Artificial intelligence as efficient technique for ball bearing fretting wear damage prediction

Broadening functionality of artificial intelligence and machine learning techniques shows that they are very useful computational intelligence methods. In the present study the potential of various artificial intelligence techniques to predict and analyze the damage is investigated. 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. Neural network structures are implemented to learn from experimental data, genetic programming to find a formula describing the wear volume and fuzzy inference system to impose physically meaningful rules. To gain data for the creation and verification of the model, experiments were conducted on commonly used chromium steel under dry and base oil bath-lubricated fretting test apparatus. 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.