Abstract
Accurately modeling nonlinearities is becoming increasingly important for mechanical systems, particularly in the context of system design, model-based control and monitoring systems for fault diagnosis. In the nonlinear modeling process, a pivotal phase involves pinpointing the physical locations and quantifying the magnitude of nonlinearities. This paper introduces a data-driven approach for nonlinearity location and quantification by analyzing nonparametric frequency response functions. To achieve this objective, measurement locations in mechanical systems are interpreted as nodes arranged in a dynamic network, and linearization techniques are employed on the frequency response functions formed from node to node. The efficacy of the proposed approach and the concept of nonlinearity localization and quantification are illustrated by numerical simulations and experiments on a flexible beam setup.
| Original language | English |
|---|---|
| Article number | 112124 |
| Number of pages | 17 |
| Journal | Mechanical Systems and Signal Processing |
| Volume | 224 |
| DOIs | |
| Publication status | Published - 1 Feb 2025 |
Funding
This work is supported by ASML Research, Veldhoven, the Netherlands . In addition, this work is funded by the European Union (Horizon Europe, ERC, COMPLETE, 101075836 ). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.
Keywords
- Dynamic networks
- Linearization
- Mechanical systems
- Nonlinear data-driven modeling
- Nonlinear systems
- System identification
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