Semi-Supervised Classification of the State of Operation in Self-Lubricating Journal Bearings Using a Random Forest Classifier
| Autor: | Reinhard Grundtner, Ulrike Cihak-Bayr, Josef Prost, Ioana Adina Neacșu, Franz Pirker, G. Vorlaufer |
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| Rok vydání: | 2021 |
| Předmět: |
semi-supervised learning
0209 industrial biotechnology Computer science Science condition monitoring random forest classifier 02 engineering and technology Semi-supervised learning law.invention 020901 industrial engineering & automation 0203 mechanical engineering law Classifier (linguistics) Range (statistics) self-lubricating journal bearings Bearing (mechanical) business.industry Mechanical Engineering Ex post analysis Condition monitoring Pattern recognition Surfaces Coatings and Films Random forest 020303 mechanical engineering & transports Artificial intelligence State (computer science) business |
| Zdroj: | Lubricants Volume 9 Issue 5 Lubricants, Vol 9, Iss 50, p 50 (2021) |
| ISSN: | 2075-4442 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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