Real-world application of machine-learning-based fault detection trained with experimental data

Buildings are responsible for a large portion of the overall energy consumption. With the rising penetration of renewable energies, the heating and cooling demand of buildings will be increasingly satisfied by heat pumps. However, faults in the heat pump systems reduce energy efficiency or cause system failure, leading to an increased demand for primary energy. Hence, fault detection algorithms (FDA) are used to identify faults before system failure or efficiency deterioration occurs. With the rise of artificial intelligence and big data as well as more detailed monitoring systems, data-driven FDA have become a focus of research in recent years, showing promising results with acceptable effort. However, studies often use specific training data sets, thus generating FDAs adapted to a specific experimental system. In this paper, we investigate whether FDAs trained on a fault data set gathered with a laboratory heat pump system can be deployed on a real-world application system without the need for expensive modifications. We also investigate a big data approach, in which we use data collected over an extended period of time to train the FDAs. To this end, we use a data set kindly provided by the National Institute of Standards and Technology (NIST) containing data for typical heat pump failures measured on a specially outfitted air-water heat pump. From this data, we extract a series of features as input for the FDAs and evaluate the importance of those features for the FDAs. We train the algorithms to detect faults on the NIST data set, and transfer the fitted FDAs to our own measurement data. The results show that the trained FDAs perform very well on the NIST data set, but poorly on the real-world data set. We identify several reasons for the FDAs' poor performance and derive mitigating actions. We believe that big data approaches for FDAs are facing several issues beyond the simple gathering of large data quantities, especially the labelling of occurred faults and completeness of the data set. (C) 2020 Elsevier Ltd. All rights reserved.