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

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.

A review of advances in tribology in 2020–2021

“…A simple ANN approach based on the "universal approximation" has also been applied to predictions of fretting wear life [434], of surfactant concentration effects in electroless Ni-B coating [435], of wear resistance of binary coatings of Cr3C237WC18M and WC20Cr3C27Ni [436], and identification of 3D ferrography wear particle images [437,438]. Prost et al [439] conducted semi-supervised classification of the state of operation in self-lubricating journal bearings using a random forest classifier.…”

Current Trends and Applications of Machine Learning in Tribology—A Review

“…It was indicated that data scaling was essential, while feature scaling, which is often applied in data analysis, was not suitable for the FFT classification. Prost et al [78] investigated the feasibility of classifying the operating condition (running-in, steady, pre-critical, critical) of a translationally oscillating self-lubricating journal bearing using an ensemble learning algorithm. To this end, the authors applied a semi-supervised random forest classifier (RFC), which was based on the aggregation of a large number of independent decision trees.…”

On the Importance of Temporal Information for Remaining Useful Life Prediction of Rolling Bearings Using a Random Forest Regressor

“…A comprehensive overview of the available Deep Learning methods can be found in [27]. In [10], a random forest approach has already been used to detect the state of journal bearings. The aim of the present work is to show the influence of targeted feature engineering on RUL prediction performance.…”

“…Among the machine learning algorithms used for RUL predictions there are different variants of neural networks, such as convolutional neural networks (CNN) [6], recurrent neural networks (RNN) [7], long short-term memory (LSTM) [8], and generative adversarial networks (GAN) [9]. Furthermore, there are contributions to state detection using random forest algorithms [10]. Machine learning is therefore becoming increasingly relevant, not least in the field of tribology [11].…”