On the Detection and Quantification of Nonlinearity via Statistics of the Gradients of a Black-Box Model

by   G. Tsialiamanis, et al.

Detection and identification of nonlinearity is a task of high importance for structural dynamics. Detecting nonlinearity in a structure, which has been designed to operate in its linear region, might indicate the existence of damage. Therefore, it is important, even for safety reasons, to detect when a structure exhibits nonlinear behaviour. In the current work, a method to detect nonlinearity is proposed, based on the distribution of the gradients of a data-driven model, which is fitted on data acquired from the structure of interest. The data-driven model herein is a neural network. The selection of such a type of model was done in order to not allow the user to decide how linear or nonlinear the model shall be, but to let the training algorithm of the neural network shape the level of nonlinearity according to the training data. The neural network is trained to predict the accelerations of the structure for a time-instant using as inputs accelerations of previous time-instants, i.e. one-step-ahead predictions. Afterwards, the gradients of the output of the neural network with respect to its inputs are calculated. Given that the structure is linear, the distribution of the aforementioned gradients should be quite peaked, while in the case of a structure with nonlinearities, the distribution of the gradients shall be more spread and, potentially, multimodal. To test the above assumption, data from an experimental structure are considered. The structure is tested under different scenarios, some of which are linear and some nonlinear. The statistics of the distributions of the gradients for the different scenarios can be used to identify cases where nonlinearity is present. Moreover, via the proposed method one is able to quantify the nonlinearity by observing higher values of standard deviation of the distribution of the gradients for "more nonlinear" scenarios.


Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling

Discovery of dynamical systems from data forms the foundation for data-d...

Computationally Efficient Data-Driven Discovery and Linear Representation of Nonlinear Systems For Control

This work focuses on developing a data-driven framework using Koopman op...

Data-driven End-to-end Learning of Pole Placement Control for Nonlinear Dynamics via Koopman Invariant Subspaces

We propose a data-driven method for controlling the frequency and conver...

Gradients of Counterfactuals

Gradients have been used to quantify feature importance in machine learn...

Learning Density Distribution of Reachable States for Autonomous Systems

State density distribution, in contrast to worst-case reachability, can ...

Please sign up or login with your details

Forgot password? Click here to reset