System Identification Based on Output-Only Decomposition and Subspace Appropriation

Amirali Sadeqi; Shapour Moradi; Kourosh Heidari Shirazi

doi:10.1115/1.4043336

System Identification Based on Output-Only Decomposition and Subspace Appropriation

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4043336 ◽

2019 ◽

Vol 141 (9) ◽

Cited By ~ 1

Author(s):

Amirali Sadeqi ◽

Shapour Moradi ◽

Kourosh Heidari Shirazi

Keyword(s):

Subspace Identification ◽

Identification Methods ◽

Input Type ◽

Stochastic Framework ◽

Innovation Model ◽

Orthonormal Matrix ◽

Output Only ◽

Row Space ◽

First Time ◽

Transient And Steady State

Output-only identification methods have been developed on a stochastic framework, but for the first time, a subspace-based approach is proposed without using geometric and statistical tools. This aids the computational efforts to be significantly reduced and the range of input sources to be extended in a much realistic manner for future output-only analyses. The approach encompasses any input type and can properly work for systems excited by inputs with finite periods. It is demonstrated that the row space of the output sequences spanned by column vectors of the decomposed orthonormal matrix is sufficient to reconstruct the observations. The transient and steady-state portions of the output row space, afterward, can be captured to reconstruct an integrated innovation model. The advantages of the algorithm are highlighted through several numerical and experimental examples comparing with the traditional subspace identification algorithms.

Download Full-text

Stochastic System Identification for Operational Modal Analysis: A Review

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.1410370 ◽

2001 ◽

Vol 123 (4) ◽

pp. 659-667 ◽

Cited By ~ 518

Author(s):

Bart Peeters ◽

Guido De Roeck

Keyword(s):

System Identification ◽

Stochastic System ◽

Instrumental Variable ◽

Subspace Identification ◽

Response Functions ◽

Impulse Responses ◽

Identification Methods ◽

Operational Conditions ◽

Stochastic Subspace Identification ◽

Output Only

This paper reviews stochastic system identification methods that have been used to estimate the modal parameters of vibrating structures in operational conditions. It is found that many classical input-output methods have an output-only counterpart. For instance, the Complex Mode Indication Function (CMIF) can be applied both to Frequency Response Functions and output power and cross spectra. The Polyreference Time Domain (PTD) method applied to impulse responses is similar to the Instrumental Variable (IV) method applied to output covariances. The Eigensystem Realization Algorithm (ERA) is equivalent to stochastic subspace identification.

Download Full-text

Modal Parameters Identification Methods based on output-only

10.26678/abcm.cobem2017.cob17-2347 ◽

2017 ◽

Author(s):

Nilson Barbieri ◽

Jacqueline Terre de Souza

Keyword(s):

Parameters Identification ◽

Modal Parameters ◽

Identification Methods ◽

Modal Parameters Identification ◽

Output Only

Download Full-text

Identification of Linear Tire Cornering Stiffness Using Subspace Methods

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.701-702.492 ◽

2014 ◽

Vol 701-702 ◽

pp. 492-497

Author(s):

Teng Yue Ba ◽

Xi Qiang Guan ◽

Jian Wu Zhang

Keyword(s):

Data Driven ◽

Subspace Identification ◽

Subspace Methods ◽

Identification Methods ◽

The Road ◽

Strong Robustness ◽

Subspace Algorithms ◽

Double Lane Change ◽

On The Road ◽

Lane Change Test

In this paper, subspace identification methods are proposed to estimate the linear tire cornering stiffness, which are only based on the road tests data without any prior knowledge. This kind of data-driven method has strong robustness. In order to validate the feasibility and effectiveness of the algorithms, a series of standard road tests are carried out. Comparing with different subspace algorithms used in road tests, it can be concluded that the front tire cornering stiffness can be estimated accurately by the N4SID and CCA methods when the double lane change test data are taken into analysis.

Download Full-text

Output-Only Identification of System Parameters from Noisy Measurements by Multiwavelet Denoising

Advances in Mechanical Engineering ◽

10.1155/2014/218328 ◽

2014 ◽

Vol 6 ◽

pp. 218328 ◽

Cited By ~ 2

Author(s):

O. Al-Gahtani ◽

M. El-Gebeily ◽

Y. Khulief

Keyword(s):

Wavelet Transforms ◽

Model Identification ◽

Subspace Identification ◽

Measuring Devices ◽

Noisy Output ◽

Output Only ◽

Modal Values ◽

Noisy Measurements ◽

Stochastic Identification ◽

Wavelet Methods

In this paper we estimate the parameters of a multidimensional system from a record of noisy output measurements by using a multiwavelet denoising technique. In this output-only identification scheme, we extend wavelet denoising methods to the multiwavelet case. After the noise has been removed from the output records by wavelet methods, either full model identification or deterministic subspace identification can be performed. In the former case, full information on the system such as modal values and shapes becomes available by postprocessing. In the latter case, the observable modal values of the system as well as modal shapes at the sensor locations can be extracted from the identified parameters. Additionally, we discuss the requirements on the measuring devices to be compatible with wavelet transforms of a particular type. The validity and merit of the developed scheme are illustrated by examples of numerically simulated and experimentally measured signals, including comparisons with stochastic identification methods.

Download Full-text

Business model innovation canvas: a visual business model innovation model

European Journal of Innovation Management ◽

10.1108/ejim-02-2021-0079 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Yuran Jin ◽

Shoufeng Ji ◽

Li Liu ◽

Wei Wang

Keyword(s):

3D Printing ◽

Business Model ◽

Innovation Process ◽

Theory Model ◽

Business Model Innovation ◽

Mass Selection ◽

Unified Framework ◽

Content Type ◽

Innovation Model ◽

First Time

PurposeMore and more enterprises have realized the importance of business model innovation. However, the model tools for it are still scarce. There is a clear research gap in this academic field. Therefore, the aim of this study is to put forward a visual business model innovation model.Design/methodology/approachThe scientific literature clustering paradigm of grounded theory is used to design business model innovation theory model (BMITM). BMITM and the business model innovation options traced back from 870 labels in the grounded process are integrated into a unified framework to build the business model innovation canvas (BMIC).FindingsBMIC composed of three levels and seven modules is successfully developed. 145 business model innovation options are designed in BMIC. How to use BMIC is explained in detail. Through the analysis of innovation hotspots, the potential business model innovation directions can be found. A new business model of clothing enterprises using 3D printing is innovated with BMIC as an example.Research limitations/implicationsCompared with the previous tools, BMIC owns a clearer business model innovation framework and provides a problem-oriented business model innovation process and mechanism.Practical implicationsBMIC provides a systematic business model innovation solution set and roadmap for business model innovation practitioners.Originality/valueBMIC, a new tool for business model innovation is put forward for the first time. “Mass Selection Customization-Centralized Manufacturing” designed with BMIC for the clothing enterprises using 3D printing is put forward for the first time.

Download Full-text