Semi-Supervised Classification of the State of Operation in Self-Lubricating Journal Bearings Using a Random Forest Classifier

Prost, Josef; Cihak-Bayr, Ulrike; Neacșu, Ioana Adina; Grundtner, Reinhard; Pirker, Franz; Vorlaufer, Georg

doi:10.3390/lubricants9050050

Open AccessArticle

Semi-Supervised Classification of the State of Operation in Self-Lubricating Journal Bearings Using a Random Forest Classifier

by

Josef Prost

^*

,

Ulrike Cihak-Bayr

,

Ioana Adina Neacșu

,

Reinhard Grundtner

,

Franz Pirker

and

Georg Vorlaufer

AC2T research GmbH, Viktor-Kaplan-Straße 2/C, 2700 Wiener Neustadt, Austria

^*

Author to whom correspondence should be addressed.

Lubricants 2021, 9(5), 50; https://doi.org/10.3390/lubricants9050050

Submission received: 26 March 2021 / Revised: 30 April 2021 / Accepted: 30 April 2021 / Published: 4 May 2021

(This article belongs to the Special Issue Machine Learning in Tribology)

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Abstract

For a tribological experiment involving a steel shaft sliding in a self-lubricating bronze bearing, a semi-supervised machine learning method for the classification of the state of operation is proposed. During the translatory oscillating motion, the system may undergo different states of operation from normal to critical, showing self-recovering behaviour. A Random Forest classifier was trained on individual cycles from the lateral force data from four distinct experimental runs in order to distinguish between four states of operation. The labelling of the individual cycles proved to be crucial for a high prediction accuracy of the trained RF classifier. The proposed semi-supervised approach allows choosing within a range between automatically generated labels and full manual labelling by an expert user. The algorithm was at the current state used for ex post classification of the state of operation. Considering the results from the ex post analysis and providing a sufficiently sized training dataset, online classification of the state of operation of a system will be possible. This will allow taking active countermeasures to stabilise the system or to terminate the experiment before major damage occurs.

Keywords: condition monitoring; semi-supervised learning; random forest classifier; self-lubricating journal bearings

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MDPI and ACS Style

Prost, J.; Cihak-Bayr, U.; Neacșu, I.A.; Grundtner, R.; Pirker, F.; Vorlaufer, G. Semi-Supervised Classification of the State of Operation in Self-Lubricating Journal Bearings Using a Random Forest Classifier. Lubricants 2021, 9, 50. https://doi.org/10.3390/lubricants9050050

AMA Style

Prost J, Cihak-Bayr U, Neacșu IA, Grundtner R, Pirker F, Vorlaufer G. Semi-Supervised Classification of the State of Operation in Self-Lubricating Journal Bearings Using a Random Forest Classifier. Lubricants. 2021; 9(5):50. https://doi.org/10.3390/lubricants9050050

Chicago/Turabian Style

Prost, Josef, Ulrike Cihak-Bayr, Ioana Adina Neacșu, Reinhard Grundtner, Franz Pirker, and Georg Vorlaufer. 2021. "Semi-Supervised Classification of the State of Operation in Self-Lubricating Journal Bearings Using a Random Forest Classifier" Lubricants 9, no. 5: 50. https://doi.org/10.3390/lubricants9050050

APA Style

Prost, J., Cihak-Bayr, U., Neacșu, I. A., Grundtner, R., Pirker, F., & Vorlaufer, G. (2021). Semi-Supervised Classification of the State of Operation in Self-Lubricating Journal Bearings Using a Random Forest Classifier. Lubricants, 9(5), 50. https://doi.org/10.3390/lubricants9050050

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Semi-Supervised Classification of the State of Operation in Self-Lubricating Journal Bearings Using a Random Forest Classifier

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