Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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Materials Map under construction

The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (1/1 displayed)

  • 2024Fault Detection in AHU: A Walkthrough for Implementation in a Danish Educational Buildingcitations

Places of action

Chart of shared publication
Heiselberg, Per Kvols
1 / 2 shared
Melgaard, Simon Pommerencke
1 / 2 shared
Ferreira, Pedro Miguel
1 / 1 shared
Jensen, Rasmus Lund
1 / 3 shared
Andersen, Kamilla Heimar
1 / 2 shared
Cogo, Vinicius Vielmo
1 / 1 shared
Chart of publication period
2024

Co-Authors (by relevance)

  • Heiselberg, Per Kvols
  • Melgaard, Simon Pommerencke
  • Ferreira, Pedro Miguel
  • Jensen, Rasmus Lund
  • Andersen, Kamilla Heimar
  • Cogo, Vinicius Vielmo
OrganizationsLocationPeople

document

Fault Detection in AHU: A Walkthrough for Implementation in a Danish Educational Building

  • Heiselberg, Per Kvols
  • Melgaard, Simon Pommerencke
  • Ferreira, Pedro Miguel
  • Jensen, Rasmus Lund
  • Andersen, Kamilla Heimar
  • Dionisio, Nuno
  • Cogo, Vinicius Vielmo
Abstract

The implementation of fault detection in research articles is relatively sparse, yet it holds significant potential to contribute to the decarbonization of our building stock. This study proposes a Fault Detection (FD) methodology that can be split into three stages. 1) FD algorithm development,testing, and validation on known datasets. 2) platform creation, data collection and curation, and method implementation. 3) FD algorithm testing on an operational system running under normal conditions with no artificially induced faults. The results showed that for step 1, using suitable evaluation metrics for realistic datasets is highly important, as otherwise wrong conclusions can be drawn. It was further found that the proposed FD methodology, as intended, led to choosing an algorithm that did not cause many false alarms, as the emphasis was on avoiding these, but also that changing the weighting of the included terms could shift the focus to prioritize other issues.

Topics
  • impedance spectroscopy