• Smart Structures and Systems
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• v.32 no.3
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• pp.179-193
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• 2023

This study explores an alternative to the existing centralized process for data anomaly detection in modern Internet of Things (IoT)-based structural health monitoring (SHM) systems. An edge intelligence framework is proposed for the early detection and classification of various data anomalies facilitating quality enhancement of acquired data before transmitting to a central system. State-of-the-art deep neural network pruning techniques are investigated and compared aiming to significantly reduce the network size so that it can run efficiently on resource-constrained edge devices such as wireless smart sensors. Further, depthwise separable convolution (DSC) is invoked, the integration of which with advanced structural pruning methods exhibited superior compression capability. Last but not least, quantization-aware training (QAT) is adopted for faster processing and lower memory and power consumption. The proposed edge intelligence framework will eventually lead to reduced network overload and latency. This will enable intelligent self-adaptation strategies to be employed to timely deal with a faulty sensor, minimizing the wasteful use of power, memory, and other resources in wireless smart sensors, increasing efficiency, and reducing maintenance costs for modern smart SHM systems. This study presents a theoretical foundation for the proposed framework, the validation of which through actual field trials is a scope for future work.

• Smart Structures and Systems
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• v.11 no.4
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• pp.349-364
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• 2013

Recently, researchers in the field of structural health monitoring (SHM) have been rigorously striving to replace the conventional NDE techniques with the smart material based SHM techniques, employing smart materials such as piezoelectric materials. For instance, the electromechanical impedance (EMI) technique employing piezo-impedance (lead zirconate titanate, PZT) transducer is known for its sensitivity in detecting local damage. For practical applications, various external factors such as fluctuations of temperature and loading, affecting the effectiveness of the EMI technique ought to be understood and compensated. This paper aims at investigating the damage monitoring capability of EMI technique in the presence of axial stress with fixed boundary condition. A compensation technique using effective frequency shift (EFS) by cross-correlation analysis was incorporated to compensate the effect of loading and boundary stiffening. Experimental tests were conducted by inducing damages on lab-sized aluminium beams in the presence of tensile and compressive forces. Two types of damages, crack propagation and bolts loosening were simulated. With EFS for compensation, both cross-correlation coefficient (CC) index and reduction in peak frequency were found to be efficient in characterizing damages in the presence of varying axial loading.

• Wind and Structures
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• v.12 no.6
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• pp.479-504
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• 2009

Structural health monitoring (SHM) systems have been recently embraced in long span cable-supported bridges, in which buffeting-induced stress monitoring is one of the tasks to ensure the safety of the bridge under strong winds. In line with this task, this paper presents a SHM-oriented finite element model (FEM) for the Tsing Ma suspension bridge in Hong Kong so that stresses/strains in important bridge components can be directly computed and compared with measured ones. A numerical procedure for buffeting induced stress analysis of the bridge based on the established FEM is then presented. Significant improvements of the present procedure are that the effects of the spatial distribution of both buffeting forces and self-excited forces on the bridge deck structure are taken into account and the local structural behaviour linked to strain/stress, which is prone to cause local damage, are estimated directly. The field measurement data including wind, acceleration and stress recorded by the wind and structural health monitoring system (WASHMS) installed on the bridge during Typhoon York are analyzed and compared with the numerical results. The results show that the proposed procedure has advantages over the typical equivalent beam finite element models.

• Smart Structures and Systems
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• v.10 no.4_5
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• pp.313-329
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• 2012

A challenging issue in design and implementation of an effective structural health monitoring (SHM) system is to determine where a number of sensors are properly installed. In this paper, research on the optimal sensor placement (OSP) is carried out on the Canton Tower (formerly named Guangzhou New Television Tower) of 610 m high. To avoid the intensive computationally-demanding problem caused by tens of thousands of degrees of freedom (DOFs) involved in the dynamic analysis, the three dimension finite element (FE) model of the Canton Tower is first simplified to a system with less DOFs. Considering that the sensors can be physically arranged only in the translational DOFs of the structure, but not in the rotational DOFs, a new method of taking the horizontal DOF as the master DOF and rotational DOF as the slave DOF, and reducing the slave DOF by model reduction is proposed. The reduced model is obtained by IIRS method and compared with the models reduced by Guyan, Kuhar, and IRS methods. Finally, the OSP of the Canton Tower is obtained by a kind of dual-structure coding based generalized genetic algorithm (GGA).

• Smart Structures and Systems
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• v.15 no.1
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• pp.191-207
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• 2015

Proper placement of sensors plays a key role in construction and implementation of an effective structural health monitoring (SHM) system. This paper proposes a novel methodology called the distributed monkey algorithm (DMA) for the optimum design of SHM system sensor arrays. Different from the existing algorithms, the dual-structure coding method is adopted for the representation of design variables and the single large population is partitioned into subsets and each subpopulation searches the space in different directions separately, leading to quicker convergence and higher searching capability. After the personal areas of all subpopulations have been finished, the initial optimal solutions in every subpopulation are extracted and reordered into a new subpopulation, and the harmony search algorithm (HSA) is incorporated to find the final optimal solution. A computational case of a high-rise building has been implemented to demonstrate the effectiveness of the proposed method. Investigations have clearly suggested that the proposed DMA is simple in concept, few in parameters, easy in implementation, and could generate sensor configurations superior to other conventional algorithms both in terms of generating optimal solutions as well as faster convergence.

• Transactions on Electrical and Electronic Materials
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• v.18 no.2
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• pp.78-88
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• 2017

Cantilever beam MEMS piezoelectric accelerometers are the simplest and most widely used accelerometer structure. This paper discusses the design of a piezoelectric accelerometer exclusively for SHM applications. While such accelerometers need to operate at a lower frequency range, they also need to possess high sensitivity and low noise floor. The availability of a simple model for deflection, charge, and voltage sensitivities will make the accelerometer design procedure less cumbersome. However, a review of the open literature suggests that such a model has not yet been proposed. In addition, previous works either depended on FEM analysis or only reported on the fabrication and characterization of piezoelectric accelerometers. Hence, this paper presents, for the first time, a simple analytical model developed for the deflection, induced voltage, and charge sensitivity of a cantilever beam piezoelectric accelerometer.The model is then verified using FEM analysis for a range of different cases. Further, the model was validated by comparing the induced voltages of an accelerometer estimated using this model with experimental voltages measured in the accelerometer after fabrication. Subsequently, the design of an accelerometer is demonstrated for SHM applications using the analytical model developed in this work. The designed accelerometer has 60 mV/g voltage sensitivity and 2.4 pC/g charge sensitivity, which are relatively high values compared to those of the piezoresistive and capacitive accelerometers for SHM applications reported earlier.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.21-28
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• 2006

Structural health monitoring (SHM) is a new technology that will be increasingly applied at the industrial field as a potential approach to improve cost and convenience of structural inspection. Recently, the development of smart sensor is very active for real application. This study has focused on preparation and application study of SAL sensor. In order to detect elastic wave, smart piezoelectric sensor, SAL, is fabricated by using a piezoelectric element, shielding layer and protection layer. This protection layer plays an important role in a patched network of distributed piezoelectric sensor and shielding treatment. Four types of SAL sensor are designed/prepared/tested, and these details will be discussed in the paper. In this study, SAL sensor can be feasibly applied to perform structural health monitoring and to detect damage sources which result in elastic waves.

• Smart Structures and Systems
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• v.20 no.1
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• pp.35-42
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• 2017

Recently, compressive sensing based data loss recovery techniques have become popular for Structural Health Monitoring (SHM) applications. These techniques involve an encoding process which is onerous to sensor node because of random sensing matrices used in compressive sensing. In this paper, we are presenting a model where the sampled raw acceleration data is directly transmitted to base station/receiver without performing any type of encoding at transmitter. The received incomplete acceleration data after data losses can be reconstructed faithfully using compressive sensing based reconstruction techniques. An in-depth simulated analysis is presented on how random losses and continuous losses affects the reconstruction of acceleration signals (obtained from a real bridge). Along with performance analysis for different simulated data losses (from 10 to 50%), advantages of performing interleaving before transmission are also presented.

• Smart Structures and Systems
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• v.10 no.4_5
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• pp.393-410
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• 2012

The 610 m high Canton Tower (formerly named Guangzhou New Television Tower) is currently considered as a benchmark problem for structural health monitoring (SHM) of high-rise slender structures. In the benchmark study task I, a set of 24-hour ambient vibration measurement data has been available for the output-only system identification study. In this paper, the vector autoregressive models (ARV) method is adopted in the operational modal analysis (OMA) for this TV tower. The identified natural frequencies, damping ratios and mode shapes are presented and compared with the available results from some other research groups which used different methods, e.g., the data-driven stochastic subspace identification (SSI-DATA) method, the enhanced frequency domain decomposition (EFDD) algorithm, and an improved modal identification method based on NExT-ERA technique. Furthermore, the environmental effects on the estimated modal parameters are also discussed.

• Smart Structures and Systems
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• v.16 no.3
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• pp.435-457
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• 2015

The most widely known form of multifunctional aircraft structure is smart structures for structural health monitoring (SHM). The aim is to provide automated systems whose purposes are to identify and to characterize possible damage within structures by using a network of actuators and sensors. Unfortunately, environmental and operational variability render many of the proposed damage detection methods difficult to successfully be applied. In this paper, an original robust damage detection approach using output-only vibration data is proposed. It is based on independent component analysis and matrix perturbation analysis, where an analytical threshold is proposed to get rid of statistical assumptions usually performed in damage detection approach. The effectiveness of the proposed SHM method is demonstrated numerically using finite element simulations and experimentally through a conformal load-bearing antenna structure and composite plates instrumented with piezoelectric ceramic materials.