**5. Conclusions**

In this paper, an oscillating, translatory sliding experiment of a self-lubricating bronze journal bearing, which provides the system with the ability to self-recover minor damages, was studied to elaborate a semi-supervised ML algorithm predicting critical operating conditions. An RF classifier was trained on the basis of single cycles of lateral force signals acquired with high resolution and including expertise knowledge of tribologists. Four different states of operation were identified based on the shape of the cycles. The main findings of the present paper are as follows:


**Author Contributions:** Conceptualisation: U.C.-B. and F.P., data curation: J.P. and G.V., formal analysis: J.P., G.V. and I.A.N., methodology: U.C.-B., R.G., J.P. and G.V., project administration: J.P., software: J.P., supervision: G.V., validation: J.P., visualisation: J.P., writing—original draft preparation: J.P., R.G. and I.A.N., writing—review and editing: U.C.-B., F.P. and G.V. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was funded by the Austrian COMET Program (project InTribology1, No. 872176) via the Austrian Research Promotion Agency (FFG) and the Provinces of Niederösterreich and Vorarlberg and has been carried out within the Austrian Excellence Centre of Tribology (AC2T research GmbH).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this work are available on request from the corresponding author.

**Conflicts of Interest:** The authors declare no conflict of interest.
