Extraction of modal parameters for identification of time-varying systems using data-driven stochastic subspace identification

2017 ◽  
Vol 24 (20) ◽  
pp. 4781-4796 ◽  
Author(s):  
Wenchao Li ◽  
Viet-Hung Vu ◽  
Zhaoheng Liu ◽  
Marc Thomas ◽  
Bruce Hazel
Keyword(s):  
Data Driven ◽  
Modal Parameters ◽  
Subspace Identification ◽  
Time Varying ◽  
Lumped Mass ◽  
State Matrix ◽  
Mass System ◽  
Stochastic Subspace Identification ◽  
Time Varying Systems ◽  
Using Data

This paper presents a method for the extraction of modal parameters for identification of time-varying systems using Data-Driven Stochastic Subspace Identification (SSI-DATA). In practical applications of SSI-DATA, both the modal parameters and computational ones are mixed together in the identified results. In order to differentiate the structural ones from computational ones, a new method based on the eigen-decomposition of the state matrix constructed in SSI-DATA is proposed. The efficiency of the proposed method is demonstrated through numerical simulation of a lumped-mass system and experimental test of a moving robot for extracting excited natural frequencies of the system.

A Modified Stochastic Subspace Identification Method for Estimating Natural Frequencies

2016 ◽  
Author(s):  
Kaoshan Dai ◽  
Ying Wang ◽  
Yichao Huang ◽  
W. D. Zhu ◽  
Y. F. Xu
Keyword(s):  
Natural Frequencies ◽  
Health Assessment ◽  
Operating Conditions ◽  
Subspace Identification ◽  
Lumped Mass ◽  
Mass System ◽  
Stochastic Subspace Identification ◽  
Identification Method ◽  
Periodic Rotation ◽  
Subspace Identification Method

A system identification method for estimating natural frequencies is proposed. This method developed based on the stochastic subspace identification method can identify modal parameters of structures in operating conditions with harmonic components in excitation. It benefits wind turbine tower structural health assessment because classical operational modal analysis methods can fail as periodic rotation excitation from a turbine introduces strong harmonic disturbance to tower structure response data. The effectiveness, accuracy and robustness of the proposed method were numerically investigated and verified through a lumped-mass system model.

Experimental Modal Analysis of Jacket-Type Platforms Using Data-Driven Stochastic Subspace Identification Method

2012 ◽  
Author(s):  
Junfeng Xin ◽  
Sau-Lon James Hu ◽  
Huajun Li
Keyword(s):  
White Noise ◽  
Impact Loading ◽  
Modal Identification ◽  
Data Driven ◽  
Subspace Identification ◽  
Stochastic Subspace Identification ◽  
Gaussian White Noise Process ◽  
Using Data ◽  
Output Only ◽  
The Impact

Employing efficient techniques to accurately identify the modal parameters of new and aging offshore structures has been of interest to the offshore industry for decades. Early methods of modal identification were developed for the frequency domain. The new trend is to employ either input-output or output-only time-domain modal identification methods. Under the assumption that the excitation input is a zero-mean Gaussian white noise process, a modern output-only method that allows direct application to the response time series is the data-driven stochastic subspace identification (SSI-data) method. The main objective of this paper is to evaluate the performance of the SSI-data method using the test data measured from a physical model of a realistic offshore jacket-type platform. Response acceleration data associated with three different excitation mechanisms are investigated: impact loading, step relaxation and white noise ground motion. Although the SSI-data method has been theoretically developed, and often perceived to be only valid, for the ambient noise testing environment, it is shown in this study that the SSI-data method also performs well using data from either the impact loading or step relaxation tests.

Time-Varying Output-Only Identification of a Cracked Beam

2009 ◽  
Vol 413-414 ◽  
pp. 643-650 ◽  
Author(s):  
A. Bellino ◽  
Luigi Garibaldi ◽  
Stefano Marchesiello
Keyword(s):  
Subspace Identification ◽  
Open Crack ◽  
Moving Mass ◽  
Time Varying ◽  
Cracked Beam ◽  
Stochastic Subspace Identification ◽  
Identification Method ◽  
Time Varying Systems ◽  
Output Only ◽  
Short Time

In this paper a time-varying identification method is presented, in order to detect the presence of an open crack in a beam with a moving mass travelling on it. The ratio between the considered moving mass and the total mass of the beam is high, thus the identified modal frequencies of the whole structure are time-varying. This situation often occurs when considering the dynamic interaction beetween a train and a bridge and specific identification tools must be used. It is shown that the identification method, referred to here as Short-Time Stochastic Subspace Identification, can give information about the presence of damage in case of time-varying systems.

Floquet Experimental Modal Analysis for System Identification of Linear Time-Periodic Systems

2007 ◽  
Author(s):  
Matthew S. Allen
Keyword(s):  
System Identification ◽  
Linear Time ◽  
Time Varying ◽  
Response Model ◽  
State Matrix ◽  
Linear Time Invariant ◽  
Response Data ◽  
Time Varying Systems ◽  
Time Invariant ◽  
Time Periodic

A variety of systems can be faithfully modeled as linear with coefficients that vary periodically with time or Linear Time-Periodic (LTP). Examples include anisotropic rotorbearing systems, wind turbines, satellite systems, etc… A number of powerful techniques have been presented in the past few decades, so that one might expect to model or control an LTP system with relative ease compared to time varying systems in general. However, few, if any, methods exist for experimentally characterizing LTP systems. This work seeks to produce a set of tools that can be used to characterize LTP systems completely through experiment. While such an approach is commonplace for LTI systems, all current methods for time varying systems require either that the system parameters vary slowly with time or else simply identify a few parameters of a pre-defined model to response data. A previous work presented two methods by which system identification techniques for linear time invariant (LTI) systems could be used to identify a response model for an LTP system from free response data. One of these allows the system’s model order to be determined exactly as if the system were linear time-invariant. This work presents a means whereby the response model identified in the previous work can be used to generate the full state transition matrix and the underlying time varying state matrix from an identified LTP response model and illustrates the entire system-identification process using simulated response data for a Jeffcott rotor in anisotropic bearings.

scholarly journals Damping Effects Induced by a Mass Moving along a Pendulum

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
E. Gandino ◽  
S. Marchesiello ◽  
A. Bellino ◽  
A. Fasana ◽  
L. Garibaldi
Keyword(s):  
Linear Time ◽  
Damping Ratio ◽  
System Response ◽  
Time Varying ◽  
Simple Method ◽  
Equivalent Damping ◽  
Stochastic Subspace Identification ◽  
Time Varying Systems ◽  
Instantaneous Frequencies ◽  
Short Time

The experimental study of damping in a time-varying inertia pendulum is presented. The system consists of a disk travelling along an oscillating pendulum: large swinging angles are reached, so that its equation of motion is not only time-varying but also nonlinear. Signals are acquired from a rotary sensor, but some remarks are also proposed as regards signals measured by piezoelectric or capacitive accelerometers. Time-varying inertia due to the relative motion of the mass is associated with the Coriolis-type effects appearing in the system, which can reduce and also amplify the oscillations. The analytical model of the pendulum is introduced and an equivalent damping ratio is estimated by applying energy considerations. An accurate model is obtained by updating the viscous damping coefficient in accordance with the experimental data. The system is analysed through the application of a subspace-based technique devoted to the identification of linear time-varying systems: the so-called short-time stochastic subspace identification (ST-SSI). This is a very simple method recently adopted for estimating the instantaneous frequencies of a system. In this paper, the ST-SSI method is demonstrated to be capable of accurately estimating damping ratios, even in the challenging cases when damping may turn to negative due to the Coriolis-type effects, thus causing amplifications of the system response.

Sign in / Sign up

Export Citation Format

Share Document