**4. Discussion**

This paper presents a semi-supervised method for the classification of states of operation during a tribological sliding experiment in oscillating, translatory motion using an RF classifier.

An RF classifier was selected due to its low complexity regarding implementation, its good prediction accuracies, and the low requirements for model tuning. The RF model can be easily trained, validated, and applied on a local machine, with the capability of real-time classification. RF classifiers are especially well suited for industrial applications, as no AI expert is required to set up and tune sophisticated ANN-based algorithms [24].

The algorithm was trained on the basis of individual cycles. This is only possible if the force data are recorded with high temporal resolution. The trained algorithm was able to classify the state of operation with an accuracy of 0.939 for data of a labelled test experiment, using samples from four different experimental runs (i.e., four different journal bearings with otherwise identical experimental setup) as the training dataset.

The proposed methodology can be extended to similar systems, with different dimensions and materials of the involved bodies. However, datasets from these systems are necessary for training the algorithm. The transfer of an already trained algorithm to other systems remains an aspect for further investigation.

As a future perspective, online classification of the current status of the system will help to identify critical operation conditions. This will allow taking real-time countermeasures to assist the self-recovering process of the system, such as reduction of oscillation frequency or normal load, up to stopping the experiment to prevent major damage. The experiment can be stopped during critical operation for ex post analysis, e.g., material or surface analysis of the sliding bodies. Detailed knowledge of the system and its history can be used to define more complex stopping criteria, additionally to simple threshold values.

The presented approach may be extended to applications in industrial machinery, provided that a continuous force measurement and a sufficient amount of training data from ex post analysis are available. Examples for potential industrial applications range from journal bearings mounted in industrial equipment or drive trains to hydraulic presses, pistons, and manufacturing tools, especially where the accessibility of the system is limited for optical inspection.

In a further step, the presented algorithm may form a basis for lifetime prediction. Experimentally determined durations until reaching the stop criteria and thus termination of the experiment may be used as additional input for training the algorithm. This is a challenging task, as terminal failure often occurs suddenly without showing progressive deterioration in advance [9]. In the present work, sudden terminal failure occurred in about half of the analysed experiments. In the other experiments, terminal failure was preceded by pre-critical operation of up to 3.5 h. Inclusion of further continuous sensor data, such as temperature, acceleration, airborne or structure-borne AE, may serve to improve labelling and provide additional information for lifetime prediction. With a combination of these sensors, training of a similar RF algorithm is possible, even if no continuous force data are available.

In contrast to most studies regarding ML in tribological applications, in the current study, a self-recovering system was analysed. Thus, the system may stabilise after a precritical or even critical phase and return to steady operation. For conventional tribological systems, pre-critical or critical operation indicates an impending failure of the system, and stopping the experiment is the only way to prevent major damages. For self-recovering systems, an online ML algorithm will have to distinguish between transient and terminal critical operation. To achieve that, additional datasets such as AE or acceleration data have to be included.

A high quality of the labels assigned to the training dataset has proven to be the key for a high prediction accuracy of the RF algorithm. The presented semi-supervised approach—labelling by unsupervised k-means clustering with manual refinement—offers the flexibility to choose within a range between fully automated, unsupervised labelling and entirely manual labelling based on expert knowledge. In order to provide high-quality labelled training datasets, tribological and engineering expertise have to be included in the classification process in any case.

There are several papers on friction and wear monitoring as well as failure classification using data from AE sensors, e.g., [9,14,51] or image data, including optical [22] and thermal imaging [23]. In contrast, the proposed method focuses on time-series data from a force sensor collected at sampling rate of 5 kHz, similar to e.g., [2], as a data source for training a ML algorithm. This has the great advantage that high classification accuracy can be reached by using only force data, recorded by default in any tribological experiment. However, time-series data from other sensors, such as AE or acceleration, and optical or thermal image data, can provide useful additional information, which can be used to increase the algorithm's classification accuracy.

The focus of the current work was set to the overall health condition of the bearing, which can be characterised by its state of operation, ultimately related to wear and lubrication in the contact area. As a system of self-lubricating journal bearings exhibits the ability for self-recovery during usage, it could be shown that the presented RF classifier allows detecting critical conditions prior to the onset of machine failure, solely based on the lateral force data. Future research will address the prediction of useful remaining lifetime and ultimate system failure.
