Vibration based methods

This study focuses on the problem of vibration-based damage detection for a population of like structures. Although nominally identical, like structures exhibit variability in their characteristics due to variability in the materials and manufacturing. This inevitably leads to variability in the dynamics, which may be so significant as to mask deviations due to damage. Damage detection, conventionally employing a single nominal (baseline) model for the population, thus becomes challenging, with available methods exhibiting inadequate performance. The use of a new, Multiple Model (MM) framework, is presently explored for the potential alleviation of the problem. Within it, four response-only methods are considered and exhaustively tested via a population of laboratory-scale like composite beams and delamination type damage. The results illustrate the improvement in performance attained within the postulated framework, with one parametric method achieving impressive performance characteristics. The general conclusion is that the MM framework offers some new and exciting possibilities for effectively tackling the problem of damage detection for a population of like structures.

This work aims at the precise assessment of a recently introduced method that, in addition to damage detection, allows for complete and accurate damage identification (localization) and magnitude estimation. The method is based on Vector–dependent Functionally Pooled (VFP) models and is capable of offering an effective and precise solution in a unified framework. The effectiveness of the method is experimentally assessed via its application to a prototype GARTEURtype laboratory scale aircraft structure.

Monitoring structural damage is extremely important for sustaining and preserving the service life of civil structures. While successful monitoring provides resolute and staunch information on the health, serviceability, integrity and safety of structures; maintaining continuous performance of a structure depends highly on monitoring the occurrence, formation and propagation of damage. Damage may accumulate on structures due to different environmental and human-induced factors. Numerous monitoring and detection approaches have been developed to provide practical means for early warning against structural damage or any type of anomaly. Considerable effort has been put into vibration-based methods, which utilize the vibration response of the monitored structure to assess its condition and identify structural damage. Meanwhile, with emerging computing power and sensing technology in the last decade, Machine Learning (ML) and especially Deep Learning (DL) algorithms have become more feasible and extensively used in vibration-based structural damage detection with elegant performance and often with rigorous accuracy. While there have been multiple review studies published on vibration-based structural damage detection, there has not been a study where the transition from traditional methods to ML and DL methods are described and discussed. This paper aims to fulfill this gap by presenting the highlights of the traditional methods and provide a comprehensive review of the most recent applications of ML and DL algorithms utilized for vibration-based structural damage detection in civil structures.

Monitoring structural damage is extremely important for sustaining and preserving the service life of civil structures. While successful monitoring provides resolute and staunch information on the health, serviceability, integrity and safety of structures; maintaining continuous performance of a structure depends highly on monitoring the occurrence, formation and propagation of damage. Damage may accumulate on structures due to different environmental and human-induced factors. Numerous monitoring and detection approaches have been developed to provide practical means for early warning against structural damage or any type of anomaly. Considerable effort has been put into vibration-based methods, which utilize the vibration response of the monitored structure to assess its condition and identify structural damage. Meanwhile, with emerging computing power and sensing technology in the last decade, Machine Learning (ML) and especially Deep Learning (DL) algorithms have become more feasible and extensively used in vibration-based structural damage detection with elegant performance and often with rigorous accuracy. While there have been multiple review studies published on vibration-based structural damage detection, there has not been a study where the transition from traditional methods to ML and DL methods are described and discussed. This paper aims to fulfill this gap by presenting the highlights of the traditional methods and provide a comprehensive review of the most recent applications of ML and DL algorithms utilized for vibration-based structural damage detection in civil structures.

In this study vibration-based delamination detection is achieved using artificial immune system (AIS) method. The approach is based upon mimicking immune recognition mechanisms that possess features such as adaptation, evolution, and immune learning. The delamination location and length in composite beam identified as an optimization problem. The cost function based on differences between analytical or experimental natural frequencies and predicted natural frequencies by AIS method. Analytical natural frequencies of delaminated beam obtained from Euler-Bernoulli beam theory with constrained delamination mode. Errors of predicted location and length are 2.16% and 0.1968% respectively, using the AIS algorithm. To investigate the accuracy of the proposed method some experimental results were extracted. A laser vibrometer used to found out natural frequencies change in delaminated CFRP composite beam case study. The average values of location and length errors are 18.7% and 9.8% respect...

Composite materials are widely used in aeronautical, marine and automotive industries, because of their excellent mechanical properties, low density and ease of manufacture. However, composite laminates are susceptible to delaminations, which may not be visible externally, but can substantially affect the performance of the structure. The final objective of this research is to develop a Structural Health Monitoring (SHM) system based on vibration monitoring, to detect, locate and assess delamination damage in laminated composite structures. Towards this end, finite element modelling is employed to simulate the dynamic response of composite laminates (beams and plates) with delamination and extract their vibration parameters. According to the reported theoretical models, we built two finite element (FE) models using Ansys, one free and the other constrained. Modal analysis is applied to the free FE model to extract eigen frequencies while transient analysis is used to get natural frequencies from theconstrained model where contact elements haven't been ignored as in the former. The natural frequencies extracted from the current numerical simulations are compared with previous theoretical, numerical and experimental results as well as from our experimental modal analysis conducted on composite beams with delaminations.

This work aims at the precise assessment of a recently introduced method that, in addition to damage detection, allows for complete and accurate damage identification (localization) and magnitude estimation. The method is based on Vector–dependent Functionally Pooled (VFP) models and is capable of offering an effective and precise solution in a unified framework. The effectiveness of the method is experimentally assessed via its application to a prototype GARTEURtype laboratory scale aircraft structure.

Vibration based damage detection methods are among the most popular and promising approaches for health monitoring of structures. In this work a critical review of different methods for damage detection methods of structures is presented. The theoretical bases of the most popular methods based on the changes in the modal properties of the structures are deduced. The review includes the modal displacements, the mode shape slopes, the modal curvatures and the strain energy methods. The efficiency of all these methods is compared by using a finite element analysis of intact and damaged beams. The methods are tested experimentally by using a scanning laser vibrometer to measure the modal properties of specially prepared composite beams with defects. All this methods are compared with the damage detection method based on the analysis of the Poincaré maps of the motion of the structures). Conclusions concerning the advantages and the applicability of the considered methods are deduced.

In recent years, structural health monitoring is gaining high importance in conjunction with damage assessment and safety evaluation of structures. Delamination is a common type of damage in fibre reinforced composite laminates used for mechanical and aerospace structures. It is usually an internal and hidden type of damage which may lead to catastrophic failure without prior warning. Early detection of this type of damage is essential. The damage detection techniques proposed here use the displacement mode shapes of the beam, which are then converted to respective curvature modes and modal strain energy. The damage indices based on these parameters are also evaluated for locating the delamination after applying gapped smoothing techniques to the curvature mode shapes. This work presents a numerical study on detection of delamination damage in composite beams, where the proposed methodology is applied for identifying delamination in a three-dimensional numerical finite element model of a composite beam.