Sensor validation using minimum mean square error estimation

Sensor fault can be detected and corrected in a multichannel measurement system with enough redundancy using solely the measurement data. A single or multiple sensors can be estimated from the remaining sensors if training data from the functioning sensor network are available. The method is based on the minimum mean square error (MMSE) estimation, which is applied to the time history data, e.g. accelerations. The faulty sensor can be identified and replaced with the estimated sensor. Both spatial and temporal correlation of the sensors can be utilized. Using the temporal correlation is justified if the number of active structural modes is larger than the number of sensors. The disadvantages of the temporal model are discussed. Experimental multichannel vibration measurements are used to verify the proposed method. Different, and also simultaneous, sensor faults are studied. The effects of environmental variability and structural damage are discussed.     

Differentiating damage effects in a structural component from the time response

This paper proposes an approach to differentiate the effect of local anomalies on the different load-resisting capacities of a structural component made of elastic isotropic material. The sensitivities of structural dynamic response with respect to different damage indices are derived. They are then used in a time-domain sensitivity-based algorithm for model updating using a gradient-based approach. A planar frame structure is studied in the numerical simulation. Results show that the proposed method can effectively locate and quantify the different types of damage effects, and the proposed method is insensitive to measurement noise. Laboratory verification was performed with a three-dimensional truss structure with the identified results clearly showing how much a local physical damage can change the different physical parameters that lead to the load-resisting capacities of a structural component.     

Damage localisation in composite and metallic structures using a structural neural system and simulated acoustic emissions

Detecting and locating damage in structural components and joints that have high feature densities and complex geometry is a difficult problem in the field of structural health monitoring (SHM). Active propagation of diagnostic waves is one approach that is used to detect damage. But small cracks and damage are difficult to detect because they have a small effect on the propagating waves as compared to the effects the complex geometry itself which causes dispersion and reflection of waves. Another limitation of active wave propagation is that pre-damage data is required for every sensor–actuator combination, and a large number of sensors might be needed to detect small cracks on large structures. Overall, the problem of detecting damage in complex geometries is not well investigated in the field of SHM. Nevertheless, the problem is important because damage often initiates at joints and locations where section properties change.Recently there have been advances in the development of a passive structural neural system (SNS) for damage detection. The SNS uses electronic logic circuits to mimic the signal processing in the biological neural system. The advantage of the SNS is that highly distributed continuous sensors provide high sensitivity to damage, and the biomimetic signal processing and passive sensing tremendously simplify the instrumentation and wiring of the monitoring system. Also, the SNS operates continuously during operation of the structure to detect ambient Lamb waves or bulk waves that are produced by cracking, delamination, bearing damage, rotor imbalance, flow instabilities, impacts, or other material failure modes.In this paper, asymmetric Lamb wave propagation representing acoustic emissions (AE) is modelled based on a superposition of plate bending vibration modes. The simulation demonstrates that the SNS with four channels of data acquisition can localize damage within a grid of sensors irrespective of the number of sensors in the network. To experimentally validate the analysis results, a two-neuron prototype of the SNS was built and tested using a simulated AE source (a pencil lead break) on a riveted aluminium joint and on a composite plate. In both experiments, the SNS was able to localize simulated damages. These results indicate the feasibility of expanding the SNS to a large number of neurons.     

Distributed structural health monitoring with a smart sensor array

This paper presents work on smart sensor arrays for distributed structural health monitoring (SHM) and damage diagnosis. The goal of the work was to implement local vibration-based diagnostic algorithms inside a smart ‘black box’ to demonstrate the feasibility of distributed health monitoring for damage detection and location. Dynamic transmissibility features for SHM and the smart-processing platform are described in detail and various damage configurations in two large test structures, a representative three-storey building and a rotorcraft fuselage, are diagnosed. The results show that the near real-time integrated monitoring system works well in spite of certain limited environmental fluctuations (e.g. temperature, input levels) and boundary condition non-linearities. Wired piezoelectric arrays of accelerometers are implemented in conjunction with the black box.     

Dynamic analysis and structural optimization of a fiber optic sensor using neural networks

The objective of this work is to apply artificial neural networks for solving inverse problems in the structural optimization of a fiber optic pressure sensor. For the sensor under investigation to achieve a desired accuracy, the change in the distance between the tips of the two fibers due to the applied pressure should not interfere with the phase change due to the change in the density of the air between the two fibers. Therefore, accurate dynamic analysis and structural optimization of the sensor is essential to ensure the accuracy of the measurements provided by the sensor. To this end, a normal mode analysis and a transient response analysis of the sensor were performed by combining commercial finite element analysis package, MSC/NASTRAN, and MATLAB. Furthermore, a parametric study on the design of the sensor was performed to minimize the size of the sensor while fulfilling a number of constraints. In performing the parametric study, the need for a relationship between the design parameters and the response of the sensor was fulfilled by using a neural network. The whole process of the dynamic analysis using commercial finite element analysis package and the parameter optimization of the sensor were automated within the MATLAB environment.     

The research on structural damage identification using rough set and integrated neural network

A huge amount of information and identification accuracy in large civil engineering structural damage identification has not been addressed yet. To efficiently solve this problem, a new damage identification method based on rough set and integrated neural network is first proposed. In brief, rough set was used to reduce attributes so as to decrease spatial dimensions of data and extract effective features. And then the reduced attributes will be put into the sub-neural network. The sub-neural network can give the preliminary diagnosis from different aspects of damage. The decision fusion network will give the final damage identification results. The identification examples show that this method can simplify the redundant information to reduce the neural network model, making full use of the range of information to effectively improve the accuracy of structural damage identification.     

Wind pressure data reconstruction using neural network techniques: A comparison between BPNN and GRNN

Structural health monitoring (SHM) technique is increasingly used in civil engineering structures, with which the authentic environmental and structural response data can be obtained directly. To get accurate structural condition assessment and damage detection, it is important to make sure the monitoring system is robust and the sensors are functioning properly. When sensor fault occurs, data cannot be correctly acquired at the faulty sensor(s). In such situations, approaches are needed to help reconstruct the missing data. This paper presents an investigation on wind pressure monitoring of a super-tall structure of 600 m high during a strong typhoon, aiming to compare the performance of data reconstruction using two different neural network (NN) techniques: back-propagation neural network (BPNN) and generalized regression neural network (GRNN). The early stopping technique and the Bayesian regularization technique are introduced to enhance the generalization capability of the BPNN. The field monitoring data of wind pressure collected during the typhoon are used to formulate the models. In the verification, wind pressure time series at faulty sensor location are reconstructed by using the monitoring data acquired at the adjacent sensor locations. It is found that the NN models perform satisfactorily in reconstructing the missing data, among which the BPNN model adopting Bayesian regularization (BR-BPNN) performs best. The reconstructed wind pressure dataset has maximum root mean square error about 23.4 Pa and minimum correlation coefficient about 0.81 in reference to the field monitoring data. It is also shown that the reconstruction capability of the NN models decreases as the faulty sensor location moves from center to corner of the sensor array. While the BR-BPNN model performs best in reconstructing the missing data, it takes the longest computational time in model formulation.     

Investigation of operational and environmental variability effects on damage detection algorithms in a woven composite plate

Variability in structural health monitoring systems can result in reduced reliability by increasing the likelihood of false-positive/-negative indications of damage. It is important to understand how sources of operational, environmental, and even computational variability influence damage indicator functions. A sensitivity model-based technique, which focuses on the physical rather than the statistical nature of variability, is described. Each source of static/dynamic variability is sequentially isolated with a variability test matrix for a woven composite plate. Computational sources of variability are investigated by comparing two different damage detection algorithms (i.e., transmissibility and embedded sensitivity). It is determined that by using a piecewise variability feedback process, certain parameters of the frequency response measurement and analysis (e.g., frequency band, input–output locations, etc.) can be chosen to reduce sensitivity of the damage indices to variability. It is also shown that the sensitivity due to changes in the sensor frequency bandwidth accounts for the largest source of static variability. Finally, by using a root mean square normalization procedure, static and dynamic sources of variability can be compared with changes in damage indicators due to actual damage. It is shown that damage detection algorithms can be improved by selecting specific frequency ranges that accentuate damage indicators while minimizing the effects of variability.     

The influence of enhanced closed-loop sensitivity towards breathing-type structural damage

This paper investigates the performance of a nonlinear damage detection method using sensitivity enhancing control (SEC). Damage nonlinearity due to the cyclic behavior of crack breathing could provide valuable evidence of structural damage without information of the structure’s original healthy condition. Not having such information is considered a major challenge in vibration-based damage detection. In this study, two different categories of damage detection methods are investigated: frequency and time-domain techniques focusing on the benefit of SEC for breathing-type nonlinear damage in a structure. Numerical simulations using a cantilevered beam and spring-mass-damper system demonstrated that the level of nonlinear dynamic behavior heavily depends on the closed-loop pole placement through feedback control. According to SEC theory, the characteristic of the feedback gain defines the sensitivity of modal frequency to the change of stiffness or mass of the system. The sensitivity enhancement by properly designed closedloop pole location more visually clarifies the evidence of crack nonlinearity than the open-loop case where no sensitivity is enhanced. A damage detection filter that uses time series data could directly benefit from implementing SEC. The amplitude of damage-evident error signal of the closed-loop case significantly increases more than that of the open-loop case if feedback control or SEC properly modifies the dynamics of the system.     

Reconstructing structural changes in a dynamic system from experimentally identified state-space models

This paper presents an experimental investigation of a recently developed Kronecker Product (KP) method to determine the type, location, and intensity of structural change from an identified state-space model of the system. Although this inverse problem appears to be highly nonlinear, the system mass, stiffness, and damping matrices are identified through a series of transformations, and with the aid of the Kronecker product, only linear operations are involved in the process. Since a state-space model can be identified directly from input-output data, an initial finite element model and/or model updating is not required. The test structure is a two-degree-of-freedom torsional system in which mass and stiffness are arbitrarily adjustable to simulate various conditions of structural change or damage. This simple apparatus illustrates the potential applicability of the system identification technique for damage detection problems by not only identifying the location and the extent of the damage, but also differentiating the nature of the damage. The results are successfully confirmed by laboratory tests.     

电感位移传感器结构特性研究

在电感位移传感器设计中,电感传感器内线圈与磁芯的结构特性是影响传感器性能的关键因素。为提高传感器检测性能,根据毕奥-沙伐尔定律建立了传感器内线圈与磁芯模型,利用有限元分析软件建立了相应结构模型。通过分析线圈与磁芯各项参数变化以及线圈与磁芯存在安装与加工误差的情况,得到了各参数变化以及安装与加工误差对传感器测量性能的影响,以此对线圈和磁芯进行了优化。研究结果表明,当线圈长度与磁芯长度的比值在1.45~1.6之间时,传感器能获得较大的电感相对变化量;线圈长度、磁芯长度与磁芯半径的变化对线性度的影响不明显,线性度在0.5%附近,磁芯安装偏心与倾斜对传感器性能影响较小,可忽略不计;电感相对变化量会随着磁芯锥度的增大而减小。     

Environmental effects on the identified natural frequencies of the Dowling Hall Footbridge

Continuous monitoring of structural vibrations is becoming increasingly common as sensors and data acquisition systems become more affordable, and as system and damage identification methods develop. In vibration-based structural health monitoring, the dynamic modal parameters of a structure are usually used as damage-sensitive features. The modal parameters are often sensitive to changing environmental conditions such as temperature, humidity, or excitation amplitude. Environmental conditions can have as large an effect on the modal parameters as significant structural damage, so these effects should be accounted for before applying damage identification methods. This paper presents results from a continuous monitoring system installed on the Dowling Hall Footbridge on the campus of Tufts University. Significant variability in the identified natural frequencies is observed; these changes in natural frequency are strongly correlated with temperature. Several nonlinear models are proposed to represent the relationship between the identified natural frequencies and measured temperatures. The final model is then validated using independent sets of measured data. Finally, confidence intervals are estimated for the identified natural frequencies as a function of temperature. The ratio of observed outliers to the expected rate of outliers based on the confidence level can be used as a damage detection index.     

An efficient representative for object recognition in structural health monitoring

Structural health monitoring (SHM) program has been widely applied for damage assessment or monitoring the performances and the reliabilities of structures. Usually, structural damage is assessed by discriminating abnormal change from normal change in the structural dynamic behavior. This paper takes the SHM data from a real bridge as a study example and recognizes the trains which daily cross this bridge. The main aim of the current work is to improve the accuracy of structural damage detection. In this paper, symbolic data analysis (SDA) is introduced to extract hidden patterns from raw data, and principal component analysis (PCA) is performed to extract the most important properties of the signals and eliminate the influence of noise. A new representative is proposed in this paper and the recognition results obtained by the clustering algorithms with different representatives are compared. The results show that the new representative is robust to recognize the trains, and PCA is very useful to extract the properties of the signals as well as to reduce the influence of noise.     

Conceptual damage-sensitive features for structural health monitoring: Laboratory and field demonstrations

The use of damage-sensitive features to evaluate structural condition or health is a very critical aspect of structural health monitoring. The purpose of this paper is to investigate the potential of two different damage-sensitive features for detecting damage. Different damage scenarios are simulated on a large-scale laboratory structure and a three-span highway bridge for demonstration. The features presented in this paper are the modal flexibility-based deflection and curvature both of which are obtained directly from dynamic properties. In the literature, flexibility associated with mode shapes and mode shapes curvatures have been mostly explored. In this study, multi-input–multi-output dynamic data are used to obtain modal flexibility, which is a close approximation to the actual flexibility. A main novelty is that the curvature is calculated from the deflected shapes using the modal flexibility as opposed to using modal vectors. In this paper, the theory of the methodology is explained and then experimental studies and results are presented. For the experimental studies, the laboratory specimen and the three-span bridge were gradually damaged. It is shown that both deflection and curvature are conceptual and physically meaningful features for damage detection and localization. The issues and the requirements for these features to perform successfully are also presented.     

A stochastic analysis framework for a steel frame structure using wireless sensor system measurements

This paper presents a stochastic analysis framework for estimating the system-level first-passage probability of the structural responses of multi-degree-of-freedom structural systems based on experimentally measured uncertainties. The uncertainties are quantified by comparing the measured structural responses using a wireless sensor system and the predicted responses from an analytical model. The wireless sensor network is designed based on a modular design method, and the experimental program details for the measurement of structural responses are provided using the developed wireless sensor network. This framework employs a Monte Carlo simulation (MCS)-based first-passage probability estimation technique in which a structural dynamic analysis is performed in each simulation realization. The framework is applied to a 16-story steel frame structure, and the first-passage probability of 16 locations and the series system passage probability of the entire system have been estimated. The effect of the dependency between the structural responses is considered, and the improvements that need to be made to the presented framework in the future works are discussed.     

Self-diagnosis of smart structures based on dynamical properties

When we talk about “smart structures” we can think about different properties and capabilities which make a structure “intelligent” in a certain sense. Originally, the expression “smart” was used in the context that a structure can react and adapt to certain environmental conditions, such as change of shape, compensation of deformations, active vibration damping, etc. Over the last year, the expression “smart” has been extended to the field of structural health monitoring (SHM), where sensor networks, actuators and computational capabilities are used to enable a structure to perform a self-diagnosis with the goal that this structure can release early warnings about a critical health state, locate and classify damage or even to forecast the remaining life-time. This paper intends to give an overview and point out recent developments of vibration-based methods for SHM. All these methods have in common that a structural change due to a damage results in a more or less significant change of the dynamic behavior. For the diagnosis an inverse problem has to be solved. We discuss the use of modal information as well as the direct use of forced and ambient vibrations in the time and frequency domain. Examples from civil and aerospace engineering as well as off-shore wind energy plants show the applicability of these methods.     

A Bayesian approach to optimal sensor placement for structural health monitoring with application to active sensing

This paper introduces a novel approach for optimal sensor and/or actuator placement for structural health monitoring (SHM) applications. Starting from a general formulation of Bayes risk, we derive a global optimality criterion within a detection theory framework. The optimal configuration is then established as the one that minimizes the expected total presence of either type I or type II error during the damage detection process. While the approach is suitable for many sensing/actuation SHM processes, we focus on the example of active sensing using guided ultrasonic waves by implementing an appropriate statistical model of the wave propagation and feature extraction process. This example implements both pulse-echo and pitch-catch actuation schemes and takes into account line-of-site visibility and non-uniform damage probabilities over the monitored structure. The optimization space is searched using a genetic algorithm with a time-varying mutation rate. We provide three actuator/sensor placement test problems and discuss the optimal solutions generated by the algorithm.     

Development of scanning damage index for the damage detection of plate structures using modal strain energy method

This work presents a novel approach of nondestructive detection of damage in plate structures by using experimental modal analysis (EMA) and modal strain energy method (MSEM). An aluminum alloy 6061 thin plate with a surface crack is investigated in this study. EMA is conducted on the plate to obtain the mode shapes before and after damage. The modal displacements of each mode shape are then used to compute the modal strain energy. For all measured mode shapes, a damage index is defined by using the ratio of modal strain energies of the plate before and after damage. In fact, small damage causes very little change in system response, but it is an essential early warning of structure damage. As the second-order derivatives, modal strain energy is much more sensitive to the small change of structural response than frequencies and mode shapes. It is therefore feasible to approach the small damage by using a damage index defined by fractional MSE of the structure before and after damage. In this study, a scanning damage index (SDI) is developed by moving damage indices obtained from the local area throughout the structure as if a scanning sensor is used to inspect the structure. The damage indices in overlap areas are added up and the summation may intensify the signals of damage in the plate. Limited by the numbers of measured point, a differential quadrature method is employed to calculate the partial differential terms in strain energy formula. Experimental results show that SDI well identifies a surface crack location by using only few measured mode shapes of the aluminum plate. This novel approach provides a flexible, cost-effective, and nondestructive damage evaluation in either local or global structure. Its applicability to different types of structures and different sizes of damage is to be experimentally validated in the future work.     

自适应多重TMD在大跨楼板结构减振中的应用

多重调谐质量阻尼器（multiple tuned mass damper,简称MTMD）常被用于大跨楼板结构的竖向振动舒适度控制中。为改善目前工程中使用的MTMD对频率调谐敏感和难以调频的缺点,提出了一种自适应多重调谐质量阻尼器（adaptive-passive MTMD,简称AP-MTMD）减振系统。该系统中的每个自适应调谐质量阻尼器（tuned mass damper,简称TMD）均具有可变质量的构造,以及由加速度传感器、控制电路板和驱动装置组成的伺服控制系统。环境激励下,控制电路板采集置于主结构上加速度传感器的信号,通过基于小波变换（wavelet transformation,简称WT）的频率识别方法识别得主结构的主导自振频率,然后自发地启动驱动装置改变TMD的质量以调谐自身频率至所识别得到的主结构频率。以某大跨楼板结构为例进行分析,首先,通过现场实测修正有限元模型;其次,根据修正前的结构模型设计了一套自适应多重TMD系统,验证了其频率自适应调节的鲁棒性;最后,通过施加若干种人行荷载,对比了启动调节前后的MTMD系统对修正后模型的减振效果。结果表明,自适应多重TMD能够自发地调谐自身频率,提高对楼板结构人致振动的减振效果。     

自适应多重TMD在大跨楼板结构减振中的应用

双轴振镜系统是扫描式激光多普勒测振仪中控制激光束运动的核心部件。通过双轴振镜系统中X,Y振镜的空间位置关系及光学反射原理,建立了激光束方向与X,Y振镜偏转角度之间的关系及其数学模型, 并进一步基于多项式拟合算法实现了激光点从振镜系统坐标系到实测结构坐标系的坐标变换,从而可高精度控制扫描激光的姿态。 同时,利用高清摄像头将测试坐标系转换到屏幕坐标系,提高了该方法的实际操作性。最后,以某型压气机叶片为例对该方法进行了验证,结果表明本方法对实现激光在实测结构表面任意测点的精确位置控制提供了理论基础,具有较好的指导作用。