The interpolation damage detection method for frames under seismic excitation

In this paper a new procedure, addressed as Interpolation Damage Detecting Method (IDDM), is investigated as a possible mean for early detection and location of light damage in a structure struck by an earthquake. Damage is defined in terms of the accuracy of a spline function in interpolating the operational mode shapes (ODS) of the structure. At a certain location a decrease (statistically meaningful) of accuracy, with respect to a reference configuration, points out a localized variation of the operational shapes thus revealing the existence of damage. In this paper, the proposed method is applied to a numerical model of a multistory frame, simulating a damaged condition through a reduction of the story stiffness. Several damage scenarios have been considered and the results indicate the effectiveness of the method to assess and localize damage for the case of concentrated damage and for low to medium levels of noise in the recorded signals. The main advantage of the proposed algorithm is that it does not require a numerical model of the structure as well as an intense data post-processing or user interaction. The ODS are calculated from Frequency Response Functions hence responses recorded on the structure can be directly used without the need of modal identification. Furthermore, the local character of the feature chosen to detect damage makes the IDDM less sensitive to noise and to environmental changes with respect to other damage detection methods. For these reasons the IDDM appears as a valid option for automated post-earthquake damage assessment, able to provide after an earthquake, reliable information about the location of damage.     

The use of pseudo-faults for novelty detection in SHM

The main problem associated with pattern recognition based approaches to Structural Health Monitoring (SHM) is that damage localisation and quantification almost always require supervised learning. In the case of high-value engineering structures like aircraft, it is simply not possible to generate the training data associated with damage by experiment. It is also unlikely that data can always be generated by simulation as the models required would often need to be of such high fidelity that the costs of development and the run-times would again be prohibitive. The object of this paper is to explore the potential of a simple experimental strategy, which involves adding masses to the structure, in the attempt to extract features for novelty detection. The idea itself is not presented as revolutionary based on the fact that adding masses has been considered as a case of damage before, however, an in-depth investigation of its suitability for guiding feature selection is presented here. The approach is illustrated first on a simple structure by using data generated from a finite-element (FE) simulation and then validated experimentally on a more complicated laboratory structure. Simulated damage, in the form of a loss in the stiffness in the case of the numerical model and of a saw-cut in the case of the structure is used for comparison. The results show similar patterns in both cases which suggests a potential use of the method for higher level damage detection.     

EXPERIMENTAL VALIDATION OF A STRUCTURAL HEALTH MONITORING METHODOLOGY: PART II. NOVELTY DETECTION ON A GNAT AIRCRAFT

This paper concerns the second phase of an experimental validation programme for a structural health monitoring methodology based on novelty detection. This phase seeks to apply one of the methods considered in the first stage of the work on a more realistic structure, namely the wing of a Gnat aircraft, as opposed to the previously investigated laboratory structure. The novelty detection algorithm used is that of outlier analysis and damage is introduced by making several copies of an inspection panel, each with a different controlled fault. All of these faults were detectable, a single feature was highlighted which proved capable of separating all the fault conditions from the unfaulted.     

EXPERIMENTAL VALIDATION OF A STRUCTURAL HEALTH MONITORING METHODOLOGY: PART I. NOVELTY DETECTION ON A LABORATORY STRUCTURE

This paper is concerned with the experimental validation of a structural health monitoring methodology, previously only investigated using synthetic data. The structure considered here is a simplified model of a metallic aircraft wingbox i.e., a plate incorporating stiffening elements. Damage is simulated by a saw-cut to one of the panel stringers (stiffeners). The analysis approach uses novelty detection based on measured transmissibilities from the structure. Three different novelty detection algorithms are considered here: outlier analysis, density estimation and an auto-associative neural network technique. All three methods are shown to be successful to an extent, although a critical comparison indicates reservations about the density estimation approach when used on sparse data sets.     

Structural health monitoring and damage assessment using a novel time series analysis methodology with sensor clustering

This study presents a novel time series analysis methodology to detect, locate, and estimate the extent of the structural changes (e.g. damage). In this methodology, ARX models (Auto-Regressive models with eXogenous input) are created for different sensor clusters by using the free response of the structure. The output of each sensor in a cluster is used as an input to the ARX model to predict the output of the reference channel of that sensor cluster. Two different approaches are used for extracting Damage Features (DFs) from these ARX models. For the first approach, the coefficients of the ARX models are directly used as the DFs. It is shown with a 4 dof numerical model that damage can be identified, located and quantified for simple models and noise free data. To consider the effects of the noise and model complexity, a second approach is presented based on using the ARX model fit ratios as the DFs. The second approach is first applied to the same 4 DOF numerical model and to the numerical data coming from an international benchmark study for noisy conditions. Then, the methodology is applied to the experimental data from a large scale laboratory model. It is shown that the second approach performs successfully for different damage cases to identify and locate the damage using numerical and experimental data. Furthermore, it is observed that the DF level is a good indicator for estimating the extent of the damage for these cases. The potential and advantages of the methodology are discussed along with the analysis results. The limitations of the methodology, recommendations, and future work are also addressed.     

EXPERIMENTAL VALIDATION OF A STRUCTURAL HEALTH MONITORING METHODOLOGY: PART III. DAMAGE LOCATION ON AN AIRCRAFT WING

This paper documents the third phase of a programme of experimental work which validates a structural health monitoring methodology based on novelty detection. In this paper, an extension of the detection method for damage location is proposed and demonstrated. The structure of interest is a Gnat aircraft wing. Although it was not possible to damage the aircraft, the method was demonstrated by determining which of a set of inspection panels had been removed.     

Acoustic detection of invisible damage in aircraft composite panels

The wing and body panels of modern commercial and military aircraft often consist of a three-layer structure in which two thin skins of fibre-reinforced composite or of aluminium are held apart by a much thicker core consisting of a honeycomb structure made from either folded paper-like material impregnated with aramid resin or from thin, folded aluminium sheet. A major maintenance inspection problem arises from the fact that impact by a heavy soft object has the potential to deflect the skin and damage the core, after which the skin can return to its original shape so that the defect is nearly invisible. This paper gives details of an acoustic inspection system that can reveal such damage and provide information on its nature and size using a hand-held “pitch-catch” device that can be scanned over the suspected area to produce a visual display on a computer screen. The whole system operates in the frequency range 10-30 kHz and embedded programs provide optimal examination procedures.     

An adhesive bond state classification method for a composite skin-to-spar joint using chaotic insonification

The combination of chaotically amplitude-modulated ultrasonic waves and time series prediction algorithms has shown the ability to locate and classify various bond state damage conditions of a composite bonded joint. This study examines the ability of a new two-part supervised learning classification scheme not only to classify disbond size but also to classify whether a bond for which there is no baseline data is undamaged or has some form of disbond. This classification is performed using data from a similarly configured composite bond for which baseline data are available. The test structures are analogous to a wing skin-to-spar bonded joint. An active excitation signal is imparted to the structure through a macro fiber composite (MFC) patch on one side of the bonded joint and sensed using an equivalent MFC patch on the opposite side of the joint. There is an MFC actuator/sensor pair for each bond condition to be identified. The classification approach compares features derived from an autoregressive (AR) model coefficient vector cross-assurance criterion.     

Quantifying Information without Entropy: Identifying Intermittent Disturbances in Dynamical Systems

A system’s response to disturbances in an internal or external driving signal can be characterized as performing an implicit computation, where the dynamics of the system are a manifestation of its new state holding some memory about those disturbances. Identifying small disturbances in the response signal requires detailed information about the dynamics of the inputs, which can be challenging. This paper presents a new method called the Information Impulse Function (IIF) for detecting and time-localizing small disturbances in system response data. The novelty of IIF is its ability to measure relative information content without using Boltzmann’s equation by modeling signal transmission as a series of dissipative steps. Since a detailed expression of the informational structure in the signal is achieved with IIF, it is ideal for detecting disturbances in the response signal, i.e., the system dynamics. Those findings are based on numerical studies of the topological structure of the dynamics of a nonlinear system due to perturbated driving signals. The IIF is compared to both the Permutation entropy and Shannon entropy to demonstrate its entropy-like relationship with system state and its degree of sensitivity to perturbations in a driving signal.     

基于多源数据的新疆棉田螨害大范围监测研究

针对新疆棉田传统螨害监测方法耗时低效的问题,提出了一种基于冠层高光谱、近地多光谱、环境数据与地面调查相结合的多源数据棉田螨害大范围监测方法。首先,分别采集地面尺度的棉花冠层350~2 500 nm高光谱遥感数据和不同时期低空尺度的棉田无人机多光谱遥感影像数据,通过分析高光谱的原始光谱和一阶微分光谱特征,提取出了4个螨害敏感波段,分别为：绿光波段553 nm附近、红光波段680 nm附近、红边波段680~750 nm、近红外波段760~1 350 nm,这几个波段同时包含在无人机搭载的多光谱传感器波段范围内,验证了低空尺度下无人机遥感螨害监测的可行性。其次,初选23种植被指数和13种田间环境数据,结合地面调查的螨害发生情况做相关性分析。其中,SAVI、OSAVI、TVI、NDGI、平均湿度、温湿系数和10 cm土壤平均温度均与螨害发生达到极显著相关水平（sig≤0.01）;RDVI、RVI、MSR、最高温度、平均温度、积温、10 cm土壤最高温度和10 cm土壤平均湿度均与螨害发生达到显著相关水平（sig≤0.05）。选取sig值在0.05以下的15种特征值,分别建立基于单一环境数据、单一植被指数、环境数据与植被指数相结合的3种支持向量机（SVM）棉田螨害发生监测模型。最后,根据评价效果最优的监测模型,绘制不同时期的螨害遥感监测空间分布图,通过统计分布图中螨害和健康像元数计算出螨害面积占比,将螨害占比与同时期田间环境数据进行相关性分析,筛选出显著特征值,再通过多元逐步回归分析法确定出与螨害面积值关系最密切的环境因子,建立棉田螨害面积预测模型。结果表明：基于单一环境数据的棉田螨害发生监测模型准确率为62.22%,基于单一植被指数的棉田螨害发生监测模型准确率为75.56%,基于环境数据与植被指数相结合的棉田螨害发生监测模型效果最优,准确率为80%。螨害面积预测模型的决定系数R2=0.848,模型拟合度较好。本研究基于多源数据建立的棉田螨害发生监测模型和螨害面积预测模型,可以为新疆地区棉田螨害的大范围监测和趋势预警提供参考。     

The self-consistent diagram approximation for lattice systems

We apply the self-consistent diagram approximation to calculate equilibrium properties of lattice systems. The free energy of the system is represented by a diagram expansion in Mayer-like functions with averaging over states of a reference system. The latter is defined by one-particle mean potentials, which are calculated using the variational condition formulated. As an example, numerical computations for a two-dimensional lattice gas on a square lattice with attractive interaction between nearest neighbours were carried out. The critical temperature, the phase coexistence curve, the chemical potential and particle and vacancy distribution functions coincide within a few per cent with exact or with Monte Carlo data. Received 18 March 1999 and Received in final form 8 November 1999     

Guided waves for autonomous online identification of structural defects under ambient temperature variations

Ultrasonic guided waves that are excited by piezoelectric transducers can be used for the autonomous online identification of structural defects in thin structures. The proposed technique in this paper continuously analyzes a damage metric which is defined as the maximum residual amplitude of the differential signal. A special focus is on the decision making to discriminate the undamaged from the damaged state of the structure where the appropriate detection thresholds are derived statistically from the inverse cumulative distribution function of the damage metric during an initial training phase. An integrated trend analysis by means of the moving average mitigates the impact of statistical outliers and reduces the probability of erroneous identifications.Long-term measurements under ambient temperature variations have been conducted on an aluminum and a composite plate to study the properties of the proposed novelty detection framework. In this process the temperature effect was compensated by the well-known combination of optimal baseline selection (OBS) and baseline signal stretch (BSS). In case of the aluminum structure two artificial cracks with different sizes have been identified reliably. Consistent results were found on the composite specimen where an impact damage was identified for different excitation frequencies.     

On damage diagnosis for a wind turbine blade using pattern recognition

With the increased interest in implementation of wind turbine power plants in remote areas, structural health monitoring (SHM) will be one of the key cards in the efficient establishment of wind turbines in the energy arena. Detection of blade damage at an early stage is a critical problem, as blade failure can lead to a catastrophic outcome for the entire wind turbine system. Experimental measurements from vibration analysis were extracted from a 9 m CX-100 blade by researchers at Los Alamos National Laboratory (LANL) throughout a full-scale fatigue test conducted at the National Renewable Energy Laboratory (NREL) and National Wind Technology Center (NWTC). The blade was harmonically excited at its first natural frequency using a Universal Resonant EXcitation (UREX) system. In the current study, machine learning algorithms based on Artificial Neural Networks (ANNs), including an Auto-Associative Neural Network (AANN) based on a standard ANN form and a novel approach to auto-association with Radial Basis Functions (RBFs) networks are used, which are optimised for fast and efficient runs. This paper introduces such pattern recognition methods into the wind energy field and attempts to address the effectiveness of such methods by combining vibration response data with novelty detection techniques.     

小型旋翼机机载辐射环境监测系统的设计与实现

利用小型旋翼机良好的机动性能和环境适用性,将小型旋翼机与传统的核辐射探测设备相结合,再通过软硬件的开发、无线通讯、GPS定位以及核数据处理技术的应用,研发了一套具有受地形环境影响小、可避免人员受到近距离辐射损伤等优点的机载辐射监测系统。介绍了系统的结构组成、工作原理以及关键技术难点,重点研究了系统关键模块的设计以及剂量率响应线性范围的测定。飞行实测结果表明,该系统可对环境中的放射性实时、准确地测量,可用于日常的辐射环境监测以及失控放射源的搜寻,为核应急提供了一种新方法。     

Application of the bispectrum for detection of small nonlinearities excited sinusoidally

The nonlinear behaviour of damaged systems excited by vibration or ultrasound offers potential as a technique for damage detection in machine condition monitoring and non-destructive testing applications. The bispectrum, a third-order spectrum, has properties that lend themselves to the measurement of nonlinearities in systems. The properties of interest are insensitivity to Gaussian noise and ability to detect quadratic phase coupling. However, thus far analysis of the statistics of bispectrum estimation has been mainly aimed at stochastic systems. Many applications to vibration and ultrasound involve primarily deterministic, periodic excitations in the presence of stochastic noise. This paper considers the properties of a bispectrum estimate when applied to a system with weak quadratic nonlinearity excited by the superposition of two sinusoids in the presence of additive Gaussian noise. This is compared, using signal-to-noise ratios, to the powerspectrum, with the results validated using numerical data. Also addressed is the effect of quadratic phase coupling on such a system (in the absence of noise).     

Laser ultrasonic propagation imaging method in the frequency domain based on wavelet transformation

A wavelet-transformed ultrasonic propagation imaging method capable of ultrasonic propagation imaging in the frequency domain was developed and applied as a new structural damage or flaw visualization algorithm. Since the wavelet-transformed ultrasonic propagation imaging method has strong frequency selectivity, it can visualize the propagation of ultrasonic waves of a specific frequency (for example, to isolate ultrasonic mode of interest and a damage-related ultrasonic wave). The strong frequency selectivity of the wavelet-transformed ultrasonic propagation imaging method was demonstrated, isolating only the zeroth-order asymmetrical mode of the fundamental Lamb wave modes in an anisotropic carbon fiber-reinforced plastic plate with a thickness of 5 mm. The wavelet-transformed ultrasonic propagation imaging method can also convert a complex time domain multiple wavefield into a simple frequency domain single wavefield. This feature enables easy interpretation of the results, and facilitates the precise evaluation of the location and size of structural damage or flaws. We demonstrated this capability by detecting a disbond in a sandwich structure made of Al-alloy skins and a foam core. A disbond with a diameter of 20 mm, which is representative of a common manufacturing flaw, was successfully detected, localized, and evaluated. Since a method to determine the allowable maximum pulse repetition frequency depending on target materials and structures was found by investigating the residual wave caused from the previous laser impinging, our laser ultrasonic system can scan rapidly the target with an optimal pulse repetition rate. In addition, the proposed wavelet-transformed ultrasonic propagation imaging method can visualize damage or flaw without the need for reference data from the intact state of the structure. Hence, we propose the wavelet-transformed ultrasonic propagation imaging approach for automatic inspection of in-service engineering structures, or in-process quality inspection in manufacturing.     

Spatial field shifts in ocean acoustic environmental sensitivity analysis

This article examines the effects of spatial field shifts in ocean acoustic environmental sensitivity analysis. Acoustic sensitivity studies are typically based on comparing acoustic fields computed for a reference environmental model and for a perturbed model in which one or more parameters have been changed. The perturbation to the acoustic field due to the perturbed environment generally includes a component representing a spatial shift of the field (i.e., local field structure remains coherent, but shifts in range and/or depth) and a component representing a change to the shifted field. Standard sensitivity measures based on acoustic perturbations at a fixed point can indicate high sensitivity in cases where the field structure changes very little, but is simply shifted by a small spatial offset; this can conflict with an intuitive understanding of sensitivity. This article defines and compares fixed-point and field-shift corrected sensitivity measures. The approaches are illustrated with examples of deterministic sensitivity (i.e., sensitivity to a specific environmental change) and stochastic sensitivity (sensitivity to environmental uncertainty) in range-independent and range-dependent environments.     

Reference-free impedance-based crack detection in plates

Impedance-based damage detection techniques gained popularity among structural health monitoring (SHM) and nondestructive testing (NDT) communities due to their sensitivity to local damage and applicability to complex structures. In general, conventional impedance-based techniques identify damage by comparing “current” impedance signals with “baseline” ones obtained from the pristine condition of a structure. However, in-situ structures are often subject to changing temperature and loading conditions that can adversely affect measured impedance signals and cause false-alarms. In this paper, a “reference-free” impedance method, which does not require direct comparison of the current impedance signals with the previously obtained baseline impedance signals, is developed for crack detection in a plate-like structure. The proposed technique utilizes a single pair of PZTs collocated on the opposite surfaces of a structure to extract mode conversion produced by crack formation. Then, a reference-free damage classifier is developed and performed on the extracted mode conversion for instantaneous damage diagnosis. Numerical simulations and experimental tests have been conducted explicitly considering varying temperature and loading conditions to demonstrate the robustness of the proposed damage detection technique under varying operational and environmental conditions.     

Structural damage identification of the highway bridge Z24 by FE model updating

The development of a methodology for accurate and reliable condition assessment of civil structures has become very important. The finite element (FE) model updating method provides an efficient, non-destructive, global damage identification technique, which is based on the fact that the modal parameters (eigenfrequencies and mode shapes) of the structure are affected by structural damage. In the FE model the damage is represented by a reduction of the stiffness properties of the elements and can be identified by tuning the FE model to the measured modal parameters. This paper describes an iterative sensitivity based FE model updating method in which the discrepancies in both the eigenfrequencies and unscaled mode shape data obtained from ambient tests are minimized. Furthermore, the paper proposes the use of damage functions to approximate the stiffness distribution, as an efficient approach to reduce the number of unknowns. Additionally the optimization process is made more robust by using the trust region strategy in the implementation of the Gauss-Newton method, which is another original contribution of this work. The combination of the damage function approach with the trust region strategy is a practical alternative to the pure mathematical regularization techniques such as Tikhonov approach. Afterwards the updating procedure is validated with a real application to a prestressed concrete bridge. The damage in the highway bridge is identified by updating the Young's and the shear modulus, whose distribution over the FE model are approximated by piecewise linear functions.     

Left and right temporal-parietal junctions (TPJs) as “match/mismatch” hedonic machines: A unifying account of TPJ function

Experimental and theoretical studies have tried to gain insights into the involvement of the Temporal Parietal Junction (TPJ) in a broad range of cognitive functions like memory, attention, language, self-agency and theory of mind. Recent investigations have demonstrated the partition of the TPJ in discrete subsectors. Nonetheless, whether these subsectors play different roles or implement an overarching function remains debated. Here, based on a review of available evidence, we propose that the left TPJ codes both matches and mismatches between expected and actual sensory, motor, or cognitive events while the right TPJ codes mismatches. These operations help keeping track of statistical contingencies in personal, environmental, and conceptual space. We show that this hypothesis can account for the participation of the TPJ in disparate cognitive functions, including “humour”, and explain: a) the higher incidence of spatial neglect in right brain damage; b) the different emotional reactions that follow left and right brain damage; c) the hemispheric lateralisation of optimistic bias mechanisms; d) the lateralisation of mechanisms that regulate routine and novelty behaviours. We propose that match and mismatch operations are aimed at approximating “free energy”, in terms of the free energy principle of decision-making. By approximating “free energy”, the match/mismatch TPJ system supports both information seeking to update one's own beliefs and the pleasure of being right in one's own' current choices. This renewed view of the TPJ has relevant clinical implications because the misfunctioning of TPJ-related “match” and “mismatch” circuits in unilateral brain damage can produce low-dimensional deficits of active-inference and predictive coding that can be associated with different neuropsychological disorders.