Vibrations are the root cause of many mechanical and civil structure failures. Dynamic characteristics of a structure must be extracted to better understand structural vibrational problems. Modal analysis is used to determine the dynamic characteristics of a system like natural frequencies, damping ratios and mode shapes. Some of the applications of modal analysis include damage detection, design of a structure/machine for dynamic loading conditions and structural health monitoring. The techniques used for modal analysis are experimental modal analysis (EMA), operational modal analysis (OMA) and a less known technique called impact synchronous modal analysis (ISMA), which is a new development. EMA is performed in simulated controlled environment, while OMA and ISMA are performed when the system is in operation. Although EMA is the oldest modal analysis technique, there is an increasing interest in operational modal analysis techniques in recent years. In this paper, operational modal analysis techniques OMA and ISMA are reviewed with their development over the years and their pros and cons discussed.

A review of operational modal analysis techniques for in-service modal identification

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.

https://www.mdpi.com/2076-3417/10/10/3607/pdf

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

Gravity and flexibility will cause fluctuations of the rotation angle in the servo system for flexible manipulators. The fluctuation will seriously affect the motion accuracy of end-effectors. Therefore, this paper adopts a control method combining the RBF (Radial Basis Function) neural network and pole placement strategy to suppress the rotation angle fluctuations. The RBF neural network is used to identify uncertain items caused by the manipulator’s flexibility and the time-varying characteristics of dynamic parameters. Besides, the pole placement strategy is used to optimize the PD (Proportional Differential) controller’s parameters to improve the response speed and stability. Firstly, a dynamic model of flexible manipulators considering gravity is established based on the assumed mode method and Lagrange’s principle. Then, the system’s control characteristics are analyzed, and the pole placement strategy optimizes the parameters of the PD controllers. Next, the control method based on the RBF neural network is proposed, and the Lyapunov stability theory demonstrates stability. Finally, numerical analysis and control experiments prove the effectiveness of the control method proposed in this paper. The means and standard deviations of rotation angle error are reduced by the control method. The results show that the control method can effectively reduce the rotation angle error and improve motion accuracy.

Control Method of Flexible Manipulator Servo System Based on a Combination of RBF Neural Network and Pole Placement Strategy

Output-only modal estimation using sparse component analysis and density-based clustering algorithm

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 applicat...

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

Output-only identification of modal shape coupling in a flexible robot by vector autoregressive modeling

Application of adaptable functional series vector time-dependent autoregressive model for extraction of real modal parameters for identification of time-varying systems

Wenchao Li

Papers

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

Output-only identification of modal shape coupling in a flexible robot by vector autoregressive modeling

Application of adaptable functional series vector time-dependent autoregressive model for extraction of real modal parameters for identification of time-varying systems