title: Identification of pre-sliding friction dynamics.
abstract: The hysteretic nonlinear dependence of pre-sliding friction force on displacement is modeled using different physics-based and black-box approaches including various Maxwell-slip models, NARX models, neural networks, nonparametric (local) models and dynamical networks. The efficiency and accuracy of these identification methods is compared for an experimental time series where the observed friction force is predicted from the measured displacement. All models, although varying in their degree of accuracy, show good prediction capability of pre-sliding friction. Finally, we show that even better results can be achieved by using an ensemble of the best models for prediction.
authors.affiliation: Drittes Physikalisches Institut, Universitat Gottingen, Burgerstrasse 42-44, D-37073 Gottingen, Germany. parlitz@dpi.physik.uni-goettingen.de
authors.forename: U
authors.initials: U
authors.lastname: Parlitz
authors.forename: A
authors.initials: A
authors.lastname: Hornstein
authors.forename: D
authors.initials: D
authors.lastname: Engster
authors.forename: F
authors.initials: F
authors.lastname: Al-Bender
authors.forename: V
authors.initials: V
authors.lastname: Lampaert
authors.forename: T
authors.initials: T
authors.lastname: Tjahjowidodo
authors.forename: S D
authors.initials: SD
authors.lastname: Fassois
authors.forename: D
authors.initials: D
authors.lastname: Rizos
authors.forename: C X
authors.initials: CX
authors.lastname: Wong
authors.forename: K
authors.initials: K
authors.lastname: Worden
authors.forename: G
authors.initials: G
authors.lastname: Manson