Preprocessing and Modeling of Radial Fan Data for Health State Prediction

Florian Christian Holzinger, Michael Kommenda

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitrag


Monitoring critical components of systems is a crucial step towards failure safety. Affordable sensors are available and the industry is in the process of introducing and extending monitoring solutions to improve product quality. Often, no expertise of how much data is required for a certain task (e.g. monitoring) exists. Especially in vital machinery, a trend to exaggerated sensors may be noticed, both in quality and in quantity. This often results in an excessive generation of data, which should be transferred, processed and stored nonetheless. In a previous case study, several sensors have been mounted on a healthy radial fan, which was later artificially damaged. The gathered data was used for modeling (and therefore monitoring) a healthy state. The models were evaluated on a dataset created by using a faulty impeller. This paper focuses on the reduction of this data through downsampling and binning. Different models are created with linear regression and random forest regression and the resulting difference in quality is discussed.

TitelComputer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers
Redakteure/-innenRoberto Moreno-Díaz, Alexis Quesada-Arencibia, Franz Pichler
Herausgeber (Verlag)Springer
ISBN (Print)9783030450922
PublikationsstatusVeröffentlicht - 2020
Veranstaltung17th International Conference on Computer Aided Systems Theory, eurocast - Las Palmas, Gran Canaria, Spanien
Dauer: 17 Apr. 201922 Apr. 2019


NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band12013 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349


Konferenz17th International Conference on Computer Aided Systems Theory, eurocast
OrtLas Palmas, Gran Canaria


Untersuchen Sie die Forschungsthemen von „Preprocessing and Modeling of Radial Fan Data for Health State Prediction“. Zusammen bilden sie einen einzigartigen Fingerprint.