A generalized flexibility matrix based approach for structural damage detection

In this paper, a new structural damage detection approach based on changes in the generalized flexibility matrix is presented. The generalized flexibility matrix is first introduced; its sensitivity and change are then used to detect structural damage location and damage extent. Compared with the original flexibility matrix based approach, the effect of truncating higher-order modes can be considerably reduced in this new approach. Finally, a numerical example for a simply supported beam is used to illustrate the effectiveness of this proposed method.     

Automatic detection of impact damage in carbon fiber composites using active thermography

Accidental impacts can severely reduce the structural strength and stability of composite materials, which can lead to severe consequences due to the degradation of the mechanical properties of components designed to perform for decades. Because accidental impacts are difficult to avoid, robust and reliable inspection methods to detect impact damage are required. Many methods have been proposed recently. However, most of them require an experienced technician to analyze the data, which leads to a significant decrease in manufacturing productivity. This work proposes a method to automatically detect impact damage in carbon fiber composites using active thermography. The proposed system detects defects caused by impact damage in the infrared images without human intervention. Impact damage detection is performed using a robust method based on an active thermographic inspection. Thermographic data is preprocessed to improve signal-to-noise ratio and to remove non-uniform background caused by non-uniform heating. Then, peaks and edges are identified and clustered, and regions corresponding to impact damage are detected. The proposed procedure has been applied to three specimens that contain 6 and 12 plies, different types of cores, and damage caused by energies from 6 J to 50 J. All defects are detected correctly.     

A Bayesian approach to characterising multi-phase flows using magnetic resonance: application to bubble flows

Magnetic Resonance (MR) imaging is difficult to apply to multi-phase flows due to both the inherently short T?* characterising such systems and the relatively long time taken to acquire the data. We develop a Bayesian MR approach for analysing data in k-space that eliminates the need for image acquisition, thereby significantly extending the range of systems that can be studied. We demonstrate the technique by measuring bubble size distributions in gas-liquid flows. The MR approach is compared with an optical technique at a low gas fraction (～2%), before being applied to a system where the gas fraction is too high for optical measurements (～15%).     

A Bayesian approach to seafloor classification using multi-beam echo-sounder backscatter data

Seafloor classification using acoustic remote sensing techniques is an attractive approach due to its high-coverage capabilities and limited costs. The multi-beam echo-sounder (MBES) system provides high-resolution bathymetry and backscatter information with 100% coverage. In this paper, we present a seafloor classification method that employs the MBES backscatter data. The method uses the averaged backscatter data per beam. It, therefore, is independent on the quality of the MBES calibration. Also, its performance is insensitive to seafloor type variation along the MBES swathe and corrections for the angular dependence of the backscatter are not needed. The method accounts for the ping-to-ping variability of the backscatter intensity. It estimates both the number of seafloor types present in the survey area and the probability density function for the backscatter strength at a certain angle for each of the seafloor types. Application of the method to MBES backscatter data acquired in a well-known test area in the North Sea shows very good agreement with available ground truth. The method’s discriminatory performance for this area is demonstrated to be comparable to that of taking samples of the sediment. All seafloor types known to be present in the area are resolved for. Application of the method to the Stanton bank data set shows clearly separable areas that differ in seafloor composition.     

GUW-based structural damage detection using WPT statistical features and multiclass SVM

Recently, guided ultrasonic waves (GUW) are widely used for damage detection in structural health monitoring (SHM) of different engineering structures. In this study, an intelligent damage detection method is proposed to be used in SHM applications. At first, GUW signal is de-noised by discrete wavelet transform (DWT). After that, wavelet packet transform (WPT) is employed to decompose the de-noised signal and the statistical features of decomposed packets are extracted as damage-sensitive features. Finally, a multiclass support vector machine (SVM) classifier is used to detect the damage and estimate its severity. The proposed method is employed for GUW-based structural damage detection of a thick steel beam. The effects of different parameters on the sensitivity of the method are surveyed. Furthermore, by comparing with some other similar algorithms, the performance of the proposed method is verified. The experimental results demonstrate that the proposed method can appropriately detect a structural damage and estimate its severity.     

Sequential structural damage diagnosis algorithm using a change point detection method

This paper introduces a damage diagnosis algorithm for civil structures that uses a sequential change point detection method. The general change point detection method uses the known pre- and post-damage feature distributions to perform a sequential hypothesis test. In practice, however, the post-damage distribution is unlikely to be known a priori, unless we are looking for a known specific type of damage. Therefore, we introduce an additional algorithm that estimates and updates this distribution as data are collected using the maximum likelihood and the Bayesian methods. We also applied an approximate method to reduce the computation load and memory requirement associated with the estimation. The algorithm is validated using a set of experimental data collected from a four-story steel special moment-resisting frame and multiple sets of simulated data. Various features of different dimensions have been explored, and the algorithm was able to identify damage, particularly when it uses multidimensional damage sensitive features and lower false alarm rates, with a known post-damage feature distribution. For unknown feature distribution cases, the post-damage distribution was consistently estimated and the detection delays were only a few time steps longer than the delays from the general method that assumes we know the post-damage feature distribution. We confirmed that the Bayesian method is particularly efficient in declaring damage with minimal memory requirement, but the maximum likelihood method provides an insightful heuristic approach.     

Structural damage detection using cross correlation functions of vibration response

Structural damage detection methods based on vibration responses are appealing for a variety of reasons such as their potential to observe damage from sensors placed remote from an unknown damage site. Of particular interest to the authors is online damage detection in which changes in the structure can be flagged up in an automated fashion by permanently installed transducers. In a previous paper by the authors, the inner product vector (IPV) was proposed as a damage detection algorithm which uses cross correlation functions between response measurements. Implicitly assumed in the formulation is that the response quantity is that of displacement resulting from white noise excitation. In this paper, the IPV technique is first reviewed and then generalised to address velocity and acceleration response to band pass white noise excitation. It is shown that the IPV is a weighted summation of the mode shapes, and the effect of some particular measurement noise on the IPV can be adaptively eliminated in the calculation of IPV. Then, the damage detection method based on changes in the IPV is proposed. Finally, damage detection experiments of shear frame structure, honeycomb sandwich composite beam and aircraft stiffened panel are presented to illustrate the feasibility and effectiveness of the proposed method.     

Airframe structural damage detection: a non-linear structural surface intensity based technique

The non-linear structural surface intensity (NSSI) based damage detection technique is extended to airframe applications. The selected test structure is an upper cabin airframe section from a UH-60 Blackhawk helicopter (Sikorsky Aircraft, Stratford, CT). Structural damage is simulated through an impact resonator device, designed to simulate the induced vibration effects typical of non-linear behaving damage. An experimental study is conducted to prove the applicability of NSSI on complex mechanical systems as well as to evaluate the minimum sensor and actuator requirements. The NSSI technique is shown to have high damage detection sensitivity, covering an extended substructure with a single sensing location.     

Structural damage detection of controlled building structures using frequency response functions

If a building structure requires both a vibration control system and a health monitoring system, the integration of the two systems will be cost-effective and beneficial. One of the key problems of this integrated system is how to use control devices to enhance system identification and damage detection. This paper presents a new method for system identification and damage detection of controlled building structures equipped with semi-active friction dampers through model updating based on frequency response functions. The two states of the building are first created by adding a known stiffness using semi-active friction dampers. A scheme based on the frequency response functions of the two states of the building is then presented to identify stiffness parameters of structural members in consideration of structural connectivity and transformation information. By applying the proposed model updating scheme to the damaged building, a damage detection scheme is proposed based on the identified stiffness parameters of structural members of both the original and damaged buildings. The feasibility of the proposed schemes is finally demonstrated through a detailed numerical investigation in terms of an example building, in which the effects of measurement noise and excitation conditions are discussed. The numerical results clearly show that the proposed method can locate and quantify damage satisfactorily even though measurement noise is taken into consideration.     

A Bayesian Networks approach to Operational Risk

A system for Operational Risk management based on the computational paradigm of Bayesian Networks is presented. The algorithm allows the construction of a Bayesian Network targeted for each bank and takes into account in a simple and realistic way the correlations among different processes of the bank. The internal losses are averaged over a variable time horizon, so that the correlations at different times are removed, while the correlations at the same time are kept: the averaged losses are thus suitable to perform the learning of the network topology and parameters; since the main aim is to understand the role of the correlations among the losses, the assessments of domain experts are not used. The algorithm has been validated on synthetic time series. It should be stressed that the proposed algorithm has been thought for the practical implementation in a mid or small sized bank, since it has a small impact on the organizational structure of a bank and requires an investment in human resources which is limited to the computational area.     

A local damage detection approach based on restoring force method

Chain-like systems have been studied by many researchers for their simple structure and wide range of application. Previously, the damage in a chain-like system was detected by the reduction of the mass-normalized stiffness coefficient for certain elements as reported by Nayeri et al. (2008 [16]). However, some shortcomings exist in that approach and for overcoming them; an improved approach is derived and presented in this paper. In our improved approach, the mass normalized stiffness coefficients under two states (baseline state and potentially damaged state) are first estimated by a least square method, then these mass-stiffness coupled coefficients are decoupled to derive stiffness and mass relative change ratios for individual elements. These ratios are assembled in a vector, which is defined as damage indication vector (DIV). Each component in DIV is normalized individually to one to get multiple solutions. These solutions are averaged for estimating relative system changes, while abnormal solutions are discarded. The work of judging a solution as normal or abnormal is done by a cluster analysis algorithm. The most intriguing merit of this improved approach is that the relative stiffness and mass changes, which are coupled in the previous approach, can be separately identified. By this approach, the damage (single or multiple) extent and location can be correctly detected under operational conditions, meanwhile the proposed damage index has a clear physical meaning and is directly related to the stiffness reduction of corresponding structural elements. For illustrating the effectiveness and robustness of the improved approach, numerical simulation of a four floor building was carried out and experimental data from a structure tested at the Los Alamos National Laboratory was employed. Identified structural changes with both simulation and experimental data properly indicated the location and extent of actual structural damage, which validated the proposed approach.     

Flaw detection in composites using time-average shearography

Results on flaw detection of glass fibre reinforced plastic beams using time- average shearography are presented here. Detection and sizing of flaws such as debonds or delaminations are successfully carried out using this technique. For easy detection of flaws, the component has to be excited at the resonance frequencies of the flaws. As the flaw gets smaller, a higher frequency is required. For flaws of the same exterior size, a deeper one will also require a higher frequency.     

A new method for spectral decomposition using a bilinear Bayesian approach

A frequent problem in analysis is the need to find two matrices, closely related to the underlying measurement process, which when multiplied together reproduce the matrix of data points. Such problems arise throughout science, for example, in imaging where both the calibration of the sensor and the true scene may be unknown and in localized spectroscopy where multiple components may be present in varying amounts in any spectrum. Since both matrices are unknown, such a decomposition is a bilinear problem. We report here a solution to this problem for the case in which the decomposition results in matrices with elements drawn from positive additive distributions. We demonstrate the power of the methodology on chemical shift images (CSI). The new method, Bayesian spectral decomposition (BSD), reduces the CSI data to a small number of basis spectra together with their localized amplitudes. We apply this new algorithm to a 19F nonlocalized study of the catabolism of 5-fluorouracil in human liver, 31P CSI studies of a human head and calf muscle, and simulations which show its strengths and limitations. In all cases, the dataset, viewed as a matrix with rows containing the individual NMR spectra, results from the multiplication of a matrix of generally nonorthogonal basis spectra (the spectral matrix) by a matrix of the amplitudes of each basis spectrum in the the individual voxels (the amplitude matrix). The results show that BSD can simultaneously determine both the basis spectra and their distribution. In principle, BSD should solve this bilinear problem for any dataset which results from multiplication of matrices representing positive additive distributions if the data overdetermine the solutions.     

Sensitivity analysis of an auto-correlation-function-based damage index and its application in structural damage detection

Structural damage detection using time domain vibration responses has advantages such as simplicity in calculation and no requirement of a finite element model, which attracts more and more researchers in recent years. In present paper, a new approach to detect the damage based on the auto correlation function is proposed. The maximum values of the auto correlation function of the vibration response signals from different measurement points are formulated as a vector called Auto Correlation Function at Maximum Point Value Vector, AMV for short. The relative change of the normalized AMV before and after damage is used as the damage index to locate the damage. Sensitivity analysis of the normalized AMV with respect to the local stiffness shows that the normalized AMV has a sharp change around the local stiffness change location, which means the normalized AMV is a good indicator to detect the damage even when the damage is very small. Stiffness reduction detection of a 12-story frame structure is provided to illustrate the validity, effectiveness and the anti-noise ability of the proposed method. Comparison of the normalized AMV and the other correlation-function-based damage detection method shows the normalized AMV has a better detectability.     

Estimating parameters in stochastic systems: A variational Bayesian approach

This work is concerned with approximate inference in dynamical systems, from a variational Bayesian perspective. When modelling real world dynamical systems, stochastic differential equations appear as a natural choice, mainly because of their ability to model the noise of the system by adding a variation of some stochastic process to the deterministic dynamics. Hence, inference in such processes has drawn much attention. Here a new extended framework is derived that is based on a local polynomial approximation of a recently proposed variational Bayesian algorithm. The paper begins by showing that the new extension of this variational algorithm can be used for state estimation (smoothing) and converges to the original algorithm. However, the main focus is on estimating the (hyper-) parameters of these systems (i.e. drift parameters and diffusion coefficients). The new approach is validated on a range of different systems which vary in dimensionality and non-linearity. These are the Ornstein-Uhlenbeck process, the exact likelihood of which can be computed analytically, the univariate and highly non-linear, stochastic double well and the multivariate chaotic stochastic Lorenz ’63 (3D model). As a special case the algorithm is also applied to the 40 dimensional stochastic Lorenz ’96 system. In our investigation we compare this new approach with a variety of other well known methods, such as the hybrid Monte Carlo, dual unscented Kalman filter, full weak-constraint 4D-Var algorithm and analyse empirically their asymptotic behaviour as a function of observation density or length of time window increases. In particular we show that we are able to estimate parameters in both the drift (deterministic) and the diffusion (stochastic) part of the model evolution equations using our new methods.     

不确实海洋环境下的贝叶斯匹配场处理

讨论了一种不确实海洋环境下贝叶斯匹配场处理方法。该方法在不确实环境下匹配场处理时,对不确实知识进行建模,同时,充分利用阵接收的空时数据,找到了匹配场处理时一种结合模基和数据驱动的机制。经理论推导、仿真分析以及实验海试数据验证,结果表明:(1)最小方差无偏响应(MVDR)、Bartlett等匹配场处理器是贝叶斯处理器的基本单元;(2)贝叶斯匹配场处理是对应的MVDR匹配场处理、Bartlett匹配场处理器等匹配场处理方法的后验概率加权和;(3)不确实海洋环境下,贝叶斯方法比对应的MVDR处理器和Bartlett处理器能更准确地对目标进行定位;(4)贝叶斯方法能较有效地抑制旁瓣。     

A numerically-enhanced machine learning approach to damage diagnosis using a Lamb wave sensing network

This paper describes a methodology for the design of a model-based diagnostic unit. The objective of the work is to define a suitable procedure for the design and verification of diagnostic performance in a simulated environment, trying to maximise the generalisation capability of pattern recognition algorithms when tested with real experimental signals. The system is designed and experimentally verified to solve the fatigue crack damage localisation and assessment problems in a realistic, though rather idealised, Structural Health Monitoring (SHM) framework. The study is applied to a piezoelectric Lamb wave sensor network and is validated experimentally on a simple aluminium skin. The analytically-derived dispersion curves for Lamb wave propagation in aluminium are used in order to determine the wave velocities and thus their arrival time at given sensors. The Local Interaction Simulation Approach (LISA) is used to simulate the entire waveform propagation. Once the agreement between analytical, numerical and experimental data is verified on a baseline undamaged condition, the parametric LISA model has been iteratively run, varying the position and the length of a crack on an aluminium skin panel, generating the virtual experience necessary to train a supervised learning regressor based on Artificial Neural Networks (ANNs). After the algorithm structure has been statistically optimised, the network sensitivity to input variations has been evaluated on simulated signals through a technique inspired by information gap theory. Real Lamb wave signals are then processed into the algorithm, providing feasible real-time indication of damage characteristics.     

A Bayesian approach to matched field processing in uncertain ocean environments

An approach of Bayesian Matched Field Processing （MFP） was discussed in the uncertain ocean environment. In this approach, uncertainty knowledge is modeled and spatial and temporal data received by the array are fully used. Therefore, a mechanism for MFP is found, which well combines model-based and data-driven methods of uncertain field processing. By theoretical derivation, simulation analysis and the validation of the experimental array data at sea, we find that （1） the basic components of Bayesian matched field processors are the cor- responding sets of Bartlett matched field processor, MVDR （minimum variance distortionless response） matched field processor, etc.; （2） Bayesian MVDR/Bartlett MFP are the weighted sum of the MVDR/Bartlett MFP, where the weighted coefficients are the values of the a posteriori probability; （3） with the uncertain ocean environment, Bayesian MFP can more correctly locate the source than MVDR MFP or Bartlett MFP; （4） Bayesian MFP can better suppress sidelobes of the ambiguity surfaces.     

A time-domain approach for damage detection in beam structures using vibration data with a moving oscillator as an excitation source

The problem of detecting local/distributed change of stiffness in bridge structures using ambient vibration data is considered. The vibration induced by a vehicle moving on the bridge is taken to be the excitation source. A validated finite element model for the bridge structure in its undamaged state is assumed to be available. Alterations to be made to this initial model, to reflect the changes in bridge behaviour due to occurrence of damage, are determined using a time-domain approach. The study takes into account complicating features arising out of dynamic interactions between vehicle and the bridge, bridge deck unevenness, spatial incompleteness of measured data and presence of measurement noise. The inclusion of vehicle inertia, stiffness and damping characteristics into the analysis makes the system time variant, which, in turn, necessitates treatment of the damage detection problem in time domain. The efficacy of the procedures developed is demonstrated by considering detection of localized/distributed damages in a beam-moving oscillator model using synthetically generated vibration data.