Buildings, vol. 128, pp. 519–529, 2016.

K. Yan, Z. Ji, and W. Shen, “Online fault detection methods for chillers combining extended kalman filter and recursive one-class svm,” Neurocomputing, vol. 228, pp. 205–212, 2017.

H. Han, Z. Cao, B. Gu, and N. Ren, “Pca-svm-based automated fault detection and diagnosis (afdd) for vapor-compression refrigeration systems,” HVAC&R Research, vol. 16, no. 3, pp. 295–313, 2010.

Y. Zhao, S. Wang, and F. Xiao, “Pattern recognition-based chillers fault detection method using support vector data description (svdd),” Applied Energy, vol. 112, pp. 041–1048, 2013.

Q. Jiang and X. Yan, “Just-in-time reorganized pca integrated with svdd for chemical process monitoring,” AIChE Journal, vol. 60, no. 3, pp. 949–965, 2014.

X. Liu, K. Li, M. McAfee, and G. W. Irwin, “Improved nonlinear pca for process monitoring using support vector data description,” Journal of Process Control, vol. 21, no. 9, pp. 1306–1317, 2011.

B. Sch¨ olkopf, R. C. Williamson, A. J. Smola, J. Shawe-Taylor, J. C. Platt, et al., “Support vector method for novelty detection,” in NIPS, vol. 12, 1999, pp. 582–588.

A. Beghi, L. Cecchinato, C. Corazzol, M. Rampazzo, F. Simmini, and G. A. Susto, “A one-class svm based tool for machine learning novelty detection in hvac chiller systems,” FAC Proceedings Volumes, vol. 47, no. 3, pp. 1953–1958, 2014.

H. Han, B. Gu, J. Kang, and Z. R. Li, “Study on a hybrid svm model for chiller fdd applications,” Applied Thermal Engineering, vol. 31, no. 4, pp. 582–592, 2011.

K. Yan, W. Shen, T. Mulumba, and A. Afshari, “Arx model based fault detection and diagnosis for chillers using support vector machines,” Energy and Buildings, vol. 81, pp. 287–295, 2014.

Z. Du, B. Fan, X. Jin, and J. Chi, “Fault detection and diagnosis for buildings and hvac systems using combined neural networks and subtractive clustering analysis,” Building and Environment, vol. 73, pp. 1–11, 2014.

M. C. Comstock, J. E. Braun, and R. Bernhard, Development of analysis tools for the evaluation of fault detection and diagnostics in chillers: Sponsored by ASHRAE Deliverable for Research Projekt 1043-RP Fault Detection and Diagnostics (FDD) Requirements and Evaluation Tools for Chillers. Purdue University, 1999.

R. van de Sand, C. Falk, S. Corasaniti, and J. Reiff-Stephan, “A data-driven fault diagnosis approach towards oil retention in vapour compression refrigeration systems,” in 2019 International IEEE Conference and Workshop in O´ buda on Electrical and Power Engineering (CANDO-EPE), IEEE, 20.11.2019 - 21.11.2019, pp. 197–202.

T. Hastie, R. Tibshirani, and J. H. Friedman, The elements of statistical learning: Data mining, inference, and prediction, Second edition, corrected at 12th printing 2017,

R. van de Sand, S. Corasaniti, and J. Reiff-Stephan, Review of condition based maintenance approaches for vapor compression refrigeration systems, 2020.

M.C. Comstock, J.E. Braun, and R. Bernhard, Experimental data from fault detection and diagnostic studies on a centrifugal chiller, 1999.