A methodological approach ball bearing damage prediction under fretting wear conditions.

The industrial demand for higher reliability of various components is one of the main flywheels of the research and development in the field of modelling of complex phenomena. There is a need to characterize the wear behaviour of the interface under fretting wear conditions in ball bearing application. Pre-treated experimental data was used to determine the wear of contacting surfaces as a criterion of damage that can be useful for a life-time prediction. The benefit of acquired knowledge can be crucial for the industrial expert systems and the scientific feature extraction that cannot be underestimated. Wear is a very complex and partially-formalized phenomenon involving numerous parameters and damage mechanisms. To correlate the working conditions with the state of contacting bodies and to define damage mechanisms different techniques are used. The use of our approaches in the prediction of the response of the system to different test conditions is validated. Two physical models, based on Archard and Energetic approach, are compared with artificial neural network model and genetic programming. Decisive factors for a comparison of used AI techniques are their: performance, generalization capabilities, complexity and time-consumption. Optimization of the structure of the model is done to reach high robustness of field applications. Finally, application of the wear level information to forecast a probability of damage is presented.