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.

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In transport of raw materials abrasive wear resistance is a requirement, which can be met with alumina ceramics. At certain locations additional impact load occurs, exacerbating the wear loss. As literature on impact-abrasive wear of ceramics is scarce this topic will be researched in this work. We selected a variety of commercial alumina ceramics (90-96% Al2O3) and according to literature much more impact resistant – and substantially more expensive – Zirconia Toughened Alumina (ZTA, 20- 25% Zr) used for wear resistant linings in impact dominated systems. We selected the Impeller-Tumbler test, an impact-abrasion test for wear evaluation of highly loaded ceramic edges. Two load regimes were evaluated for the abrasive, either corundum particles for severe load or steel for extreme load conditions. Correlation of wear results with the fracture toughness (K1C) and Vickers hardness of the materials is intended – material parameters used as measure for brittle failure of ceramics and wear resistance, respectively. The various ceramic materials showed very different wear behaviour under the impact-abrasive load. The 90% Al2O3 was least wear resistant under both load conditions. Interestingly large differences between the 92% types were found at extreme load conditions. The increase of alumina content to 95- 96% showed remarkable benefits in both wear regimes. Best wear resistance against severe load was achieved by 25% ZTA, while it is comparable to the 95-96% high alumina types at extreme load.

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