Single Damage Locations Research Articles

Research on damage monitoring of sandwich armor composite structure based on time reversal principle

Abstract With the wide application of lightweight armor composite, it is of great significance to monitor delamination damage in laminated materials. Ultrasonic testing often needs to be immersed in liquid for monitoring, which is inconvenient for large-size specimens and cannot be monitored in real time. In structural health monitoring, the corresponding relationship between damage and response signal characteristics (such as amplitude and energy) is established, which is greatly affected by various environmental factors (such as temperature and load). Due to the particularity of the multi-layer composite structure with large thickness, the problem of Lamb wave multi-mode and frequency dispersion affects the accuracy of damage monitoring, which brings great difficulty to the accuracy of damage monitoring. Four piezoelectric sensors were arranged on the surface of the 5 mm sandwich armor composite composed of alumina ceramic, T700 carbon fiber and TC4 titanium alloy. The signal-to-noise ratio of the obtained damage scattering signal was improved by time reversal method and Gabor wavelet transform. Monitoring and imaging of single damage and multiple damage on the reverse side of armor composite were studied. The experimental results show that the radial error of single damage location is 4.24 mm, and the radial error of multiple damage location is 5.39 mm.

A Damage Localization Approach for Rahmen Bridge Based on Convolutional Neural Network

Damage localization is the process of detecting the location of damage using a structural health monitoring system. However, existing damage localization methods cannot be used for localizing the damage of bridges in real time because of their slow testing speed. Thus, in this study a damage localization approach was developed using a convolutional neural network (CNN). To develop the CNN model for damage localization, simulation data was generated through a numerical model of a reinforced concrete Rahmen bridge with static loading conditions. The proposed CNN-based approach aims to identify 12 single damage locations or no damage. The approach was trained and tested with three different data set generated with three damage severities, and it was possible to estimate the damage location with an accuracy of 87.3% when the damage severity in the bridge is serious. The results showed that the deep learning has the potential to overcome the limitations of existing damage localization techniques.

A method of curvature mode difference and wavelet transform to damage detection on space grid structure

A method combined curvature mode difference and wavelet transformation was used to determine the damage location of space gird structure. This method considered the curvature mode difference of pre and post damage as the wavelet transform analysis signal. Moreover, these signals were analyzed by wavelet transform using wavelet function db3. As the wavelet transform coefficient occur sudden change, the damage locations of links will be obtained. In this paper, square pyramid space grid structure, orthogonal space truss structure, three way latticed grid and honeycomb triangular pyramid grid structure will be analyzed using this method to identify single damage and multi-damage location. The solutions from numerical simulations show that this method can take effective identification of single damage location and multi-damage location of square pyramid space grid structure by the first mode condition. And this method has versatility to detect damages from different space grid structures.

Statistical Performance Assessment of an NDE-Based SHM-DP Methodology for the Remaining Fatigue Life Prediction of Monitored Structural Components and Systems

Integrated structural health monitoring and damage prognosis (SHM-DP) methodologies, coupled with sensor-based nondestructive evaluation (NDE) techniques, are becoming increasingly important for the near-real-time condition assessment (i.e., SHM) and future performance predictions (i.e., DP) of aging mechanical systems, civil structures, and infrastructure networks, as well as automotive, naval, and aerospace vehicles. A successful SHM-DP strategy, capable of identifying all critical damage mechanisms while accounting for all relevant sources of uncertainty, can be used as an advanced tool to effectively and optimally manage the life-cycle of the monitored system, recursively forecast its remaining useful life (RUL), and ultimately reduce the overall ownership cost through dynamic reliability-based inspection and maintenance (RBIM) plans, system downtime minimization, catastrophic failure prevention, and potential RUL extension. In this perspective, fatigue damage propagation is one of the most critical and unpredictable deterioration processes for a large variety of structural and mechanical systems that are subjected repeatedly to cyclic and/or random operational loading during their service life. Within this limited scope, the authors developed a comprehensive NDE-based SHM-DP framework for recursively predicting the time-varying system reliability and the remaining fatigue life (RFL) of monitored systems subjected to deterioration by multi-site fatigue damage propagation. This paper provides a brief overview of the proposed framework and then uses a set of experimental fatigue test data to perform a thorough statistical performance assessment of the developed methodology at the local reliability component level (i.e., single damage mechanism and single damage location) including NDE detectability and measurement uncertainty as well as both load and model parameter uncertainty.

A recursive Bayesian approach for fatigue damage prognosis: An experimental validation at the reliability component level

Fatigue-induced damage is one of the most uncertain and highly unpredictable failure mechanisms for a large variety of mechanical and structural systems subjected to cyclic and random loads during their service life. A health monitoring system capable of (i) monitoring the critical components of these systems through non-destructive evaluation (NDE) techniques, (ii) assessing their structural integrity, (iii) recursively predicting their remaining fatigue life (RFL), and (iv) providing a cost-efficient reliability-based inspection and maintenance plan (RBIM) is therefore ultimately needed. In contribution to these objectives, the first part of the paper provides an overview and extension of a comprehensive reliability-based fatigue damage prognosis methodology — previously developed by the authors — for recursively predicting and updating the RFL of critical structural components and/or sub-components in aerospace structures. In the second part of the paper, a set of experimental fatigue test data, available in the literature, is used to provide a numerical verification and an experimental validation of the proposed framework at the reliability component level (i.e., single damage mechanism evolving at a single damage location). The results obtained from this study demonstrate (i) the importance and the benefits of a nearly continuous NDE monitoring system, (ii) the efficiency of the recursive Bayesian updating scheme, and (iii) the robustness of the proposed framework in recursively updating and improving the RFL estimations. This study also demonstrates that the proposed methodology can lead to either an extent of the RFL (with a consequent economical gain without compromising the minimum safety requirements) or an increase of safety by detecting a premature fault and therefore avoiding a very costly catastrophic failure.

Damage detection for beam-like structures using the normalized curvature of a uniform load surface

This paper presents a new vibration-based damage detection method for beam-like structures that uses the normalized uniform load surface (NULS) curvature obtained by modal flexibility. Analytical studies on the NULS curvature method for beam-like structures, which follow Bernoulli–Euler beam theory, have shown that changes in NULS curvature only occur at damaged elements and not at intact ones because the internal forces induced by damage only act on the damaged elements and not on the undamaged elements. Therefore, computing the changes in NULS curvature set indicating only damaged elements at a normalized level is central to the approach. Also, a damage index is proposed based on outlier analysis to account for measurement noise. In order to confirm the feasibility of the proposed method, a cantilever beam and a simply supported beam were numerically investigated for two damage scenarios by using modal parameters obtained by eigenvalue analysis and simulations of an impact test using MATLAB/Simulink. The results showed that the proposed method could accurately localize multiple damage locations as well as single damage locations without any false-positive or false-negative detections. For comparison, damage detection was also conducted using the uniform load surface (ULS) curvature method and the mode shape curvature method. The ULS curvature method clearly identified single damage locations but some missed multiple damage locations. For the mode shape curvature method, it was shown that the false-positive and false-negative detections were performed at several damaged or undamaged locations. The comparison showed that the proposed detection method can more effectively identify single and multiple damage locations than the other two methods. In conclusion, the proposed method performed better in detecting damages than the other two methods in terms of sensitivity to damage regardless of location and robustness against noisy signals generated from calculating the mode shape curvature.

A fuzzy logic-based damage identification method for simply-supported bridge using modal shape ratios

Abstract A fuzzy logic system (FLS) is established for damage identification of simply supported bridge. A novel damage indicator is developed based on ratios of mode shape components between before and after damage. Numerical simulation of a simply-supported bridge is presented to demonstrate the memory, inference and anti-noise ability of the proposed method. The bridge is divided into eight elements and nine nodes, the damage indicator vector at characteristic nodes is used as the input measurement of FLS. Results reveal that FLS can detect damage of training patterns with an accuracy of 100%. Aiming at other test patterns, the FLS also possesses favorable inference ability, the identification accuracy for single damage location is up to 93.75%. Tests with noise simulated data show that the FLS possesses favorable anti-noise ability.

Theory of inner product vector and its application to multi-location damage detection

Structural damage detection methods using time domain vibration responses have shown appeal in recent years. In previous papers by the authors, the inner product vector (IPV) was proposed as a damage detection algorithm which uses cross correlation functions between vibration responses under white noise excitation or band pass white noise excitation. The proposed algorithm was verified by some simulative and experimental examples featuring a single damage location. However, damage at multiple locations was not considered. Therefore, this paper extends the application of IPV-based structural damage detection to the problem of multiple damage locations. Firstly, the theory of the IPV and its implementation in a damage detection context is reviewed. Then, two strategies for detecting multiple damages utilizing IPV are proposed. Finally, a damage detection experiment of a honeycomb sandwich composite beam is adopted to illustrate the feasibility and effectiveness of the IPV-based damage detection method.

A parameter subset selection method using residual force vector for detecting multiple damage locations

An improved parameter subset selection method based on a dynamic residual force measure is proposed for damage localization. Often the original subset selection using the fundamental modal characteristics requires a single damage location and the use of many smaller model updating processes for detecting multiple damage locations. A new approach to subset selection proposed herein is demonstrated to be efficient for identifying multiple damaged elements. Cases in which damage detection must be achieved using incomplete measurements of the structural responses are also considered. Modal expansion is incorporated to deal with this challenge. An advantage of employing this new approach is that it can reliably identify multiple damage locations. The effect of measurement noise on the performance of the proposed subset selection method has also been investigated. Through a series of illustrative examples considering different types of structures, the proposed method is demonstrated and shown to be very efficient in detecting multiple damage locations. Copyright © 2008 John Wiley & Sons, Ltd.