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ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
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PreDist Dataset - Operational data of district heating substations labelled with faults and maintenance information

Authors: Roelofs, Cyriana; Bastidas Guevara, Edison; Hugo, Thomas; Faulstich, Stefan; Cadenbach (neé Kallert), Anna;

PreDist Dataset - Operational data of district heating substations labelled with faults and maintenance information

Abstract

This dataset consists of operational data and labels based on incident reports and maintenance data of district heating substations of enercity Netz GmbH. The labels are available as a list of ‘disturbances’ as well as a list of fault reports including a short description and problem category, which can be used to develop (early) fault detection models for district heating substations. In addition, fault labels and monitoring potential were added to the reports where possible. The dataset is published together with the paper "Enabling Predictive Maintenance in District Heating Substations: A Labelled Dataset and Fault Detection Evaluation Framework based on Service Data", which explains the dataset in detail. When referring to this dataset, please cite the paper mentioned in the related work section. The PreDist dataset contains time series of 93 district heating substations from two manufacturers, M1 and M2, each time series spanning different lengths of time, depending on when the substation was ‘digitised’. Both sub-datasets contain a list of faults (based on incident reports), a list of disturbances (incident reports, and corrective and preventive maintenance tasks and activities), feature descriptions and a list of pre-defined ‘normal events’, which can be used in addition to the faults to evaluate normal behaviour models.

Keywords

predictive maintenance, District heating, condition monitoring, District heating substation, Anomaly detection, PreDist, Fault detection

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average