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

Authors

  • 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

DOI:

https://doi.org/10.52825/thwildauensp.v1i.33

Keywords:

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

Abstract

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.

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Published

2021-06-15