This article presents an experimental monitoring of the evolution of a crack in a beam using beam-vehicle interaction response signals for identification of progressively increasing crack-depth ratios. The beam is traversed by a two-axle model vehicle providing excitation in the time domain for the various extents of damage. The response of the beam in the time domain during the period of forced vibration is measured using strain gages. A consistent evolution of damage has been demonstrated in terms of the maxima values of the measured responses. The corresponding distortions of wavelet coefficients of the measured strain data due to the presence of various levels of damage have been identified. The evolution of the phase space and the wavelet transformed phase spaces have been evaluated with damage evolution. The wavelet transformed phase spaces for the undamaged and the damaged cases are observed to be distinctly different at high scales. The importance of denoising of the acquired data and the importance of vehicle configuration has been illustrated. This study presents a basis for a general model free damage assessment and structural health monitoring framework. The study presented is particularly useful in the context of continuous online bridge health monitoring, since the data necessary for analysis can be obtained from the operating condition of the bridge and the structure does not need be closed down.

In this article, a technique is proposed to detect damage in structures from measurements taken under different environmental and operational conditions. The method is based on the regression analysis of the features extracted from the vibration measurement. Macro strain was chosen as the selected feature to be measured using the long-gage distributed fiber Bragg grating (FBG) sensors. Features extracted from the measurements on an intact structure were used to construct a reference model for damage identification. Damage was identified by comparing the slope of the regression line of the subsequent measurements to its counterpart of the reference model. Experimental results show that the extracted features were very consistent. The proposed method was demonstrated and validated using noise-polluted numerical simulation data from two different types of structures, a bridge girder and a plane frame, as well as experimental results from a steel beam with different damage scenarios under changing environmental conditions. Different levels of damage were easily identified from the change in slope of the regression lines. The proposed technique is also applicable to statically measured data.

Resonance demodulation technique is widely employed to diagnose faults of rolling bearings. In order to reduce the energy leakage influence of the traditional demodulated resonance method, a new approach based on harmonic wavelet transform (HWT) is proposed to extract the fault characteristics of rolling bearing. From the results of the numerical simulation analysis, this method is proven to be efficient in detecting the impact signal clouded in noises. Moreover, this article proposes a resonance demodulation scheme, which can obtain the optimal HWT parameters automatically to construct the proper sub-frequency band filter by calculating the relative wavelet energy of the different sub-frequency band. It solves the shortcoming in which a resonance frequency band filter is chosen manually. The proposed scheme is successfully applied to detect the fault of rolling bearings of a tilting mechanism in a converter mill.

An algorithm based on correlation analysis was adopted to estimate the probability of the presence of damage in aluminum plates using Lamb wave signals from an active sensor network. Both finite element analysis and experimental evaluations were presented. The Shannon entropy optimization criterion was applied to calibrate the optimal mother wavelet and the most appropriate continuous wavelet transform scale for signal processing. The correlation coefficients for individual sensing paths between the present state (with damage) and the reference state (without damage) were calculated, and the probability of the presence of damage in the monitoring area enclosed by the active sensor network was estimated to identify the damage. A concept of virtual sensing paths (VSPs) was proposed to enhance the performance of the algorithm by increasing the number of sensing paths in data fusion. The results identified using both simulation and experimental Lamb wave signals from different groups of sensing paths at different central frequencies agreed well with the actual situations, demonstrating the potential of the correlation-based algorithm with the application of VSPs for identification of damage in structures.

This study presents an experimental verification of a structural damage recognition technique based on a fiber Bragg grating array and a back propagation neural network. A flat plate was designed and fabricated for these experiments. The plate structure was loaded using a lever and a weight, and damage was introduced by putting a hole in the plate. Data was collected for the healthy and damaged cases when the load was applied at different positions on the plate. The neural network was able to identify damage to the plate. This approach for damage detection is useful when a static load can be applied at multiple points on a structure.

An investigation was performed to develop a sensor placement method to maximize the performance of a structural health monitoring (SHM) system with a minimal number of sensors for detection of impact in structures, particularly for structures made of fiber-reinforced composite materials. The performance of the SHM system is evaluated based on the probability of detection (POD). This optimization problem was formulated to maximize the POD through selection of optimal sensor locations for a given sensor network. A genetic algorithm was adopted and integrated with the SHM system to perform the optimization process. Numerical simulations on two composite panels showed that the selection of sensor network configuration is crucial for the performance of the SHM system. For a targeted POD, the proposed method can be used to configure an SHM system with a minimal number of sensors to identify impact forces that are greater than a pre-defined critical value.

A robust signal processing technique using linear mapping for removing dispersion of Lamb waves is presented in this article. Based on the assumption that the dispersion relation characteristic can be adequately approximated by a finite polynomial in the region close to the high wave energy intensity, the dispersion effect begins to reveal in the second-order term of the polynomial. The linear mapping performed in the finite usable frequency domain is to transform the original in priori known dispersion relation into the linear dispersion relation, i.e., truncated the polynomial up to the linear term which is nondispersive. The linear mapping technique does not require the propagation-path lengths and can be applied to the signals consisting of multiple arrivals with the same wave mode or dispersion characteristic. Synthetic and experimental data for isotropic plates with finite in-plane dimensions excited by the fundamental flexural wave mode are shown to demonstrate the robustness of the proposed dispersion removal technique.

Important engineering constructions require geometrical monitoring to predict their structural health during their lifetime. Monitoring by geodetic devices, vibration-based techniques, wireless networks or with GPS technology is not always optimal; sometimes it is impossible as shown in this article. A method based on geodetic measurements automation applying local optical scanners for monitoring is proposed. Its originality and contribution is based on the novel method of precise measurement of plane spatial angles. It considers robust invariant AD-conversion angle-to-code for dynamic angle, signal energetic center search method, initial reference scale adjustment, and uncertainty decrease using mediant fractions formalism for result approximation. An algorithm of electromechanic parts interaction for spatial angle encoding with beforehand set accuracy is described. It is shown that by its nature it is appropriate for practically unlimited uncertainty reduction: it is only limited by reasonable ratio "uncertainty/operation velocity". The operation range, accuracy, scanner operation velocity, and its adaptation to objects are determined and described. The experimental results confirm that optical device of presented passive scanning aperture overcomes all recently known compared optical devices for 3D coordinates recognition in a part of offset uncertainty. It is shown that the presented optoelectronic scanning aperture is a universal tool for various practical solutions.

This article introduces the principles of an ultra low-cost scheme for in situ, embedded structural health monitoring. Based on the interactions of a Lamb wave with the structure, the proposed technique relies in comparing the time period where the sensed signature of the Lamb wave is greater than an user-defined threshold with a reference value. Compared to similar methods based on the monitoring of the structural state evolution, the proposed scheme is ultra low-cost, and therefore suitable for integrated systems that have a limited amount of available energy.

Wireless networks of smart sensors with computations distributed over multiple sensor packages have shown considerable promise in providing low-cost structural health monitoring. In these networks, microprocessors are typically embedded in individual smart sensor packages. The efficiency of embedded computational algorithms is of critical importance because the size, cost, and power requirements of the sensor arrays are central concerns. Here, very efficient methodologies are presented to compute statistical moments of a measured response time-history. These moments: the mean, standard deviation, skewness, and kurtosis are often used to characterize a measured irregular response. Two alternative approaches are presented, each of which can save substantial computer memory requirements and CPU time in certain applications. The first approach reconsiders the computational benefits of computing statistical moments by separating the data into bins and then computing the moments from the geometry of the resulting histogram, which effectively becomes a one-pass algorithm for higher moments. One benefit is that the statistical moment calculations can be carried out to arbitrary accuracy such that the computations can be tuned to the precision of the sensor hardware. The second approach is a new analytical methodology to combine statistical moments from individual segments of a time-history such that the resulting overall moments are those of the complete time-history. This methodology could be used to allow for parallel computation of statistical moments with subsequent combination of those moments, or for combination of statistical moments computed at sequential times.

A worked example is presented comparing two implementations of the new methodologies with conventional calculations in monitoring the global performance of an offshore tension leg platform. Accuracy, efficiency, and storage requirements of the calculation methods are compared with those of conventional methods. The results show that substantial CPU and memory savings can be attained with no loss in accuracy and that more dramatic savings can be attained if a slight reduction in accuracy is acceptable.

While wireless sensors are increasingly adopted in various applications, the need of developing data reduction methods to alleviate data transmission rate issue between the sensors and the data interpretation unit becomes more urgent. This article presents a new data reduction method for sensors used in structural health monitoring application. Our goal is to achieve an effective data reduction capability while maintaining adequate power for damage detection. We propose to establish an explicit measure of damage detection capability for the features in the response signals and use this measure to select the subset of the features that balance between the degree of data reduction and the damage detection capability. We also explore a computationally efficient procedure searching for the best subset of the features. This new method is tested on experimentally obtained Lamb wave signals for beam damage detection. Performance comparisons with respect to the existing methods demonstrate the strength of the proposed method.

This article presents the application of monitoring and detection of structural damage techniques to a historic monument. Seville cathedral’s famous bell tower ‘La Giralda’ is 96 m tall and is crowned with a large 16th century sculpture known as ‘Giraldillo’. The sculpture is supported with an internal bar structure, which is fitted over the axis about which it rotates according to the wind direction, allowing it to function as a weathervane. Between 1999 and 2005 the Giraldillo was demounted and underwent an intensive restoration process, which included mechanical and structural repair work. As the sculpture is only accessible by means of complex and costly scaffolding systems, an instrumentation system consisting of different types of sensors was installed to study the assembly’s mechanical response, its functioning as a weathervane and its state of conservation while it was being remounted atop the Giralda Tower. Different damage detection techniques aimed at detecting possible deterioration in the Giraldillo’s support structure were employed as well. This article presents results obtained in 2 years of system operation, showing how structural heath monitoring techniques can be applied to historical monuments.

Headed shear studs are commonly used to resist longitudinal shear forces in composite railway bridges. Due to the growth of traffic and increase in train speed, these studs are subjected to high-cycle fatigue loading which may lead to damage, thus affecting the integrity between the steel girder and the concrete slab. Therefore, it is necessary to find a corresponding nondestructive damage detection method. Within the frame of this paper, the occurrence of damage in shear studs is studied by numerical analysis. In the numerical model of a real composite bridge, headed shear studs are represented by spring elements. A damage indicator based on the local modal curvature and the wavelet transform modulus maxima is proposed for stud damage identification. The efficiency of the damage indicator is investigated by means of numerical simulations where different levels of damage are introduced to the stud by decreasing the spring stiffness. It is verified that the proposed damage index can be used to locate and to quantify the damage.