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

Chair of Mineral Processing (180)

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

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.

Original language	English
Title of host publication	VORTRÄGE-Konferenzband
Subtitle of host publication	zur 17. Recy & DepoTech-Konferenz
Place of Publication	Leoben
Pages	529-534
Number of pages	6
Publication status	Published - 13 Nov 2024
Event	Recy & Depotech 2024: Abfallwirtschafts- und Recyclingkonferenz - Montanuniversität Leoben, Leoben, Austria Duration: 13 Nov 2024 → 15 Nov 2024 https://www.recydepotech.at https://www.recydepotech.at/de/1/

Conference

Conference	Recy & Depotech 2024
Country/Territory	Austria
City	Leoben
Period	13/11/24 → 15/11/24
Internet address	https://www.recydepotech.at https://www.recydepotech.at/de/1/

Keywords

Vibration Screens
Vibration monitoring
Condition monitoring
Machine learning

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

1 Article

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 Oct 2024, In: IEEE sensors journal. 24.2024, 22, p. 38232-38243 12 p.
Research output: Contribution to journal › Article › Research › peer-review

Open Access

Cite this

@inproceedings{259efe846a9d454aa84a738634f08a47,

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

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.",

keywords = "Schwingsiebe, Schwingungs{\"u}berwachung, Zustands{\"u}berwachung, Machine Learning, Vibration Screens, Vibration monitoring, Condition monitoring, Machine learning",

author = "Philip Krukenfellner and Helmut Flachberger",

year = "2024",

month = nov,

day = "13",

language = "English",

isbn = "978-3-200-09925-8",

pages = "529--534",

booktitle = "VORTR{\"A}GE-Konferenzband",

note = "Recy & Depotech 2024 ; Conference date: 13-11-2024 Through 15-11-2024",

url = "https://www.recydepotech.at, https://www.recydepotech.at/de/1/",

}

TY - GEN

T1 - I-STEP – A Case Study: Machine Learning powered Condition Monitoring of a Linear Motion Industrial Vibrating Screen

AU - Krukenfellner, Philip

AU - Flachberger, Helmut

PY - 2024/11/13

Y1 - 2024/11/13

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

SN - 978-3-200-09925-8

SP - 529

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

Conference

Keywords

UN SDGs

Research output

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

Cite this