Preprocessing and Modeling of Radial Fan Data for Health State Prediction

Florian Christian Holzinger; Michael Kommenda

doi:10.1007/978-3-030-45093-9_38

Preprocessing and Modeling of Radial Fan Data for Health State Prediction

Florian Christian Holzinger, Michael Kommenda

Research Center Hagenberg

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution

Abstract

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.

Original language	English
Title of host publication	Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers
Editors	Roberto Moreno-Díaz, Alexis Quesada-Arencibia, Franz Pichler
Publisher	Springer
Pages	312-318
Number of pages	7
ISBN (Print)	9783030450922
DOIs	https://doi.org/10.1007/978-3-030-45093-9_38
Publication status	Published - 2020
Event	17th International Conference on Computer Aided Systems Theory, eurocast - Las Palmas, Gran Canaria, Spain Duration: 17 Apr 2019 → 22 Apr 2019 http://eurocast2019.fulp.ulpgc.es/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12013 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Conference on Computer Aided Systems Theory, eurocast
Country/Territory	Spain
City	Las Palmas, Gran Canaria
Period	17.04.2019 → 22.04.2019
Internet address	http://eurocast2019.fulp.ulpgc.es/

Keywords

Binning
Machine learning
Radial fan
Sampling

Access to Document

10.1007/978-3-030-45093-9_38

Cite this

Holzinger, F. C., & Kommenda, M. (2020). Preprocessing and Modeling of Radial Fan Data for Health State Prediction. In R. Moreno-Díaz, A. Quesada-Arencibia, & F. Pichler (Eds.), Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers (pp. 312-318). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12013 LNCS). Springer. https://doi.org/10.1007/978-3-030-45093-9_38

Holzinger, Florian Christian ; Kommenda, Michael. / Preprocessing and Modeling of Radial Fan Data for Health State Prediction. Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers. editor / Roberto Moreno-Díaz ; Alexis Quesada-Arencibia ; Franz Pichler. Springer, 2020. pp. 312-318 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "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.",

keywords = "Binning, Machine learning, Radial fan, Sampling",

author = "Holzinger, {Florian Christian} and Michael Kommenda",

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doi = "10.1007/978-3-030-45093-9_38",

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Holzinger, FC & Kommenda, M 2020, Preprocessing and Modeling of Radial Fan Data for Health State Prediction. in R Moreno-Díaz, A Quesada-Arencibia & F Pichler (eds), Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12013 LNCS, Springer, pp. 312-318, 17th International Conference on Computer Aided Systems Theory, eurocast, Las Palmas, Gran Canaria, Spain, 17.04.2019. https://doi.org/10.1007/978-3-030-45093-9_38

Preprocessing and Modeling of Radial Fan Data for Health State Prediction. / Holzinger, Florian Christian; Kommenda, Michael.
Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers. ed. / Roberto Moreno-Díaz; Alexis Quesada-Arencibia; Franz Pichler. Springer, 2020. p. 312-318 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12013 LNCS).

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution

TY - GEN

T1 - Preprocessing and Modeling of Radial Fan Data for Health State Prediction

AU - Holzinger, Florian Christian

AU - Kommenda, Michael

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Binning

KW - Machine learning

KW - Radial fan

KW - Sampling

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U2 - 10.1007/978-3-030-45093-9_38

DO - 10.1007/978-3-030-45093-9_38

M3 - Conference contribution

SN - 9783030450922

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 312

EP - 318

BT - Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers

A2 - Moreno-Díaz, Roberto

A2 - Quesada-Arencibia, Alexis

A2 - Pichler, Franz

PB - Springer

T2 - 17th International Conference on Computer Aided Systems Theory, eurocast

Y2 - 17 April 2019 through 22 April 2019

ER -

Holzinger FC, Kommenda M. Preprocessing and Modeling of Radial Fan Data for Health State Prediction. In Moreno-Díaz R, Quesada-Arencibia A, Pichler F, editors, Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers. Springer. 2020. p. 312-318. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-45093-9_38

Preprocessing and Modeling of Radial Fan Data for Health State Prediction

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Keywords

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