2019 International IEEE Conference and Workshop in Óbuda on Electrical and Power Engineering (CANDO-EPE) 2019

DOI: 10.1109/cando-epe47959.2019.9111046

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A data-driven fault diagnosis approach towards oil retention in vapour compression refrigeration systems

Ron van de Sand

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,

Constantin Falk

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,

Sandra Corasaniti

³

et al.

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Cited by 4 publications

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“…This leads to the assumption of a general lag of appropiate datasets aswell as the evaluation of algorithms of data other than the ASHRAE dataset. Therefore this study compares a selection of models based on two datasets: on the one hand, the data from the ASHRAE study 1043-RP by Comstock et al [17] and on the other hand with data from a previous project presented in [18]. In order to select some of the presented approaches, it is necessary to define selection criteria.…”

Section: Related Workmentioning

confidence: 99%

“…For appropriately training data-driven anomaly detection models, representative datasets are necessary. As data-basis serves the datasets from the ASHRAE study 1043-RP [21] and from a project of the Technical University of Applied Science Wildau [18]. Both datasets were arranged to study the impact of different fault types at multiple severity levels (SL).…”

Section: Datasetsmentioning

confidence: 99%

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

¹

,

²

,

³

et al. 2021

TH Wildau Eng. Nat. Sci. Proc.

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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.

“…This leads to the assumption of a general lag of appropiate datasets aswell as the evaluation of algorithms of data other than the ASHRAE dataset. Therefore this study compares a selection of models based on two datasets: on the one hand, the data from the ASHRAE study 1043-RP by Comstock et al [17] and on the other hand with data from a previous project presented in [18]. In order to select some of the presented approaches, it is necessary to define selection criteria.…”

Section: Related Workmentioning

confidence: 99%

“…For appropriately training data-driven anomaly detection models, representative datasets are necessary. As data-basis serves the datasets from the ASHRAE study 1043-RP [21] and from a project of the Technical University of Applied Science Wildau [18]. Both datasets were arranged to study the impact of different fault types at multiple severity levels (SL).…”

Section: Datasetsmentioning

confidence: 99%

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

¹

,

²

,

³

et al. 2021

TH Wildau Eng. Nat. Sci. Proc.

Self Cite

View full text Add to dashboard Cite

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.

Data-driven fault diagnosis for heterogeneous chillers using domain adaptation techniques

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,

²

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³

2021

Control Engineering Practice

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Positive-Unlabelled Learning based Novelty Detection for Industrial Chillers

¹

,

²

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³

2021

TH Wildau Eng. Nat. Sci. Proc.

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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.

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