Proposed Structural Health Monitoring System Research Articles

An edge-computing framework for operational modal analysis of offshore wind-turbine tower

With massive construction of offshore wind turbines (OWT), structural health monitoring (SHM) techniques have been applied to estimate the working conditions and provide early warnings of structural damage. This study proposes an edge-computing-based SHM system for operational modal analysis (OMA) of an OWT. ThssCe system comprised an open-source data acquisition (DAQ) device, a Raspberry Pi single-board computer, and associated drivers, protocols, and algorithms. The DAQ device can measure the acceleration response of an operating OWT subjected to environmental excitation and transmit raw signals to the Raspberry Pi according to the UDP protocol. The data are further processed to obtain the first-order frequency via an OMA algorithm developed by combining Kalman filtering, the random decrement technique (RDT), and the stochastic subspace identification (SSI) method. The proposed SHM system was implemented in a scaled OWT model to verify its feasibility for field use. The experimental results indicate that (1) the edge-computing-based SHM system can accurately measure the response of the OWT; (2) the proposed algorithm can estimate the modal information of the OWT during operation; (3) the variation in the first-order frequency induced by lateral loads can be identified by the SHM system. Unlike a centralized SHM system, the proposed edge-computing-based SHM system does not require massive data transmission, making it promising for offshore environments.

Damage Detection of Regular Civil Buildings Using Modified Multi-Scale Symbolic Dynamic Entropy.

Based on the examination of the fundamental characteristics of structures, structural health monitoring (SHM) has received increased attention in recent years. Studies have shown that the SHM method using entropy analysis can precisely identify the damaged location of the structure, which is very helpful for the daily inspection or maintenance of civil structures. Although entropy analysis has shown excellent accuracy, it still consumes too much time and too many resources in terms of data processing. To improve the dilemma, in this study, modified multi-scale symbolic dynamic entropy (MMSDE) is adopted to identify the damaged location of the civil structure. A damage index (DI) based on the entropy diagram is also proposed to clearly indicate the damage location. A seven-story numerical model was created to verify the efficiency of the proposed SHM system. The results of the analysis of each case of damage show that the MMSDE curve for the damaged floor is lower than that for the healthy floor, and the structural damage can be correctly diagnosed by the damage index. Subsequently, a scaled-down steel benchmark experiment, including 15 damage cases, was conducted to verify the practical performance of the SHM system. The confusion matrix was used to further evaluate the SHM system. The results demonstrated that the MMSD-based system can quickly diagnose structural safety with reliability and accuracy. It can be used in the field of long-term structural health monitoring in the near future.

Flexural Damage Diagnosis in Reinforced Concrete Beams Using a Wireless Admittance Monitoring System-Tests and Finite Element Analysis.

The utilization and effectiveness of a custom-made, portable and low-cost structural health monitoring (SHM) system that implements the PZT-based electro-mechanical admittance (EMA) methodology for the detection and evaluation of the damage of flexural reinforced concrete (RC) beams is presented. Tests of large-scale beams under monotonic and cyclic reversal-imposed deformations have been carried out using an integrated wireless impedance/admittance monitoring system (WiAMS) that employs the voltage measurements of PZT transducers. Small-sized PZT patches that have been epoxy-bonded on the steel bars surface and on the external concrete face of the beams are utilized to diagnose damages caused by steel yielding and concrete cracking. Excitations and simultaneous measurements of the voltage signal responses of the PZT transducers have been carried out at different levels of the applied load during the tests using the developed SHM devices, which are remotely controlled by a terminal emulator. Each PZT output voltage versus frequency response is transferred wireless and in real-time. Statistical index values are calculated based on the signals of the PZT transducers to represent the differences between their baseline response at the healthy state of the beam and their response at each loading/damage level. Finite Element Modeling (FEM) simulation of the tested beams has also been performed to acquire numerical results concerning the internal cracks, the steel strains and the energy dissipation and instability parameters. FEM analyses are used to verify the experimental results and to support the visual observations for a more precise damage evaluation. Findings of this study indicate that the proposed SHM system with the implementation of two different PZT transducer settings can be effectively utilized for the assessment of structural damage caused by concrete cracking and steel yielding in flexural beams under monotonic and cyclic loading.

Design and Validation of a Scalable, Reconfigurable and Low-Cost Structural Health Monitoring System.

This paper presents the design, development and testing of a low-cost Structural Health Monitoring (SHM) system based on MEMS (Micro Electro-Mechanical Systems) triaxial accelerometers. A new control system composed by a myRIO platform, managed by specific LabVIEW software, has been developed. The LabVIEW software also computes the frequency response functions for the subsequent modal analysis. The proposed SHM system was validated by comparing the data measured by this set-up with a conventional SHM system based on piezoelectric accelerometers. After carrying out some validation tests, a high correlation can be appreciated in the behavior of both systems, being possible to conclude that the proposed system is sufficiently accurate and sensitive for operative purposes, apart from being significantly more affordable than the traditional one.

Damage detection in operational wind turbine blades using a new approach based on machine learning

The application of reliable structural health monitoring (SHM) technologies to operational wind turbine blades is a challenging task, due to the uncertain nature of the environments they operate in. In this paper, a novel SHM methodology, which uses Gaussian Processes (GPs) is proposed. The methodology takes advantage of the fact that the blades on a turbine are nominally identical in structural properties and encounter the same environmental and operational variables (EOVs). The properties of interest are the first edgewise frequencies of the blades. The GPs are used to predict the edge frequencies of one blade given that of another, after these relationships between the pairs of blades have been learned when the blades are in a healthy state. In using this approach, the proposed SHM methodology is able to identify when the blades start behaving differently from one another over time. To validate the concept, the proposed SHM system is applied to real onshore wind turbine blade data, where some form of damage was known to have taken place. X-bar control chart analysis of the residual errors between the GP predictions and actual frequencies show that the system successfully identified early onset of damage as early as six months before it was identified and remedied.

Composite Multiscale Cross-Sample Entropy Analysis for Long-Term Structural Health Monitoring of Residential Buildings.

This study proposesd a novel, entropy-based structural health monitoring (SHM) system for measuring microvibration signals generated by actual buildings. A structural health diagnosis interface was established for demonstration purposes. To enhance the reliability and accuracy of entropy evaluation at various scales, composite multiscale cross-sample entropy (CMSCE) was adopted to increase the number of coarse-grained time series. The degree of similarity and asynchrony between ambient vibration signals measured on adjacent floors was used as an in-dicator for structural health assessment. A residential building that has been monitored since 1994 was selected for long-term monitoring. The accumulated database, including both the earthquake and ambient vibrations in each seismic event, provided the possibility to evaluate the practicability of the CMSCE-based method. Entropy curves obtained for each of the years, as well as the stable trend of the corresponding damage index (DI) graphs, demonstrated the relia-bility of the proposed SHM system. Moreover, two large earthquake events that occurred near the monitoring site were analyzed. The results revealed that the entropy values may have been slightly increased after the earthquakes. Positive DI values were obtained for higher floors, which could provide an early warning of structural instability. The proposed SHM system is highly stable and practical.

Structural Health Monitoring using deep learning with optimal finite element model generated data

Identifying damage through Structural Health Monitoring (SHM) methods is increasingly attracting attention due to multiple maintenance and failure prevention applications. In order to create reliable SHM systems for structural damage identification (DI) tasks, access to large amounts of data containing measured structural responses is usually necessary. The data acquisition is mostly based on direct experimental responses up to now and requires time consuming measurements in various working and ambient conditions of the structure. In the present work, a novel SHM method is tested where all data is solely derived from FE calculated responses, after an initial experimental cost for FE model updating on the healthy structure state. The proposed method can be especially applied in cases where specific damage types are expected or anomalies are adequately defined so they can be effectively simulated by FE models. Origin of such models may then be the healthy experimental status. To test the proposed SHM system, the optimal FE model of an experimental benchmark linear beam structure is constructed, simulating an undamaged condition. In order to check the robustness of the proposed method the damage magnitudes imposed on the benchmark are kept small and combined with random excitations. Next, the optimal FE model is used for generating labeled SHM vibration data through a repetitive load case scheme which also includes uncertainties simulation. The data derived from the optimal FE model is finally used to train a Deep Learning (DL) Convolutional Neural Network (CNN) classifier which is after experimentally validated on the benchmark structure. The optimal FE generated data proves to be able to train an accurate CNN that can predict adequately the experimental benchmark states. A comparison is also given with a CNN trained by the corresponding nominal FE model data which is found not reliable on the experimental validations. The presented combination of optimal FE and DL is a potential solution for future SHM tools and further investigation is encouraged.

Integration of Refined Composite Multiscale Cross-Sample Entropy and Backpropagation Neural Networks for Structural Health Monitoring

This study developed a structural health monitoring (SHM) system based on refined composite multiscale cross-sample entropy (RCMCSE) and an artificial neural network for monitoring structures under ambient vibrations. RCMCSE was applied to enhance the reliability of entropy estimations. First, RCMCSE was implemented to extract damage features, and finite element analysis software was then used to generate training samples, which included stiffness reductions to achieve various damage patterns. A neural network model was constructed and trained using entropy values for these damage patterns. An experiment was conducted on a seven-story steel benchmark structure to validate the performance of the proposed system. Additionally, a confusion matrix was established to evaluate the performance of the proposed system. The results obtained for a scaled-down benchmark structure indicated that 89.8% of the floors were accurately classified, and 90% of the practical damaged floors were correctly diagnosed. The performance evaluation demonstrated that the proposed SHM system exhibited increased damage location accuracy.

A variable-frequency structural health monitoring system based on omnidirectional shear horizontal wave piezoelectric transducers

Structural health monitoring (SHM) is of great importance for engineering structures as it may detect the early degradation and thus avoid life and financial loss. Guided wave based inspection is very useful in SHM due to its capability for long distance and wide range monitoring. The fundamental shear horizontal (SH0) wave based method should be most promising since SH0 is the unique non-dispersive wave mode in plate-like structures. In this work, a sparse array SHM system based on omnidirectional SH wave piezoelectric transducers (OSH-PT) was proposed and the multi data fusion method was used for defect inspection in a 2 mm thick aluminum plate. Firstly, the performances of three types OSH-PTs was comprehensively compared and the thickness-poled d15 mode OSH-PT used in this work was demonstrated obviously superior to the other two. Then, the signal processing method and imaging algorithm for this SHM system was presented. Finally, experiments were carried out to examine the performance of the proposed SHM system in defect localization and imaging. Results indicated that this SHM system can locate a through hole as small as 0.12λ (4 mm) in diameter (where λ is the wavelength corresponding to the central operation frequency) under frequencies from 90 to 150 kHz. It can also locate multiple defects accurately based on the baseline subtraction method. Obviously, this SHM system can detect larger areas with sparse sensors because of the adopted single mode, non-dispersive and low frequency SH0 wave which can propagate long distance with small attenuation. Considering its good performances, simple data processing and sparse array, this SH0 wave-based SHM system is expected to greatly promote the applications of guided wave inspection.

Entropy-Based Structural Health Monitoring System for Damage Detection in Multi-Bay Three-Dimensional Structures.

In this paper, a structural health monitoring (SHM) system based on multi-scale cross-sample entropy (MSCE) is proposed for detecting damage locations in multi-bay three-dimensional structures. The location of damage is evaluated for each bay through MSCE analysis by examining the degree of dissimilarity between the response signals of vertically-adjacent floors. Subsequently, the results are quantified using the damage index (DI). The performance of the proposed SHM system was determined in this study by performing a finite element analysis of a multi-bay seven-story structure. The derived results revealed that the SHM system successfully detected the damaged floors and their respective directions for several cases. The proposed system provides a preliminary assessment of which bay has been more severely affected. Thus, the effectiveness and high potential of the SHM system for locating damage in large and complex structures rapidly and at low cost are demonstrated.

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.

Damage detection of structures with detrended fluctuation and detrended cross-correlation analyses

Recently, fractal analysis has shown its potential for damage detection and assessment in fields such as biomedical and mechanical engineering. For its practicability in interpreting irregular, complex, and disordered phenomena, a structural health monitoring (SHM) system based on detrended fluctuation analysis (DFA) and detrended cross-correlation analysis (DCCA) is proposed. First, damage conditions can be swiftly detected by evaluating ambient vibration signals measured from a structure through DFA. Damage locations can then be determined by analyzing the cross correlation of signals of different floors by applying DCCA. A damage index is also proposed based on multi-scale DCCA curves to improve the damage location accuracy. To verify the performance of the proposed SHM system, a four-story numerical model was used to simulate various damage conditions with different noise levels. Furthermore, an experimental verification was conducted on a seven-story benchmark structure to assess the potential damage. The results revealed that the DFA method could detect the damage conditions satisfactorily, and damage locations can be identified through the DCCA method with an accuracy of 75%. Moreover, damage locations can be correctly assessed by the damage index method with an improved accuracy of 87.5%. The proposed SHM system has promising application in practical implementations.

Application of multi-scale (cross-) sample entropy for structural health monitoring

This study proposes an information-theoretic structural health monitoring (SHM) system based on multi-scale entropy (MSE) and multi-scale cross-sample entropy (MSCE). By measuring the ambient vibration signal from a structure, the damage condition can be rapidly evaluated via MSE analysis. The damage location can then be detected by analyzing the signals of different floors under the same damage condition via MSCE analysis. Moreover, a damage index is proposed to efficiently quantify the SHM process. Unlike some existing SHM methods, no experimental database or numerical model is required. Instead, a reference measurement of the current stage can initiate and launch the SHM system. A numerical simulation of a four-story steel structure is used to verify that the damage location and condition can be detected by the proposed SHM algorithm, and the location can be efficiently quantified by the damage index. A seven-story scaled-down benchmark structure is then employed for experimental verification. Based on the results, the damage condition can be correctly assessed, and average accuracy rates of 63.4 and 86.6% for the damage location can be achieved using the MSCE and damage index methods, respectively. As only the ambient vibration signal is required with a set of initial reference measurements, the proposed SHM system can be implemented practically with low cost.

A bio-inspired structural health monitoring system based on ambient vibration

A structural health monitoring (SHM) system based on naïve Bayesian (NB) damageclassification and DNA-like expression data was developed in this research. Adapted fromthe deoxyribonucleic acid (DNA) array concept in molecular biology, the proposedstructural health monitoring system is constructed utilizing a double-tier regressionprocess to extract the expression array from the structural time history recordedduring external excitations. The extracted array is symbolized as the variousgenes of the structure from the viewpoint of molecular biology and reflects thepossible damage conditions prevalent in the structure. A scaled down, six-story steelbuilding mounted on the shaking table of the National Center for Research onEarthquake Engineering (NCREE) was used as the benchmark. The structuralresponse at different damage levels and locations under ambient vibration wascollected to support the database for the proposed SHM system. To improve theprecision of detection in practical applications, the system was enhanced by anoptimization process using the likelihood selection method. The obtained arrayrepresenting the DNA array of the health condition of the structure was first evaluatedand ranked. A total of 12 groups of expression arrays were regenerated from acombination of four damage conditions. To keep the length of the array unchanged,the best 16 coefficients from every expression array were selected to form theoptimized SHM system. Test results from the ambient vibrations showed thatthe detection accuracy of the structural damage could be greatly enhanced bythe optimized expression array, when compared to the original system. Practicalverification also demonstrated that a rapid and reliable result could be givenby the final system within 1 min. The proposed system implements the idea oftransplanting the DNA array concept from molecular biology into the field of SHM.

Structural Health Monitoring of an Advanced Grid Structure with Embedded Fiber Bragg Grating Sensors

The authors focus on the construction of a structural health monitoring (SHM) system with an advanced grid structure (AGS) made of carbon-fiber reinforced plastic (CFRP). AGS is often applied to aerospace structures because the ribs carry only axial forces in the carbon fiber direction, making AGS structurally effective and lightweight, and because the repetition of many ribs in the AGS composition results in damage tolerance. The failure of a single rib hardly affects the fracture of the whole structure, making AGS a fail-safe structure. In this research, the authors have embedded multiplexed-fiber Bragg grating (FBG) sensors into an AGS rib in the longitudinal direction to measure mechanical strains of all ribs in order to detect the existence and regions of AGS rib fractures. Monitoring the change in rib-longitudinal strains is the most effective SHM system for AGS. To confirm the proposal, the authors explore the following. First, various damage characteristics under low-velocity impact loading are investigated and it is verified that partial rib cracks are the most typical damage in AGS. An AGS is then fabricated with embedded FBG sensors and verified that the SHM system is able to measure all rib strains. Subsequently, it is analytically determined that the change in longitudinal-rib strains is the most appropriate mechanical feature for damage detection. Moreover, a statistical outlier analysis is introduced into the SHM system for automatic damage detection. Finally, AGS is established with the SHM system and verified experimentally. Results confirm that the existence of damage and its regions in AGS can be detected with the proposed SHM system.