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Item type:Publication, Access status: with files , Machine learning-based state maps for complex dynamical systems: applications to friction-excited brake system vibrationsIn this work, a purely data-driven approach to mapping out the state of a dynamical system over a set of chosen parameters is presented and demonstrated along a case study using real-world experimental data from a friction brake system. Complex engineering systems often exhibit a rich bifurcation behavior with respect to one or several parameters, which is difficult to grasp using experimental approaches or numerical simulations. At the same time, the growing need for energy-efficient machines that can operate under varying or extreme environmental conditions also calls for a better understanding of these systems to avoid critical transitions. The proposed method combines machine learning techniques with synthetic data augmentation to create a complete state map for a dynamical system. First, a machine learning model is trained on experimental data, picking up hidden mechanisms and complex parametric relations of the underlying dynamical system. The model is then exploited to assess the state of the system for a set of synthetically generated data to obtain a state map over the complete space spanned by the chosen parameters. In addition, an extension of the concept to a probability state map is introduced. The results indicate that the proposed method can uncover hidden variables which drive dynamical transitions between different states of a system that were previously inaccessible.Publicationtype: Journal ArticleScopus© Citations 2 36 78 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Data-driven stability maps for friction induced vibrationsFriction-induced vibrations, apparent for example as squeal noise in car braking systems, have seen a significant amount of research effort in the past and remain an area of active research today. Due to the huge bifurcation parameter space and model parameter uncertainties, the instability mechanisms of these vibrations remain elusive. Particularly, bifurcation parameters and critical sensitivities vary from brake system to brake system. Numerical simulations cannot resolve all aspects for robust instability predictions today. Recent work has illustrated how data-driven approaches to friction-induced noise modeling can be an alternative approach to conventional physics-based modeling: A digital twin representing a specific brake system was shown to predict instabilities and mode coupling associated with squeal occurrence with high accuracy. This work builds upon previous data-driven approaches to obtain a digital twin from measurement data of a real-world disc brake system. After validation, this data-based model is exploited to study the effect of various loading conditions on the predicted squealing behavior of the system. By gradually altering the loading conditions, we generate stability maps that encode information about the stability regions of the respective brake system. These stability maps provide a data-driven perspective on the mechanisms underlying frictioninduced noise, fostering a deeper understanding of the bifurcation parameter space of the underlying system.Publicationtype: Conference Presentation147 195