Structural damage detection using real-time modal parameter identification algorithm

AIAA Journal ◽  
1996 ◽  
Vol 34 (11) ◽  
pp. 2370-2376 ◽  
Author(s):  
Tae W. Lim ◽  
Albert Bosse ◽  
Shalom Fisher
Keyword(s):  
Parameter Identification ◽  
Damage Detection ◽  
Real Time ◽  
Structural Damage ◽  
Identification Algorithm ◽  
Structural Damage Detection ◽  
Modal Parameter ◽  
Modal Parameter Identification

scholarly journals Time-Varying Modal Parameters Identification by Subspace Tracking Algorithm and Its Validation Method

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Haotian Zhou ◽  
Kaiping Yu ◽  
Yushu Chen ◽  
Rui Zhao ◽  
Yunhe Bai
Keyword(s):  
Parameter Identification ◽  
Spatial Clustering ◽  
Signal To Noise Ratio ◽  
Information Criterion ◽  
Modal Parameters ◽  
Identification Algorithm ◽  
Time Varying ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Post Process

This article presents a time-varying modal parameter identification method based on the novel information criterion (NIC) algorithm and a post-process method for time-varying modal parameter estimation. In the practical application of the time-varying modal parameter identification algorithm, the identified results contain both real modal parameters and aberrant ones caused by the measurement noise. In order to improve the quality of the identified results as well as sifting and validating the real modal parameters, a post-process procedure based on density-based spatial clustering of applications with noise (DBSCAN) algorithm is introduced. The efficiency of the proposed approach is first verified through a numerical simulation of a cantilever Euler-Bernoulli beam with a time-varying mass. Then the proposed approach is experimentally demonstrated by composite sandwich structure in a time-varying high temperature environment. The identified results illustrate that the proposed approach can obtain real modal frequencies in low signal-to-noise ratio (SNR) scenarios.

scholarly journals Real-time unified single- and multi-channel structural damage detection using recursive singular spectrum analysis

2018 ◽  
Vol 18 (2) ◽  
pp. 563-589 ◽  
Author(s):  
Basuraj Bhowmik ◽  
Manu Krishnan ◽  
Budhaditya Hazra ◽  
Vikram Pakrashi
Keyword(s):  
Spectral Analysis ◽  
Damage Detection ◽  
Real Time ◽  
Structural Damage ◽  
Streaming Data ◽  
Degree Of Freedom ◽  
Time Varying ◽  
Structural Damage Detection ◽  
Autoregressive Modeling ◽  
Singular Spectral Analysis

A novel baseline-free approach for continuous online damage detection of multidegree of freedom vibrating structures using recursive singular spectral analysis in conjunction with time-varying autoregressive modeling is proposed in this article. The acceleration data are used to obtain recursive proper orthogonal components online using rank-one perturbation method, followed by time-varying autoregressive modeling of the first transformed response, to detect the change in the dynamic behavior of the vibrating system from its original state to contiguous linear/nonlinear states indicating damage. Most work to date deal with algorithms that require windowing of the gathered data that render them ineffective for online implementation. Algorithms focused on mathematically consistent recursive techniques in a rigorous theoretical framework of structural damage detection are missing that motivates the development of the present framework. The response from a single channel is provided as input to the algorithm in real time. The recursive singular spectral analysis algorithm iterates the eigenvector and eigenvalue estimates for sample covariance matrices and new data point at each successive time instants. This eliminates the need for offline post processing and facilitates online damage detection especially when applied to streaming data without requiring any baseline data. Lower order time-varying autoregressive models are applied on the transformed responses to improve detectability. Numerical simulations performed on a five-degree of freedom nonlinear system and on a single degree of freedom system modeled using a Duffing oscillator under white noise excitation data, with different levels of nonlinearity simulating the damage scenarios, demonstrate the robustness of the proposed algorithm. The method further validated on results obtained from experiments performed on a cantilever beam subjected to earthquake excitation; a toy cart experiment model with springs attached to either side; demonstrate the efficacy of the proposed methodology as an appropriate candidate for real-time, reference-free structural health monitoring.

scholarly journals Application of Improved Combined Deterministic-Stochastic Subspace Algorithm in Bridge Modal Parameter Identification

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Peng Wen ◽  
Inamullah Khan ◽  
Jie He ◽  
Qiaofeng Chen
Keyword(s):  
Parameter Identification ◽  
Clustering Algorithm ◽  
Identification Algorithm ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Fuzzy C Means ◽  
Subspace Algorithm ◽  
Bridge Structures ◽  
Fuzzy C Means Clustering ◽  
Online Tracking

Modal parameter identification is considered to be one of the most important tasks in structural health monitoring because it provides a reliable reference for structural vibration control, damage severity, and operational state. Moreover, at present, the combined deterministic-stochastic subspace algorithm is cogitated as one of the key algorithms in the modal parameter identification, which is why it is widely used in the modal parameter identification of bridge structures. In this paper, a novel method is proposed, which is a time-domain identification algorithm, based on sliding window-fuzzy C-means clustering algorithm-combined with deterministic-stochastic subspace identification (SC-CDSI), to achieve online intelligent tracking and identification of modal parameters for nonlinear time-varying structures. First of all, to realize the online tracking and identification process, it is necessary to divide the input and output signal of the nonlinear time-varying structure by windowing; for that, to determine the window function, window size and window step length according to the characteristics of the signal are analyzed. Secondly, in order to satisfy the intelligent identification of effective modals in stability diagram, the fuzzy C-means clustering algorithm is kept as a base, whereas frequency, damping ratio, and modal shapes serve as clustering elements, applied to fuzzy C-means clustering algorithm, and then the intelligent selection of effective modals is achieved. Finally, a shaking table test bridge is used as a modal parameter identification in lab, and its results are compared with the MIDAS finite element results. The compared results show that the proposed SC-CDSI identification algorithm can accurately achieve the intelligent identification of online tracking of the structural frequency, and the identification results are reliable to be used in real-life bridge structures.

Real-time structural damage detection using wireless sensing and monitoring system

2008 ◽  
Vol 4 (6) ◽  
pp. 759-777 ◽  
Author(s):  
Kung-Chun Lu ◽  
Chin-Hsiung Loh ◽  
Yuan-Sen Yang ◽  
Jerome P. Lynch ◽  
K.H. Law
Keyword(s):  
Damage Detection ◽  
Real Time ◽  
Monitoring System ◽  
Structural Damage ◽  
Wireless Sensing ◽  
Structural Damage Detection
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