An Optimal Sensitivity-Enhancing Feedback Control Approach via Eigenstructure Assignment for Structural Damage Identification

2007 ◽  
Vol 129 (6) ◽  
pp. 771-783 ◽  
Author(s):  
L. J. Jiang ◽  
J. Tang ◽  
K. W. Wang
Keyword(s):  
Feedback Control ◽  
Frequency Shift ◽  
Natural Frequency ◽  
Structural Damage ◽  
Closed Loop ◽  
Damage Identification ◽  
Frequency Measurement ◽  
Sensitivity Enhancement ◽  
Stiffness Reduction ◽  
Structural Damage Identification

The concept of using sensitivity-enhancing feedback control to improve the performance of frequency-shift-based structural damage identification has been recently explored. In previous studies, however, the feedback controller is designed to alter only the closed-loop eigenvalues, and the effect of closed-loop eigenvectors on the sensitivity enhancement performance has not been considered. In this research, it is shown that the sensitivity of the natural frequency shift to the damage in a multi-degree-of-freedom structure can be significantly influenced by the placement of both the eigenvalues and the eigenvectors. A constrained optimization problem is formulated to find the optimal assignment of both the closed-loop eigenvalues and eigenvectors, and then an optimal sensitivity-enhancing control is designed to achieve the desired closed-loop eigenstructure. Another advantage of this scheme is that the dataset of frequency measurement for damage identification can be enlarged by utilizing a series of closed-loop controls, which can be realized by activating different combinations of actuators in the system. Therefore, by using this proposed idea of multiple sensitivity-enhancing feedback controls, we can simultaneously address the two major limitations of frequency-shift-based damage identification: the low sensitivity of frequency shift to damage effects and the deficiency of frequency measurement data. A series of case studies are performed. It is demonstrated that the sensitivity of natural frequency shift to stiffness reduction can be significantly enhanced by using the designed sensitivity-enhancing feedback control, where the optimal placement of closed-loop eigenvectors plays a very important role. It is further verified that such sensitivity enhancement can directly benefit the damage identification accuracy and robustness.

Tuning Criterion of Variable Piezoelectric Circuitry for Frequency-Shift-Based Damage Identification Enhancement

Aerospace ◽  
2006 ◽  
Author(s):  
L. J. Jiang ◽  
J. Tang ◽  
K. W. Wang
Keyword(s):  
Frequency Shift ◽  
Piezoelectric Transducer ◽  
Structural Damage ◽  
Damage Identification ◽  
Measurement Data ◽  
Frequency Measurement ◽  
Damage Effect ◽  
Plate Structures ◽  
Curve Veering ◽  
Selection Of

A new concept of using piezoelectric transducer circuitry with tunable inductance to enhance the performance of frequency-shift-based damage identification method has been recently proposed. While previous work has shown that the frequency-shift information used for damage identification can be significantly enriched by tuning the inductance in the piezoelectric circuitry, a fundamental issue of this approach, namely, how to tune the inductance to best enhance the damage identification performance, has not been addressed. Therefore, this research aims at advancing the state-of-the-art of such a technology by proposing guidelines to form favorable inductance tuning such that the enriched frequency measurement data can effectively capture the damage effect. Our analysis shows that when the inductance is tuned to accomplish eigenvalue curve veering, the change of system eigenvalues induced by structural damage will vary significantly with respect to the change of inductance. Under such curve veering, one may obtain a series of frequency-shift data with different sensitivity relations to the damage, and thus the damage characteristics can be captured more effectively and completely. When multiple tunable piezoelectric transducer circuitries are integrated with the mechanical structure, multiple eigenvalue curve veering can be simultaneously accomplished between desired pairs of system eigenvalues. An optimization scheme aiming at achieving desired set of eigenvalue curve veering is formulated to find the critical inductance values that can be used to form the favorable inductance tuning for multiple piezoelectric circuitries. In the numerical analyses of damage identification, an iterative second-order perturbation-based algorithm is used to identify damages in beam and plate structures. Numerical results show that the performance of damage identification is significantly affected by the selection of inductance tuning, and only when the favorable inductance tuning is used, the locations and severities of structural damages can be accurately identified.

scholarly journals A REVIEW PAPER ON STRUCTURAL DAMAGE IDENTIFICATION OF CANTILEVER BEAM BY USING VIBRATION TECHNIQUE, FINITE ELEMENT ANALYSIS & ARTIFICIAL NEURAL NETWORK

2015 ◽  
Vol 3 (12) ◽  
pp. 125-128
Author(s):  
Aakanksha MohanraoGarud ◽  
V. G. Bhamre
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Artificial Neural Network ◽  
Structural Damage ◽  
Review Paper ◽  
Damage Identification ◽  
Frequency Measurement ◽  
Element Analysis ◽  
Structural Damage Identification ◽  
Artificial Neural

In this review paper structural damage identification work in cantilever beam is done by using the Artificial Neural Network as diagnostic parameter. The study is based on the concept that natural frequency is inversely proportional to the mass of the structure. Thus to regulate the proper condition of structure, periodical frequency measurement is necessary. But in dynamic conditions and in complicated structures frequency measurement is difficult, for the same we reviewed various papers to identify the structural damage using various methods. The factors which affects on the damage of structural parts like crack depth, crack location etc. is also discussed in this work. Natural frequency is measured with the help of fast fourier transform by various authors and artificial neural network is also used for identification of the damage in many papers. So in this review work we studied methods of structural damage identification such as vibrations, finite element analysis and artificial neural network.

scholarly journals Development and application of a deep learning–based sparse autoencoder framework for structural damage identification

2018 ◽  
Vol 18 (1) ◽  
pp. 103-122 ◽  
Author(s):  
Chathurdara Sri Nadith Pathirage ◽  
Jun Li ◽  
Ling Li ◽  
Hong Hao ◽  
Wanquan Liu ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Structural Damage ◽  
Damage Identification ◽  
Experimental Studies ◽  
Measurement Data ◽  
Frame Structure ◽  
Stiffness Reduction ◽  
Highly Nonlinear ◽  
Structural Damage Identification ◽  
Sparse Autoencoder

This article proposes a deep sparse autoencoder framework for structural damage identification. This framework can be employed to obtain the optimal solutions for some pattern recognition problems with highly nonlinear nature, such as learning a mapping between the vibration characteristics and structural damage. Three main components are defined in the proposed framework, namely, the pre-processing component with a data whitening process, the sparse dimensionality reduction component where the dimensionality of the original input vector is reduced while preserving the required necessary information, and the relationship learning component where the mapping between the compressed dimensional feature and the stiffness reduction parameters of the structure is built. The proposed framework utilizes the sparse autoencoders based deep neural network structure to enhance the capability and performance of the dimensionality reduction and relationship learning components with a pre-training scheme. In the final stage of training, both components are jointly optimized to fine-tune the network towards achieving a better accuracy in structural damage identification. Since structural damages usually occur only at a small number of elements that exhibit stiffness reduction out of the large total number of elements in the entire structure, sparse regularization is adopted in this framework. Numerical studies on a steel frame structure are conducted to investigate the accuracy and robustness of the proposed framework in structural damage identification, taking into consideration the effects of noise in the measurement data and uncertainties in the finite element modelling. Experimental studies on a prestressed concrete bridge in the laboratory are conducted to further validate the performance of using the proposed framework for structural damage identification.

Optimal Sensor Placement Algorithm for Structural Damage Identification

2020 ◽  
Vol 14 (1) ◽  
pp. 69-81
Author(s):  
C.H. Li ◽  
Q.W. Yang
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Sensor Placement ◽  
Optimization Procedure ◽  
Frame Structure ◽  
Truss Structures ◽  
Optimal Sensor Placement ◽  
Structural Damage Identification ◽  
Placement Algorithm ◽  
Sensor Locations

Background: Structural damage identification is a very important subject in the field of civil, mechanical and aerospace engineering according to recent patents. Optimal sensor placement is one of the key problems to be solved in structural damage identification. Methods: This paper presents a simple and convenient algorithm for optimizing sensor locations for structural damage identification. Unlike other algorithms found in the published papers, the optimization procedure of sensor placement is divided into two stages. The first stage is to determine the key parts in the whole structure by their contribution to the global flexibility perturbation. The second stage is to place sensors on the nodes associated with those key parts for monitoring possible damage more efficiently. With the sensor locations determined by the proposed optimization process, structural damage can be readily identified by using the incomplete modes yielded from these optimized sensor measurements. In addition, an Improved Ridge Estimate (IRE) technique is proposed in this study to effectively resist the data errors due to modal truncation and measurement noise. Two truss structures and a frame structure are used as examples to demonstrate the feasibility and efficiency of the presented algorithm. Results: From the numerical results, structural damages can be successfully detected by the proposed method using the partial modes yielded by the optimal measurement with 5% noise level. Conclusion: It has been shown that the proposed method is simple to implement and effective for structural damage identification.

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