Diagnostic system for structural damage and degradataion: damage identification based on a mode parameter and wave propagation

2003 ◽  
Author(s):  
Koji Tsuchimoto ◽  
Toshiyuki Ikeshita ◽  
Yoshikazu Kitagawa
Keyword(s):  
Wave Propagation ◽  
Structural Damage ◽  
Damage Identification ◽  
Diagnostic System ◽  
Mode Parameter

An automated damage identification technique based on vibration and wave propagation data

2006 ◽  
Vol 365 (1851) ◽  
pp. 479-491 ◽  
Author(s):  
Ajit Mal ◽  
Sauvik Banerjee ◽  
Fabrizio Ricci
Keyword(s):  
Wave Propagation ◽  
Dynamic Response ◽  
Structural Damage ◽  
Damage Identification ◽  
Damage Index ◽  
Composite Plates ◽  
Ambient Conditions ◽  
Current State ◽  
Potential Applications ◽  
Identification Technique

This paper is concerned with the detection and characterization of hidden defects in advanced structures before they grow to a critical size. A novel method is developed using a combination of vibration and wave propagation data to determine the location and degree of damage in structural components requiring minimal operator intervention. The structural component is to be instrumented with an array of actuators and sensors to excite and record its dynamic response. A damage index, calculated from the measured dynamic response of the structure in a reference state (baseline) and the current state, is introduced as a determinant of structural damage. The index is a relative measure comparing the two states of the structure under the same ambient conditions. The indices are used to identify damages in the forms of delaminations and holes in composite plates for different arrangements of the source and the receivers. The potential applications of the approach in developing health monitoring systems in defects-critical structures are discussed.

Structural Assessment System for Damage and Degradation: Two-Stage Damage Identification Based on Neural Networks and Improved MDLAC Method

2004 ◽  
Author(s):  
Koji Tsuchimoto ◽  
Naoaki Wada ◽  
Yoshikazu Kitagawa
Keyword(s):  
Neural Networks ◽  
Monitoring System ◽  
Structural Damage ◽  
Structural Integrity ◽  
Damage Identification ◽  
Diagnostic System ◽  
Structural Condition ◽  
Smart Structure ◽  
Assessment System ◽  
Detection Methods

A “smart” structure has many functions, including monitoring, repairing, shape formation, and learning. Recently, interest in applying a monitoring system to structures for quality assurance and for evaluating seismic risk has been strong. Monitoring system is useful to diagnose the structural condition, and detect structural damage and degradation. In this study, we developed a monitoring system to assess the structural integrity. This system includes a diagnostic system for structural damage and degradation based on neural networks and improved MDLAC method, say, to detect the damage sites globally by applying neural networks and then to narrow the damage sites by using improved MDLAC method. To validate this system, we then use the 5-story structure in which the beams are fixed at both ends in order to confirm the performance of our proposal damage detection methods. As a result, it is pointed out that there are some possibilities to confirm the diagnostic system by utilizing these two methods.

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.

Sparse damage detection via the elastic net method using modal data

2021 ◽  
pp. 147592172110219
Author(s):  
Rongrong Hou ◽  
Xiaoyou Wang ◽  
Yong Xia
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Damage Identification ◽  
Measurement Data ◽  
Elastic Net ◽  
Sensitivity Matrix ◽  
Regularization Technique ◽  
Modal Data ◽  
Regularization Techniques ◽  
Net Method

The l1 regularization technique has been developed for damage detection by utilizing the sparsity feature of structural damage. However, the sensitivity matrix in the damage identification exhibits a strong correlation structure, which does not suffice the independency criteria of the l1 regularization technique. This study employs the elastic net method to solve the problem by combining the l1 and l2 regularization techniques. Moreover, the proposed method enables the grouped structural damage being identified simultaneously, whereas the l1 regularization cannot. A numerical cantilever beam and an experimental three-story frame are utilized to demonstrate the effectiveness of the proposed method. The results showed that the proposed method is able to accurately locate and quantify the single and multiple damages, even when the number of measurement data is much less than the number of elements. In particular, the present elastic net technique can detect the grouped damaged elements accurately, whilst the l1 regularization method cannot.

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