Using Robust Generalized Fuzzy Modeling and Enhanced Symbolic Regression to Model Tribological Systems

Tribological systems are mechanical systems that rely on friction to transmit forces. The design and dimensioning of such systems requires prediction of various characteristic, such as the coefficient of friction. The core contribution of this paper is the analysis of two data-based modeling techniques which can be used to produce accurate and at the same time interpretable models for friction systems. We focus on two methods for building interpretable and potentially non-linear regression models: (i) robust fuzzy modeling with batch processing and an enhanced regularized learning scheme, and (ii) enhanced symbolic regression using genetic programming. We compare our results of both methods with state-of-the-art methods and found that linear models are insufficient for predicting the coefficient of friction, temperature, wear, and noise-vibration-harshness rating of the tribological systems, while the proposed robust fuzzy modeling and the enhanced symbolic regression approaches, as well as the state-of-the-art regression techniques, are able to generate satisfactory models. However, robust fuzzy modeling and enhanced symbolic regression lead to simpler models with fewer parameters that can be interpreted by domain experts.