@inproceedings{vandeSandCorasanitiReiffStephan2020, author = {van de Sand, Ron and Corasaniti, Sandra and Reiff-Stephan, J{\"o}rg}, title = {Review of Condition Based Maintenance approaches for vapor compression refrigeration systems}, series = {Conference Proceedings : 1st German-West African Conference on Sustainable, Renewable Energy Systems SusRes : 1st July 2020 - Kara, Togo}, booktitle = {Conference Proceedings : 1st German-West African Conference on Sustainable, Renewable Energy Systems SusRes : 1st July 2020 - Kara, Togo}, doi = {10.15771/978-3-9819225-5-4_SI-2a}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-13369}, pages = {24 -- 33}, year = {2020}, abstract = {Vapor compression refrigeration systems are subject to performance degradation over time due to the presence of faults. However, latest work in the field of condition-based maintenance shows promising results in the automatic early detection of anomalous behaviour as well as in accurate machine diagnostics and can, therefore, increase the overall system reliability by simultaneously preventing machine downtimes. In this paper, the latest research works carried out within the last decade are reviewed and the approaches are classified regarding their working principles. Furthermore, the work at hand depicts the current research trend in this field and outlines current obstacles.}, language = {en} } @inproceedings{FalkvandeSandCorasanitietal.2021, author = {Falk, Constantin and van de Sand, Ron and Corasaniti, Sandra and Reiff-Stephan, J{\"o}rg}, title = {A Comparison Study of Data-Driven Anomaly Detection Approaches for Industrial Chillers}, series = {2nd German-West African Conference on Sustainable, Renewable Energy Systems (SusRES2021)}, booktitle = {2nd German-West African Conference on Sustainable, Renewable Energy Systems (SusRES2021)}, publisher = {TIB Open Publishing}, address = {Hannover}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-14970}, pages = {165 -- 174}, year = {2021}, 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.}, language = {en} } @inproceedings{vandeSandCorasanitiReiffStephan2021, author = {van de Sand, Ron and Corasaniti, Sandra and Reiff-Stephan, J{\"o}rg}, title = {Positive-Unlabelled Learning based Novelty Detection for Industrial Chillers}, series = {2nd German-West African Conference on Sustainable, Renewable Energy Systems (SusRES2021)}, volume = {1}, booktitle = {2nd German-West African Conference on Sustainable, Renewable Energy Systems (SusRES2021)}, publisher = {TIB Open Publishing}, address = {Hannover}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-14965}, pages = {155 -- 164}, year = {2021}, abstract = {Chiller systems are used in many different applications in both the industrial and the commercial sector. They are considered major energy consumers and thus contribute a non-negligible factor to environmental pollution as well as to the overall operating cost. In addition, chillers, especially in industrial applications, are often associated with high reliability requirements, as unplanned system downtimes are usually costly. As many studies over the past decades have shown, the presence of faults can lead to significant performance degradation and thus higher energy consumption of these systems. Thus, data-driven fault detection plays an ever-increasing role in terms of energy efficient control strategies. However, labelled data to train associated algorithms are often only available to a limited extent, which consequently inhibits the broad application of such technologies. Therefore, this paper presents an approach that exploits only a small amount of labelled and large amounts of unlabelled data in the training phase in order to detect fault related anomalies. For this, the model utilizes the residual space of the data transformed through principal component analyses in conjunction with a biased support vector machine, which can be ascribed to the concept of semi-supervised learning, or more specifically, positive-unlabelled learning.}, language = {en} }