I-STEP – A Case Study: Machine Learning powered Condition Monitoring of a Linear Motion Industrial Vibrating Screen

Philip Krukenfellner; Helmut Flachberger

I-STEP – A Case Study: Machine Learning powered Condition Monitoring of a Linear Motion Industrial Vibrating Screen

Philip Krukenfellner
, Helmut Flachberger

Lehrstuhl für Aufbereitung und Veredlung (180)

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in Konferenzband

Abstract

Vibrating Screens, crucial in mineral and waste processing industries, usually lack adequate condition monitoring to assess condition states or predict machine errors. Addressing this issue, IFE Aufbereitungstechnik GmbH and its partners are developing "i-STEP," a digitalization solution potentially integrating any market-available sensor for a customizable, plant-wide monitoring platform. Thus far, a vibration sensor, "SES" has been developed to specifically measure oscillation patterns of vibrating screens, which is the main focus of this research.

Originalsprache	Englisch
Titel	VORTRÄGE-Konferenzband
Untertitel	zur 17. Recy & DepoTech-Konferenz
Erscheinungsort	Leoben
Seiten	529-534
Seitenumfang	6
Publikationsstatus	Veröffentlicht - 13 Nov. 2024
Veranstaltung	Recy & Depotech 2024: Abfallwirtschafts- und Recyclingkonferenz - Montanuniversität Leoben, Leoben, Österreich Dauer: 13 Nov. 2024 → 15 Nov. 2024 https://www.recydepotech.at https://www.recydepotech.at/de/1/

Konferenz

Konferenz	Recy & Depotech 2024
Land/Gebiet	Österreich
Ort	Leoben
Zeitraum	13/11/24 → 15/11/24
Internetadresse	https://www.recydepotech.at https://www.recydepotech.at/de/1/

UN SDGs

Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung

SDG 9 – Industrie, Innovation und Infrastruktur

Schlagwörter

Schwingsiebe
Schwingungsüberwachung
Zustandsüberwachung
Machine Learning

1 Artikel

Predicting condition states, based on displacement data, generated by acceleration sensors on industrial linear vibrating screens through neural networks
Krukenfellner, P., Rückert, E. & Flachberger, H., 4 Okt. 2024, in: IEEE sensors journal. 24, 22, S. 38232-38243 12 S.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Begutachtung

Open Access

1 Mitwirkung als Gastredner/-in

Erkenntnisse aus der digitalen Überwachung von Schwingsieben mittels smarter Beschleunigungssensoren vom Typ SES der Firma IFE Aufbereitungstechnik GmbH
Krukenfellner, P. (Redner) & Flachberger, H. (Beitragende/r)
30 Jan. 2025
Aktivität: Gespräch oder Vortrag › Mitwirkung als Gastredner/-in

Dieses zitieren

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AU - Krukenfellner, Philip

AU - Flachberger, Helmut

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N2 - Vibrating Screens, crucial in mineral and waste processing industries, usually lack adequate condition monitoring to assess condition states or predict machine errors. Addressing this issue, IFE Aufbereitungstechnik GmbH and its partners are developing "i-STEP," a digitalization solution potentially integrating any market-available sensor for a customizable, plant-wide monitoring platform. Thus far, a vibration sensor, "SES" has been developed to specifically measure oscillation patterns of vibrating screens, which is the main focus of this research.

AB - Vibrating Screens, crucial in mineral and waste processing industries, usually lack adequate condition monitoring to assess condition states or predict machine errors. Addressing this issue, IFE Aufbereitungstechnik GmbH and its partners are developing "i-STEP," a digitalization solution potentially integrating any market-available sensor for a customizable, plant-wide monitoring platform. Thus far, a vibration sensor, "SES" has been developed to specifically measure oscillation patterns of vibrating screens, which is the main focus of this research.

KW - Schwingsiebe

KW - Schwingungsüberwachung

KW - Zustandsüberwachung

KW - Machine Learning

KW - Vibration Screens

KW - Vibration monitoring

KW - Condition monitoring

KW - Machine learning

M3 - Conference contribution

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EP - 534

BT - VORTRÄGE-Konferenzband

CY - Leoben

T2 - Recy & Depotech 2024

Y2 - 13 November 2024 through 15 November 2024

ER -

I-STEP – A Case Study: Machine Learning powered Condition Monitoring of a Linear Motion Industrial Vibrating Screen

Abstract

Konferenz

UN SDGs

Schlagwörter

Publikationen

Predicting condition states, based on displacement data, generated by acceleration sensors on industrial linear vibrating screens through neural networks

Aktivitäten

Erkenntnisse aus der digitalen Überwachung von Schwingsieben mittels smarter Beschleunigungssensoren vom Typ SES der Firma IFE Aufbereitungstechnik GmbH

Dieses zitieren