scholarly journals A Comparative Study of the Data-Driven Stochastic Subspace Methods for Health Monitoring of Structures: A Bridge Case Study

2020 ◽  
Vol 10 (9) ◽  
pp. 3132 ◽  
Author(s):  
Hoofar Shokravi ◽  
Hooman Shokravi ◽  
Norhisham Bakhary ◽  
Seyed Saeid Rahimian Koloor ◽  
Michal Petrů
Keyword(s):  
System Identification ◽  
State Space ◽  
Health Monitoring ◽  
Modal Identification ◽  
Canonical Variate ◽  
Lumped Mass ◽  
Mass System ◽  
Principle Component ◽  
Computation Efficiency ◽  
Subspace System Identification

Subspace system identification is a class of methods to estimate state-space model based on low rank characteristic of a system. State-space-based subspace system identification is the dominant subspace method for system identification in health monitoring of the civil structures. The weight matrices of canonical variate analysis (CVA), principle component (PC), and unweighted principle component (UPC), are used in stochastic subspace identification (SSI) to reduce the complexity and optimize the prediction in identification process. However, researches on evaluation and comparison of weight matrices’ performance are very limited. This study provides a detailed analysis on the effect of different weight matrices on robustness, accuracy, and computation efficiency. Two case studies including a lumped mass system and the response dataset of the Alamosa Canyon Bridge are used in this study. The results demonstrated that UPC algorithm had better performance compared to two other algorithms. It can be concluded that though dimensionality reduction in PC and CVA lingered the computation time, it has yielded an improved modal identification in PC.

scholarly journals Application of the Subspace-Based Methods in Health Monitoring of Civil Structures: A Systematic Review and Meta-Analysis

2020 ◽  
Vol 10 (10) ◽  
pp. 3607
Author(s):  
Hoofar Shokravi ◽  
Hooman Shokravi ◽  
Norhisham Bakhary ◽  
Mahshid Heidarrezaei ◽  
Seyed Saeid Rahimian Koloor ◽  
...  
Keyword(s):  
System Identification ◽  
Systematic Reviews ◽  
Health Monitoring ◽  
Meta Analysis ◽  
Civil Structures ◽  
Literature Reviews ◽  
Research Gap ◽  
Meta Analyses ◽  
Subspace System Identification ◽  
Insight Into

A large number of research studies in structural health monitoring (SHM) have presented, extended, and used subspace system identification. However, there is a lack of research on systematic literature reviews and surveys of studies in this field. Therefore, the current study is undertaken to systematically review the literature published on the development and application of subspace system identification methods. In this regard, major databases in SHM, including Scopus, Google Scholar, and Web of Science, have been selected and preferred reporting items for systematic reviews and meta-analyses (PRISMA) has been applied to ensure complete and transparent reporting of systematic reviews. Along this line, the presented review addresses the available studies that employed subspace-based techniques in the vibration-based damage detection (VDD) of civil structures. The selected papers in this review were categorized into authors, publication year, name of journal, applied techniques, research objectives, research gap, proposed solutions and models, and findings. This study can assist practitioners and academicians for better condition assessment of structures and to gain insight into the literature.

scholarly journals Health Monitoring of Civil Infrastructures by Subspace System Identification Method: An Overview

2020 ◽  
Vol 10 (8) ◽  
pp. 2786 ◽  
Author(s):  
Hoofar Shokravi ◽  
Hooman Shokravi ◽  
Norhisham Bakhary ◽  
Seyed Saeid Rahimian Koloor ◽  
Michal Petrů
Keyword(s):  
System Identification ◽  
Health Monitoring ◽  
Civil Engineering ◽  
Damage Identification ◽  
Engineering Structures ◽  
Practical Applications ◽  
Damage Scenarios ◽  
Advantages And Disadvantages ◽  
Civil Engineering Structures ◽  
Subspace System Identification

Structural health monitoring (SHM) is the main contributor of the future’s smart city to deal with the need for safety, lower maintenance costs, and reliable condition assessment of structures. Among the algorithms used for SHM to identify the system parameters of structures, subspace system identification (SSI) is a reliable method in the time-domain that takes advantages of using extended observability matrices. Considerable numbers of studies have specifically concentrated on practical applications of SSI in recent years. To the best of author’s knowledge, no study has been undertaken to review and investigate the application of SSI in the monitoring of civil engineering structures. This paper aims to review studies that have used the SSI algorithm for the damage identification and modal analysis of structures. The fundamental focus is on data-driven and covariance-driven SSI algorithms. In this review, we consider the subspace algorithm to resolve the problem of a real-world application for SHM. With regard to performance, a comparison between SSI and other methods is provided in order to investigate its advantages and disadvantages. The applied methods of SHM in civil engineering structures are categorized into three classes, from simple one-dimensional (1D) to very complex structures, and the detectability of the SSI for different damage scenarios are reported. Finally, the available software incorporating SSI as their system identification technique are investigated.

Aspects of Novelty Detection

2007 ◽  
Vol 347 ◽  
pp. 3-16 ◽  
Author(s):  
Keith Worden ◽  
Graeme Manson ◽  
Cecilia Surace
Keyword(s):  
Experimental Study ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Novelty Detection ◽  
Fe Model ◽  
Offshore Structure ◽  
Lumped Mass ◽  
Aircraft Wing ◽  
Mass System ◽  
Detection Techniques

The object of this paper is to illustrate the use of novelty detection techniques in Structural Health Monitoring (SHM) by the consideration of a number of case studies of varying complexity, from a simple lumped-mass system to an FE model of an offshore structure to an experimental study of an aircraft wing.

scholarly journals Structural System Identification in the Time Domain using Evolutionary and Behaviorally Inspired Algorithms and their Hybrids

2009 ◽  
Vol 6 (2) ◽  
pp. 64
Author(s):  
S. Sandesh ◽  
Abhishek Kumar Sahu ◽  
K. Shankar
Keyword(s):  
System Identification ◽  
Time Domain ◽  
Numerical Models ◽  
Parametric Identification ◽  
Lumped Mass ◽  
Unknown Parameters ◽  
Mass System ◽  
The Time Domain ◽  
Structural System Identification ◽  
Speed And Accuracy

 In this study, parametric identification of structural properties such as stiffness and damping is carried out using acceleration responses in the time domain. The process consists of minimizing the difference between the experimentally measured and theoretically predicted acceleration responses. The unknown parameters of certain numerical models, viz., a ten degree of freedom lumped mass system, a nine member truss and a non-uniform simply supported beam are thus identified. Evolutionary and behaviorally inspired optimization algorithms are used for minimization operations. The performance of their hybrid combinations is also investigated. Genetic Algorithm (GA) is a well known evolutionary algorithm used in system identification. Recently Particle Swarm Optimization (PSO), a behaviorally inspired algorithm, has emerged as a strong contender to GA in speed and accuracy. The discrete Ant Colony Optimization (ACO) method is yet another behaviorally inspired method studied here. The performance (speed and accuracy) of each algorithm alone and in their hybrid combinations such as GA with PSO, ACO with PSO and ACO with GA are extensively investigated using the numerical examples with effects of noise added for realism. The GA+PSO hybrid algorithm was found to give the best performance in speed and accuracy compared to all others. The next best in performance was pure PSO followed by pure GA. ACO performed poorly in all the cases. 

scholarly journals Model Predictive Control Method of Simulated Moving Bed Chromatographic Separation Process Based on Subspace System Identification

2019 ◽  
Vol 2019 ◽  
pp. 1-24 ◽  
Author(s):  
Zhen Yan ◽  
Jie-Sheng Wang ◽  
Shao-Yan Wang ◽  
Shou-Jiang Li ◽  
Dan Wang ◽  
...  
Keyword(s):  
System Identification ◽  
State Space ◽  
Predictive Control ◽  
Chromatographic Separation ◽  
Control Method ◽  
Separation Process ◽  
Simulated Moving Bed ◽  
Moving Bed ◽  
Separation Technology ◽  
Subspace System Identification

Simulated moving bed (SMB) chromatographic separation is a new type of separation technology based on traditional fixed bed adsorption operation and true moving bed (TMB) chromatographic separation technology, which includes inlet-outlet liquid, liquid circulation, and feed liquid separation. The input-output data matrices were constructed based on SMB chromatographic separation process data. The SMB chromatographic separation process was modeled by utilizing two subspace system identification algorithms: multivariable output-error state-space (MOESP) identification algorithm and numerical algorithms for subspace state-space system identification (N4SID), so as to obtain the 3rd-order and 4th-order state-space yield models of the SMB chromatographic separation process, respectively. The model predictive control method based on the established state-space models is used in the SMB chromatographic separation process. The influence of different control indicators on the predictive control system response performance is discussed. The output response curves of the yield models were obtained by changing the related parameters so that the yield model parameters are optimized set meanwhile. Finally, the simulation results showed that the yield models are successfully controlled based on the each control period and given yield range.

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