Structural Health Monitoring Algorithms Research Articles

Data-Driven Monitoring and Predictive Maintenance for Engineering Structures: Technologies, Implementation Challenges, and Future Directions

Estimating engineering structures’ health conditions and predicting their future behaviors are fundamental problems for a city’s safe and efficient operations. Data-driven solutions estimate the health conditions using statistical models generated from measurement data. They have attracted growing interest recently because advances in information and communication technologies (ICT) have enabled numerous real-world measurement data, and the flourishing big data community has provided enormous state-of-the-art data analytics algorithms. Nevertheless, most existing studies remain in numerical simulation while neglecting their real-world implementation, restricting the extensive development of data-driven methods. In this survey, we provide a structural overview of the past decade’s data-driven structural health monitoring (SHM) systems and algorithms from the perspective of real-world implementation. Specifically, we cover various aspects of the design and implementation of monitoring systems, including sensing technologies, communication technologies, and processing software. In addition, we classify the used data sources and statistical models with fined details. Under the proposed taxonomy, their limitations and advantages are thoroughly discussed. Based on our insights into existing studies, we clarify two major implementation challenges: the digressive performance in the real-world environment and inefficient computing systems for real-time data analytics. Possible solutions are then proposed to mitigate those challenges and promote the implementation of data-driven methods. Finally, we raise our outlook on future trends and suggest promising directions for further investigation.

Generated datasets from dynamic reproduction of projectiles in ballistic environments for advanced research (DROPBEAR) testbed

High-rate dynamics occur when a system’s acceleration is larger than 100 gn over durations less than 100 ms. Structural health monitoring algorithms must be created for high-rate dynamic systems to maximize safety and minimize economic losses. There is a need to evaluate these algorithms for precision and accuracy prior to real-world implementation. An experimental testbed was created to simulate large-magnitude events while maintaining repeatability to accurately and robustly assess various structural health monitoring algorithms’ capability to monitor high-rate dynamic systems. All previous datasets created on the experimental testbed are discussed, examining various sensor setups, excitations, and boundary condition changes to properly simulate near-high-rate events and provide robust experimental data to evaluate structural health monitoring algorithms.

Structural Health Monitoring in Composite Structures: A Comprehensive Review.

This study presents a comprehensive review of the history of research and development of different damage-detection methods in the realm of composite structures. Different fields of engineering, such as mechanical, architectural, civil, and aerospace engineering, benefit excellent mechanical properties of composite materials. Due to their heterogeneous nature, composite materials can suffer from several complex nonlinear damage modes, including impact damage, delamination, matrix crack, fiber breakage, and voids. Therefore, early damage detection of composite structures can help avoid catastrophic events and tragic consequences, such as airplane crashes, further demanding the development of robust structural health monitoring (SHM) algorithms. This study first reviews different non-destructive damage testing techniques, then investigates vibration-based damage-detection methods along with their respective pros and cons, and concludes with a thorough discussion of a nonlinear hybrid method termed the Vibro-Acoustic Modulation technique. Advanced signal processing, machine learning, and deep learning have been widely employed for solving damage-detection problems of composite structures. Therefore, all of these methods have been fully studied. Considering the wide use of a new generation of smart composites in different applications, a section is dedicated to these materials. At the end of this paper, some final remarks and suggestions for future work are presented.

Vibration response based crack diagnosis in beam-like structures using fuzzy inference system

Cracks in Beam-like structures can cause unexpected destruction of the structure leading to unintended damage to property and human life. This damage can be prevented by early detection of the crack. In this work, a Fuzzy Inference System is used to predict crack depth and location in a beam. The vibration signature of a healthy (uncrack) beam and a set of crack beams were obtained experimentally and characterized by the first Natural Frequency and statistical kurtosis. The inputs to the Fuzzy system were the first Natural Frequency and kurtosis of the vibration response signal. A system of Fuzzy rules was established by machine learning from the experimental data sets and applied to Triangular, Gaussian, Trapezoidal and Bell-shaped membership function sets. Dispersed standard deviations around the mean absolute errors were observed for the predictions. It was concluded that more clustered data sets for establishing the Fuzzy rules could improve precision of crack diagnosis. The presented study is capable of being used in an online Structural Health Monitoring algorithm to identify crack depth and location.

Dichotomy property of dispersion equation of guided waves propagating in anisotropic composite plates

Accurate prediction of dispersion curves for ultrasonic guided waves propagation in multi-layered composite laminates is crucial for the deployment of nondestructive testing procedures and structural health monitoring algorithms dedicated to aerospace composite materials. However, existing efforts mainly focused on finding ways to build complex-valued dispersion equations for guided waves (see transfer matrix method (TMM), global matrix method (GMM) and stiffness matrix method (SMM) for example) with little focus on developing efficient and stable numerical solving methods associated with the derived complex-valued equations. In this paper, the conditions under which complex-valued dispersion equations are either real- or purely imaginary-valued equations (termed as dichotomy property) are derived for both single- and multi-layered composite plates. With such a property, the complex-valued dispersion equations can be efficiently numerically solved within the real number field via the standard bisection method or the corrected phase change method. It is thus now possible to overcome numerical issues frequently reported in literature. Besides, a parallel computing technique is proposed in this paper to improve the computational efficiency of the traditional GMM. The proposed methodology provides a new standard framework to solve the dispersion equations which is stable, multipurpose, and numerically efficient.

A hybrid computational intelligence approach for structural damage detection using marine predator algorithm and feedforward neural networks

Finite element (FE) based structural health monitoring (SHM) algorithms seek to update structural damage indices through solving an optimisation problem in which the difference between the response of the real structure and a corresponding FE model to some excitation force is minimised. These techniques, therefore, exploit advanced optimisation algorithms to alleviate errors stemming from the lack of information or the use of highly noisy measured responses. This study proposes an effective approach for damage detection by using a recently developed novel swarm intelligence algorithm, i.e. the marine predator algorithm (MPA). In the proposed approach, optimal foraging strategy and marine memory are employed to improve the learning ability of feedforward neural networks. After training, the hybrid feedforward neural networks and marine predator algorithm, MPAFNN, produces the best combination of connection weights and biases. These weights and biases then are re-input to the networks for prediction. Firstly, the classification capability of the proposed algorithm is investigated in comparison with some well-known optimization algorithms such as particle swarm optimization (PSO), gravitational search algorithm (GSA), hybrid particle swarm optimization-gravitational search algorithm (PSOGSA), and grey wolf optimizer (GWO) via four classification benchmark problems. The superior and stable performance of MPAFNN proves its effectiveness. Then, the proposed method is applied for damage identification of three numerical models, i.e. a simply supported beam, a two-span continuous beam, and a laboratory free-free beam by using modal flexibility indices. The obtained results reveal the feasibility of the proposed approach in damage identification not only for different structures with single damage and multiple damage, but also considering noise effect.

Structural health monitoring with non-linear sensor measurements robust to unknown non-stationary input forcing

Bayesian filtering based structural health monitoring algorithms typically assume stationary white Gaussian noise models to represent an unknown input forcing. However, typical structural damages occur mostly under the action of extreme loading conditions, like earthquake or high wind/waves, which are characteristically non-stationary and non-Gaussian. Clearly, this invalidates this basic assumption, causing these algorithms to perform poorly under non-stationary noise conditions. This paper extends an existing interacting filtering algorithm to efficiently estimate structural damages while being robust to unknown non-stationary non-Gaussian input forcing. Furthermore, this approach is generalized beyond linear measurements to encompass the case of non-linear measurements such as strains. The joint estimation of state and parameters is performed by combining Ensemble Kalman filtering, for non-linear system state estimation, and Particle filtering to estimate changes in the structural parameters. The robustness against input forcing is achieved through an output injection approach embedded in the state filter equation. Numerical simulations for two kinds of response measurements (acceleration and strain) are performed on a 3D frame structure under different damage location and severity scenarios. The sensitivity with respect to noise and the impact of different sensor combinations have also been investigated.

Real-time structural health monitoring of nonlinear hysteretic structures using a fast and slow dynamics separation method

Structural health monitoring (SHM) is the process of identification, localisation, and quantification of structural damage due to external loads, such as an earthquake. SHM results simplify and automate typical visual or localised experimental approaches and enable more informed structural safety assessment and post-event retrofit. On-line SHM is of particular interest for rapid safety assessment by owners and civil defence authorities, and can also inform active control systems to further avoid or mitigate damage during an event. This significant advantage over off-line SHM has drawn many researchers to on-line or real-time methods. This paper presents an on-line SHM algorithm for model-based identification and monitoring of non-linear hysteretic structures. It separates fast and slow dynamics and uses robust piecewise least squares fitting over different time intervals to identify key structural parameters over time, including stiffness, damping, and the well-accepted Bouc–Wen hysteretic model parameters, which are directly related to well-recognised damage metrics. Proof-of-method simulations of a realistic nonlinear case study structure, subjected to a suite of 20 different ground motions, show the algorithm accurately identifies structural parameters to within 2.7% and 4.3% of the actual as-modelled values for stiffness and damping, respectively. Results for a range of major changes (damage) in structural parameters show the algorithm performs well in tracking these changes in real-time. Overall, the results present an accurate, real-time, model-based SHM method for highly nonlinear structures.

Parallel spectral element method for guided wave based structural health monitoring

Parallel implementation of the spectral element method is developed in which flat shell spectral elements are utilized for spatial domain representation. The implementation is realised by using Matlab Parallel Computing Toolbox and optimized for Graphics Processing Unit (GPU) computation. In this way, considerable computation speed-up can be achieved in comparison to computation on conventional processors. The implementation includes an interpolation of wavefield on a uniform grid. The method was tested on experimental data set available on the Open Guided Waves platform. The qualitative comparison was performed on full wavefield data, whereas quantitative comparison was made directly on signals of propagating Lamb waves registered by piezoelectric transducers. In both cases, good agreement between numerical and experimental results was achieved. The proposed method is particularly useful for structural health monitoring algorithms in which signal parameters are required such as wave velocity dependence on the angle of propagation. Moreover, it enables model–assisted damage size estimation in which the damage influence curve is estimated based on large numerical data sets and sparse experimental data. Due to relatively short computation times, large data sets can be generated and used for machine learning or other soft computing methods opening up new possibilities in health monitoring of metallic and composite structures.

An integrated algorithm for structural health monitoring of composite plate using electromechanical impedance

extra-ordinary development of structural materials, such as compositematerials, and structural configurations necessitated establishing robustand continuous structural health monitoring (SHM) systems. The aim ofthe present work is to establish an integrated SHM algorithm to remotelydetect damage starting from the insipient level for structures made ofcomposite materials. It depends on the electromechanical impedance(EMI) technique using Piezoelectric Waver Active Sensor (PWAS). Thealgorithm procedure includes several subroutines developed to ensurethe identification of real damage and relatively define its location andseverity precisely. The performance and the reliability of the proposedalgorithm are verified through numerical simulations carried out throughfinite element modeling of the monitored structure using ANSYSsoftware, since it is available to do multi-physics modeling to simulatecoupling characteristics of piezoelectricity. MATLAB codes aredeveloped to prepare and process the obtained numerical data in orderto implement the algorithm subroutines. Several numerical simulationstudies are carried out to verify the proposed algorithm procedure andmeasure its performance. Carbon woven fiber composite plates equippedwith a matrix of PWASs are assigned as test articles. Numerically, thealgorithm was shown to be beneficial to detect two different-location ofinsipient damages and two different-location of insipient and moderatedamages, as well as to locate these damages with accuracy more than80%. Reliability of data obtained from physical operating system wasguaranteed using sensors diagnostic check in advance to SHM process.

An open database for benchmarking guided waves structural health monitoring algorithms on a composite full-scale outer wing demonstrator

This article reports on the creation of an open database of piezo-actuated and piezo-received guided wave signals propagating in a composite panel of a full-scale aeronautical structure. The composite panel closes the bottom part of a wingbox that, along with the leading edge, the trailing edge, and the wingtip, forms an outer wing demonstrator approximately 4.5 m long and from 1.2 to 2.3 m wide. To create the database, a structural health monitoring system, composed of a software/hardware central unit capable of controlling a network of 160 piezoelectric transducers secondarily bonded on the composite panel, has been realized. The structural health monitoring system has been designed to (1) perform electromechanical impedance measurement at each transducer, in order to check for their reliability and bonding strength, and (2) to operate an active guided wave screening for damage detection in the composite panel. Electromechanical impedance and guided wave measurements were performed at four different testing stages: before loading, before fatigue, before impacts, and after impacts. The database, freely available at http://shm.ing.unibo.it/, can thus be used to benchmarking, on real-scale structural data, guided wave algorithms for loading, fatigue, as well as damage detection, characterization, and sizing. As an example, in this work, a delay and sum algorithm is applied on the post-impact data to illustrate how the database can be exploited.

Baseline-free damage imaging for metal and composite plate-type structures based on similar paths

Lamb wave–based structural health monitoring technology has attracted great interest and wide attention. Most of the Lamb wave–based structural health monitoring algorithms utilize scattering signals subtracted between baseline and current signals, which are recorded before and after damage and are easily influenced by environmental changes. In this article, a baseline-free method based on similar path is proposed to identify the damage in metal and composite structures. Due to Lamb wave scattering on damage, the first wave packet of signal for the transmitter–receiver path passing through or near the damage will be weakened. Based on sensor spacing (and direction for composites), all transmitter–receiver paths of transducer array can be classified as some groups, each having similar paths. The first wave packet energies of the paths in the same group are compared with each other to calculate damage indices, which are used to image the damage. Simulations were carried out to identify single- and multi-damages in the metal and composite plates. In addition, an experiment was also conducted to verify the algorithm. Both simulation and experimental results demonstrate the feasibility and effectiveness of similar path-based damage identification method.

Assessment of engineering demand parameters through SHM for RC-structures

Almost all in service and structures need some form of maintenance for monitoring their reliability and health conditions to extend their lifespans or to avoid cataclysmic failure. Structural health monitoring (SHM) is an advanced topic in the field of structural engineering that enables to monitor the structures for future safety. Considering this importance, the current research carries out SHM algorithm for RC high-rise building structures with shear walls. In this study, an approach applicable to real-time has been presented enabling decision-makers to assess the structural condition during the extreme cases. The parametric study has been done on ten stories RC residential building located in Walnut Creek, in the California State USA by considering four major recent earthquakes in the region. For algorithmic examination, general engineering demand parameters (EDPs) have been introduced. The EDPs such as spectral displacement (Sd), Inter-story drift ratio (∆s), Arias intensity (Ia) and spectral acceleration (Sa) limits have been investigated.

Structural health monitoring algorithm application to a powerboat model impacting on water surface

In naval field, live monitoring of local strains and displacements in the hull is the basis for dynamic studies such as checking the design limits, sea-keeping tests in smooth and rough seas, fatigue life estimation and damage detection. Vessels sailing on water are subject to impulsive loadings and local deformations; in these conditions the damage detection in real time becomes crucial. In this paper, a numerical methodology is proposed to measure the deformation of the whole structure of a powerboat entering the water free surface starting from local strain measurements, obtained numerically in a FE simulation. A modal decomposition approach has been used to reconstruct the structural response of the whole boat body. The reconstruction algorithm is calibrated for this study by means of the normalized modal strains matrix obtained through a FEA. A transient FE analysis is implemented to generate local strain signals from virtual sensors. In this analysis hydrodynamic loading resulting from well-known models are applied. The positioning and number of the virtual reference and control sensors are investigated. Virtual control sensors are utilized to compare strains with respect to the reconstructed quantities. Subsequently, the structural health monitoring algorithm has been applied to the powerboat model with a localized damage on the structure. The results reported in the paper reveal the capability of the method to detect the damage in real time.

Application Studies on Structural Modal Identification Toolsuite for Seismic Response of Shear Frame Structure

The improvement in computing systems and sensor technologies devotes to conduct data-driven structural health monitoring algorithms for existing civil infrastructures. Despite of the development of techniques, the uncertainty oriented from the measurement results in the discrepancy to the actual structural parameters and let engineers or decision makers hesitate to adopt such techniques. Many studies have shown that the modal identification results can be affected by the uncertainties due to the applied methods and the types of loading. This paper aims to compare the performance of modal identification methods using Structural Modal Identification Toolsuite (SMIT) which has been developed to facilitate multiple identification methods with a user-friendly designed platform. The data fed into SMIT processes three stages for the comprehensive identification including preprocessing, eigenvalue estimation, and post-processing. The seismic and white noise response for shear frame model was obtained from numerical simulation. The identified modal parameters is compared to the actual modal parameters. In order to improve the quality of coherence in identified modal parameters, several hurdles including modal phase collinearity and extended modal amplitude coherence were introduced. Numerical simulation conducted on the 5 dof shear frame model were used to validate the effectiveness of using these parameters.

Correntropy based IPKF filter for parameter estimation in presence of non-stationary noise process

Existing filtering based structural health monitoring (SHM) algorithms assume constant noise environment which does not always conform to the reality as noise is hardly stationary. Thus to ensure optimal solution even with non-stationary noise processes, the assumed statistical noise models have to be updated periodically. This work incorporates a modification in the existing Interacting Particle-Kalman Filter (IPKF) to enhance its detection capability in presence of non-stationary noise processes. To achieve noise adaptability, the proposed algorithm recursively estimates and updates the current noise statistics using the post-IPKF residual uncertainty in prediction as a measurement which in turn enhances the optimality in the solution as well.Further, this algorithm also attempts to mitigate the ill effects of abrupt change in noise statistics which most often deteriorates/ diverges the estimation. For this, the Kalman filters (KF) within the IPKF have been replaced with a maximum Correntropy criterion (MCC) based KF that, unlike regular KF, takes moments beyond second order into consideration. A Gaussian kernel for MCC criterion is employed to define a correntropy index that controls the update in state and noise estimates in each recursive steps. Numerical experiments on an eight degrees-of-freedom system establish the potential of this algorithm in real field applications.

Linear and Nonlinear Dynamic Analyses of Sandwich Panels with Face Sheet-to-Core Debonding

A survey of recent developments in the dynamic analysis of sandwich panels with face sheet-to-core debonding is presented. The finite element method within the ABAQUS™ code is utilized. The emphasis is directed to the procedures used to elaborate linear and nonlinear models and to predict dynamic response of the sandwich panels. Recently developed models are presented, which can be applied for structural health monitoring algorithms of real-scale sandwich panels. First, various popular theories of intact sandwich panels are briefly mentioned and a model is proposed to effectively analyse the modal dynamics of debonded and damaged (due to impact) sandwich panels. The influences of debonding size, form, and location and number of such damage incidents on the modal characteristics of sandwich panels are shown. For nonlinear analysis, models based on implicit and explicit time integration schemes are presented and dynamic responses gained with those models are discussed. Finally, questions related to debonding progression at the face sheet-core interface when dynamic loading continues with time are briefly highlighted.

Lossless In-Network Processing in WSNs for Domain-Specific Monitoring Applications

Internet of things (IOT) is emerging as sensing paradigms in many domain-specific monitoring applications in smart cities, such as structural health monitoring (SHM) and smart grid monitoring. Due to the large size of the monitoring objects (e.g., civil structure or the power grid), plenty of sensors need to be deployed and organized to be a large scale of multihop wireless sensor networks (WSNs), which tends to have quite high transmission cost. In-network processing is an efficient way to reduce the transmission cost in WSNs. However, implementing in-network processing for above domain-specific monitoring usually requires to losslessly distribute a dedicate domain-specific algorithm into WSNs, which is much different from most existing in-network processing works. This paper conducts a case study of a classic centralized SHM algorithm, i.e., eigensystem realization algorithm (ERA), and shows how to losslessly and optimally in-network process ERA, especially the typical feature extraction method, i.e., that is singular value decomposition (SVD) therein, in a WSN. Based on whether the intermediate data can be processed together or not by sensor nodes, we respectively implement tree-based in-network processing of SVD and chain-based in-network processing of SVD in WSNs. We prove that using an appropriate shallow light tree as routes for tree-based in-network processing of SVD, can achieve the approximation ratio ${\text{1}}+\sqrt{2}$ (in terms of transmission cost), while for the chain-based in-network processing of SVD, we design two efficient heuristic algorithms for searching the optimal routes. Extensive simulation results validate the efficiency of these proposed schemes that are customized for SVD-based IOT applications.

A structural health monitoring system based on multifractal detrended cross-correlation analysis

In recent years, multifractal-based analysis methods have been widely applied in engineering. Among these methods, multifractal detrended cross-correlation analysis (MFDXA), a branch of fractal analysis, has been successfully applied in the fields of finance and biomedicine. For its great potential in reflecting the subtle characteristic among signals, a structural health monitoring (SHM) system based on MFDXA is proposed. In this system, damage assessment is conducted by exploiting the concept of multifractal theory to quantify the complexity of the vibration signal measured from a structure. According to the proposed algorithm, the damage condition is first distinguished by multifractal detrended fluctuation analysis. Subsequently, the relationship between the q-order, q-order detrended covariance, and length of segment is further explored. The dissimilarity between damaged and undamaged cases is visualized on contour diagrams, and the damage location can thus be detected using signals measured from different floors. Moreover, a damage index is proposed to efficiently enhance the SHM process. A sevenstory benchmark structure, located at the National Center for Research on Earthquake Engineering (NCREE), was employed for an experimental verification to demonstrate the performance of the proposed SHM algorithm. According to the results, the damage condition and orientation could be correctly identified using the MFDXA algorithm and the proposed damage index. Since only the ambient vibration signal is required along with a set of initial reference measurements, the proposed SHM system can provide a lower cost, efficient, and reliable monitoring process.

Multifunctional Piezopolymer Film Transducer for Structural Health Monitoring Applications

This paper describes the design of a multifunctional transducer for structural health monitoring (SHM) applications that integrates an interdigital piezoelectric transducer, used for Lamb wave generation and reception, a circular piezoelectric sensor, used for the detection and localization of low-velocity impacts, and a resistance temperature detector (RTD) in a single device. The three elements were fabricated on the same metallized piezoelectric polyvinylidene fluoride (PVDF) film using a laser etching process. Characterization of the resulting device proved the advantages of having different transducers on the same device, providing information useful to implement advanced SHM algorithms. Testing of the RTD highlighted some criticalities of the metallized PVDF film that prevented accurate temperature measurements due to spurious behavior under strain.