兰姆波结构健康监测中的概率损伤成像

应用概率成像方法对兰姆波结构健康监测中板的损伤进行识别。根据兰姆波损伤散射信号的传播时间以及传感器网络中一对发射-接收传感器的空间位置来确定一个椭圆轨迹,该轨迹显示了损伤可能出现的位置。将监测区域均匀网格化,计算各网格节点到椭圆轨迹的最短距离,将此距离映射为损伤出现的概率。采用灰度等级对所有网格节点处的概率值进行量化,则由每一个发射接收传感器对就确定了一个灰度图像。为了突出损伤,应用图像融合技术对传感器网络中所有发射接收传感器对所构成的灰度图像进行融合。对铝板中横穿孔损伤的实验结果显示了该方法能够有效地确定损伤的位置和范围,有助于推动兰姆波结构健康监测的实用化。     

Dual-probe laser interferometer for structural health monitoring

In this note we present the development of a dual-probe laser interferometer that uses the filtering properties of a polarized beamsplitter to enable two independent (uncoupled) detection probes. The robustness of this system is demonstrated by making broadband, noncontact, high fidelity measurements of Lamb waves in an aluminum plate.     

Efficient temperature compensation strategies for guided wave structural health monitoring

The application of temperature compensation strategies is important when using a guided wave structural health monitoring system. It has been shown by different authors that the influence of changing environmental and operational conditions, especially temperature, limits performance. This paper quantitatively describes two different methods to compensate for the temperature effect, namely optimal baseline selection (OBS) and baseline signal stretch (BSS). The effect of temperature separation between baseline time-traces in OBS and the parameters used in the BSS method are investigated. A combined strategy that uses both OBS and BSS is considered. Theoretical results are compared, using data from two independent long-term experiments, which use predominantly A₀ mode and S₀ mode data respectively. These confirm that the performance of OBS and BSS quantitatively agrees with predictions and also demonstrate that the combination of OBS and BSS is a robust practical solution to temperature compensation.     

Robust methods of inclusive outlier analysis for structural health monitoring

The key novel element of this work is the introduction of robust multivariate statistical methods into the structural health monitoring (SHM) field through use of the minimum covariance determinant estimator (MCD) and the minimum volume enclosing ellipsoid (MVEE). In this paper, robust outlier statistics are investigated, focussed mainly on a high level estimation of the “masking effect” of inclusive outliers, not only for determining the presence or absence of novelty-something that is of fundamental interest but also to examine the normal condition set under the suspicion that it may already include multiple abnormalities. By identifying and detecting variability at an early stage, the prospects of achieving good generalisation and establishing a correct normal condition classifier may be increased. It is critical to highlight that there is no a priori division between the damaged and the undamaged condition data when the algorithms are implemented, offering a significant advantage over other methodologies. In summary, this paper introduces a new scheme for SHM by exploiting robust multivariate outlier statistics in order to investigate if the selected features are free from multiple outliers before such features can be selected for either supervised or unsupervised analysis.     

Phase retrieval in optical fiber modal interference for structural health monitoring

A method based on data dependent system (DDS) for extraction of phase in fiber modal interference is presented. The interference patterns of LP₀₁ & LP₁₁, LP₀₁ & LP₀₂ and LP₀₆ &LP₀₇ within the fiber have been recorded under different launching conditions. The patterns were characterized by means of autoregressive model and the self coherence functions of the corresponding interferogram were determined. It would provide the phase distribution of the pattern and the modulation of group delay due to the measurand. An application has been made for measuring strain in a simply supported beam under different loading conditions. Results are presented for the applied strain in the range of 270-1500 μ strain.     

A structural health monitoring strategy using cepstral features

A statistical pattern recognition based damage detection algorithm is proposed. The algorithm is developed according to the training and testing scheme, typical of pattern recognition applications. The original contribution of the work is given by the use of an adaptation of Mel-Frequency Cepstral Coefficients as damage sensitive features, as their compactness and de-correlation characteristics make them particularly suited for statistical pattern recognition applications. At the same time, the ease of extraction, which requires minimal user expertise, represents an important advantage over other more popular features, and makes the cepstral features particularly convenient for implementation into automatic structural health monitoring routines. The damage detection algorithm employs the squared Mahalanobis distance to solve the Structural Health Monitoring assignment. The method is validated by using both simulated and experimental data, and the performance of said features is compared to that of Auto-Regressive (AR) coefficients, which have been largely used to solve the task of structural damage detection. The experimental data were measured on a steel frame, which behave nonlinearly in its damaged configuration, at the Los Alamos National Laboratory. Results demonstrate that the proposed approach may be conveniently used in real-life applications, since cepstral features outperform AR coefficients when dealing with experimental data modeled to mimic the operational and environmental variability.     

Comparison of scale factor estimators for ultrasonic temperature monitoring: Application to structural health monitoring

In Structural Health Monitoring (SHM) of materials, estimating the effects of environmental and operational conditions such as temperature is important. Indeed, temperature changes induce modifications of the mechanical properties of the material and therefore cause a dilation of the acoustic signals characterized by a scale factor. This paper described four scale factor estimators able to monitor changes in temperature: The short-time cross-correlation (STXC) method, the stretching method (STRE), the Minimum Variance Based Estimator method (MVBE) and the Scale Transform Based Estimator method (STBE). The first two methods have already been assessed in the literature while the latter two have been specifically developed for this study. First, closed-form for the Cramer-Rao bound on the estimates of the scale factor, from a simplified deterministic signal, are derived and simplified expressions are given. Then, a statistical evaluation of the quality of estimates is conducted through Monte-Carlo simulations using synthetic signals, based on a model taking into account the influence of temperature. A raw estimate of the computational complexity of signal processing methods also completes this evaluation phase. Finally, the experimental validation of estimation methods is conducted on an aluminum plate subjected to temperatures variations in a controlled thermal environment. The temperature estimates are then faced with an analytical model describing the material behavior.     

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.     

A time reversal damage imaging method for structure health monitoring using Lamb waves

This paper investigates the Lamb wave imaging method combining time reversal for health monitoring of a metal-lic plate structure.The temporal focusing effect of the time reversal Lamb waves is investigated theoretically.It demonstrates that the focusing effect is related to the frequency dependency of the time reversal operation.Numerical simulations are conducted to study the time reversal behaviour of Lamb wave modes under broadband and narrowband excitations.The results show that the reconstructed time reversed wave exhibits close similarity to the reversed nar-rowband tone burst signal validating the theoretical model.To enhance the similarity,the cycle number of the excited signal should be increased.Experiments combining finite element model are then conducted to study the imaging method in the presence of damage like hole in the plate structure.In this work,the time reversal technique is used for the recompression of Lamb wave signals.Damage imaging results with time reversal using broadband and narrowband excitations are compared to those without time reversal.It suggests that the narrowband excitation combined time reversal can locate and determine the size of structural damage more precisely,but the cycle number of the excited signal should be chosen reasonably.     

A novel time-division multiplexing fiber Bragg grating sensor interrogator for structural health monitoring

A novel fiber Bragg grating (FBG) sensor system for measurement of strain and temperature is proposed in this paper. The proposed sensor technique is based on time-division multiplexing (TDM). A semiconductor optical amplifier (SOA), connected in a ring cavity, is used to serve as a gain medium and switch. The SOA is driven by a pulse generator, which operates the SOA at different periods of time to select reflected pulses from a particular sensor. The FBG sensors have identical center wavelengths and can be deployed along the same fiber. This technique relieves the spectral bandwidth issue and permits the interrogation of up to 100 FBGs along a fiber, if the reflectivity of the individual sensors is sufficiently low to avoid shadowing effects. This system is particularly suitable for the application in structural health monitoring (SHM) where large numbers of sensors are required in wide measurement ranges.     

Classification of flaw severity using pattern recognition for guided wave-based structural health monitoring

In this paper, the authors present a formal classification routine to characterize flaw severity in an aircraft-grade aluminum plate using Lamb waves. A rounded rectangle flat-bottom hole is incrementally introduced into the plate, and at each depth multi-mode Lamb wave signals are collected to study the changes in received signal due to mode conversion and scattering from the flaw. Lamb wave tomography reconstructions are used to locate and size the flaw at each depth, however information about the severity of the flaw is obscured when the flaw becomes severe enough that scattering effects dominate. The dynamic wavelet fingerprint is then used to extract features from the raw Lamb wave signals, and supervised pattern classification techniques are used to identify flaw severity with up to 80.7% accuracy for a training set and up to 51.7% accuracy on a series of validation data sets extracted from independent plate samples.     

Temperature effects in ultrasonic Lamb wave structural health monitoring systems

There is a need to better understand the effect of temperature changes on the response of ultrasonic guided-wave pitch-catch systems used for structural health monitoring. A model is proposed to account for all relevant temperature-dependent parameters of a pitch-catch system on an isotropic plate, including the actuator-plate and plate-sensor interactions through shear-lag behavior, the piezoelectric and dielectric permittivity properties of the transducers, and the Lamb wave dispersion properties of the substrate plate. The model is used to predict the S(0) and A(0) response spectra in aluminum plates for the temperature range of -40-+60 degrees C, which accounts for normal aircraft operations. The transducers examined are monolithic PZT-5A [PZT denotes Pb(Zr-Ti)O3] patches and flexible macrofiber composite type P1 patches. The study shows substantial changes in Lamb wave amplitude response caused solely by temperature excursions. It is also shown that, for the transducers considered, the response amplitude changes follow two opposite trends below and above ambient temperature (20 degrees C), respectively. These results can provide a basis for the compensation of temperature effects in guided-wave damage detection systems.     

Crack as modulator,detector and amplifier in structural health monitoring

A model of a one-dimensional cracked cantilever bar subjected to longitudinal harmonic excitation is used to analyse a nonlinear response as a way to monitor structural health. The effect of the bilinear (nonlinear) character of the crack on the dynamics of the structure is studied. Simulation and experiments were performed to analyse the nonlinear behaviour of the cracked bar. In simulation the nonlinear information is obtained based on a combination of the analytical technique and the Matlab–Simulink computation. From analysis and experiment, it is found that the crack-induced nonlinearity leads to the generation of higher harmonics, whose intensity is a function of a distance from the crack. Side band frequencies were clearly revealed as well. The latter indicate modulation of exciting frequency due to systematic interaction of crack faces. The nonlinear transformation of modulated vibration by crack leads to generation of a low frequency periodic component. Its intensity is proportional to the forced response of the cracked bar at the exciting frequency. The phenomenology revealed can be effective for Structural Health Monitoring.     

Development of capacitance-based and impedance-based wireless sensors and sensor nodes for structural health monitoring applications

A field demonstration of a new and hybrid wireless sensing network paradigm for structural health monitoring (SHM) is presented. In this paradigm, both power and data interrogation commands are conveyed via a mobile agent that is sent to each sensor node to perform individual interrogations, which can alleviate several limitations of traditional sensing networks. This paper will discuss such prototype systems, which will be used to interrogate capacitive-based and impedance-based sensors for SHM applications. The capacitive-based wireless sensor node is specifically built to collect peak displacement measurements. In addition, a wireless sensor node for collecting electromechanical impedance data has also been developed. Both sensor nodes are specifically designed to accept various power sources and to be wirelessly triggered on an as-needed basis so that they can be used for the hybrid sensing network approach. The capabilities of these miniaturized and portable devices are demonstrated in the laboratory and the field, which was performed at the Alamosa Canyon Bridge in southern New Mexico.     

Passive structural health monitoring of a high-speed naval ship from ambient vibrations

Previous studies have used the cross-correlation of ambient vibrations (CAV) technique to estimate the impulse response (or Green's function) between passive sensors for passive imaging purposes in various engineering applications. The technique (CAV) relies on extracting deterministic coherent time signatures from the noise cross-correlation function computed between passive sensors, without the use of controlled active sources. Provided that the ambient structure-borne noise field remains stable, these resulting coherent waveforms obtained from CAV can then be used for structural monitoring even if they differ from the actual impulse response between the passive sensors. This article presents experimental CAV results using low-frequency random vibration data (<50 Hz) collected on an all-aluminum naval vessel (the HSV-2 Swift) operating at high speed (up to 40 knots) during high sea states. The primary excitation sources were strong wave impact loadings and rotating machinery vibrations. The consistency of the CAV results is established by extracting similar coherent arrivals from ambient vibrations between the pairs of strain gages, symmetrically located across the ship's centerline. The influence of the ship's operating conditions on the stability of the peak coherent arrival time, during the 7 days trial, is also discussed.     

Partial eigenvalue assignment for structural damage mitigation

In partial eigenvalue assignment, not all eigenvalues of the open loop system matrix are modified through a multiple input state or output feedback controller. This freedom available to assign selected eigenvalues of the closed loop system matrix has been widely used in design contexts such as to eliminate spillover effects in structural control problems. Similar approach is also required to modify damping and/or stiffness characteristics in selected eigenmodes of a damaged structure. When an external force acts on the damaged structure, partial eigenvalue assignment in this fashion will attempt to use minimal control effort and keep the structure active with safe operation. In this paper, a new approach to partial eigenvalue assignment and its application to structural damage mitigation are presented. A three mass spring-damper model with damage in one of the springs is illustrated with damping modifications at specific eigenmodes. The procedure is repeated for a second example, which is a cantilever beam modeled using two inputs and 10 state variables.     

A methodology for structural health monitoring with diffuse ultrasonic waves in the presence of temperature variations

Diffuse ultrasonic waves for structural health monitoring offer the advantages of simplicity of signal generation and reception, sensitivity to damage, and large area coverage; however, there are the serious disadvantages of no accepted methodology for analyzing the complex recorded signals and sensitivity to environmental changes such as temperature and surface conditions. Presented here is a methodology for applying diffuse ultrasonic waves to the problem of detecting structural damage in the presence of unmeasured temperature changes. This methodology is based upon the prediction and observation that the first order effect of a temperature change on a diffuse ultrasonic wave is a time dilation or compression. A multi-step procedure is implemented to (1) record a set of baseline waveforms from the undamaged specimen at temperatures spanning the expected operating range, (2) select a waveform from the baseline set whose temperature is the closest to that of a subsequently measured signal, (3) adjust this baseline waveform to best match the signal, and (4) calculate an error parameter between the signal and the adjusted waveform and compare this parameter to a threshold to determine the structural status. This procedure is applied to experimental data from aluminum plate specimens with artificial flaws. Probability of detection and the minimum flaw size detected are presented as a function of the size of the baseline waveform set. It is shown that a probability of detection of over 95% can be achieved with a small number of baseline waveforms.     

Equilibrium point defects in TiAl studied by PAC

Site fractions of dilute¹¹¹In/¹¹¹Cd probe atoms in different lattice locations in quenched, equi-atomic TiAl are measured using perturbed angular correlations of gamma rays (PAC). The vacancy-free substitutional site fraction, observed via its nuclear quadrupole interaction, is observed to decrease with increasing quenching temperature, reflecting an increase in the thermal defect concentration. Assuming that the thermal defect is the Schottky defect, the law of mass action, and a binding energy of 0.2 eV between vacancies and the In probe, the experimental temperature dependence is analysed to yield a formation enthalpy of 4.7(4) eV and entropy of 25(4)k _B for the Schottky defect. The vacancy concentration on one sublattice is given in terms of these parameters by the expression [v]=exp(S _F/2k _B) exp(–E _F/2k _B T), and is found, for example, to have the value 1.4% at 1350°C, a large value.