Blind Identification of State-Space Models in Physical Coordinates

TL;DR

This paper introduces a new blind identification method for state-space models in physical coordinates, transforming input-output models into input-free models using periodic signal assumptions, validated by numerical and practical examples.

Contribution

The paper proposes a novel approach to identify state-space models in physical coordinates by transforming models with input into input-free models using periodic signal assumptions.

Findings

Effective identification of physical and modal parameters

Validated with numerical and real-world examples

Outperforms existing methods in accuracy

Abstract

Blind identification is popular for modeling a system without the input information, such as in the research areas of structural health monitoring and audio signal processing. Existing blind identification methods have both advantages and disadvantages, in this paper, we briefly outline current methods and propose a novel blind identification method for identifying state-space models in physical coordinates. The idea behind this proposed method is first to regard the collected input data of a state-space model as a part of a periodic signal sequence, and then transform the state-space model with input and output into a model without input by augmenting the state-space model with the input model (which is a periodic signal model), and afterwards use merely the output information to identify a state-space model up to a similarity transformation, and finally derive the state-space model in physical coordinates by using a unique similarity transformation. With the above idea, physical parameters and modal parameters of a state-space system can be obtained. Both numerical and practical examples were used to validate the proposed method. The result showed the effectiveness of the novel blind identification method.