TY - GEN A1 - van de Sand, Ron A1 - Reiff-Stephan, Jörg T1 - FrostByte Dataset N2 - It is with great pleasure that we announce the release of the “Frost Byte” dataset. The dataset was collected during research on fault detection and diagnosis (FDD) approaches and their transferability to heterogeneous systems of industrial refrigeration systems at the Technical University of Applied Sciences Wildau. The data collection took place between 2018 and 2019 and is intended to supplement the ASHRAE 1043-RP dataset (Comstock & Braun, 1999), which has been the only publicly available data source in this area to date. For comparison purposes, the method of data collection was chosen similarly, whereby this dataset collection was carried out using a ~ 100 kW refrigeration capacity ammonia system with plate heat exchangers. The dataset contains steady-state data equally sampled from five classes: 1. Normal (fault-free operating condition) 2. Reduced Condenser Water Flow 3. Reduced Evaporator Water Flow 4. Non-Condensable Gases 5. Refrigeration Leak Each class was investigated under changing operational conditions and varying fault severity levels (SL). KW - predictive maintenance KW - FDD KW - machine learning Y1 - 2021 U6 - https://doi.org/10.15771/1894 N1 - Related dissertation: van de Sand, R. (2021). A Predictive maintenance model for heterogeneous industrial refrigeration systems. https://hdl.handle.net/2108/323763 Related journal article: van de Sand, R., Corasaniti, S., & Reiff-Stephan, J. (2021). Data-driven fault diagnosis for heterogeneous chillers using domain adaptation techniques. Control Engineering Practice, 112, 104815. https://doi.org/10.1016/j.conengprac.2021.104815 ER -