Structural Health Monitoring Methods Research Articles

Generalized Shannon entropy sparse wavelet packet transform for fault detection of traction motor bearings in high-speed trains

An effective structural health monitoring method of traction motor bearings is a powerful guarantee for the safety operation of high-speed trains. However, it is exceptionally difficult to detect bearing fault characteristics from the vibration signals of traction motor bearings operating at high rotational speeds. In this scenario, a generalized Shannon entropy sparse wavelet packet transform (GSWPT) for fault detection of motor bearings is proposed in this paper. Firstly, a generalized Shannon entropy sparse regularization method is proposed to obtain sparse wavelet reconstruction coefficients by extending the definition of the Shannon information entropy, and the non-convex sparse regularization function is minimized by synergistic swarm optimization algorithm. Then, the wavelet node coefficients are weighted according to the second-order cyclostationarity index of the wavelet packet node to further enhance the sparsity of the reconstructed signal. Moreover, the optimal decomposition level of GSWPT is adaptively selected by the maximum sparsity and cyclostationarity criterion. Particularly, in order to verify the bearing fault detection performance of GSWPT in practical engineering, a bearing fault dynamic model of traction motor in high-speed train was established based on Hertz contact theory and the fourth-order Runge-Kutta method to obtain simulated data under strong Gaussian white noise, and a corresponding test platform was constructed to collect experimental data under different operating conditions. Finally, the applications on the simulated and experimental signals of traction motor bearings in high-speed trains demonstrate that GSWPT significantly outperforms the conventional wavelet packet transform, dual-tree complex wavelet packet transform, blind deconvolution, modal decomposition, and Infogram methods to some extent for fault detection.

Motion state factor driven for doubly-curved shallow shell deformation reconstruction

The conventional shape sensing method, based on C0 continuity, is recognized for its computational efficiency. However, it is prone to shear locking due to low-order polynomial interpolation in shell analysis, introducing two primary challenges. Firstly, it lacks consideration for C1 continuity. Secondly, existing high-order polynomial interpolation methods significantly increase the number of degrees of freedom (DOF), reducing computational efficiency. Therefore, a brand-new real-time structural health monitoring (SHM) method driven by motion state factor instead of nodal factors is proposed. This method accommodates all deformation modes of shallow shells with C1 continuity and improves the shape function's order without additional DOF. It begins with deriving motion states from compatibility equations, leading to a polynomial shape function expression. Subsequently, conversion equations for real constants and DOF are developed, establishing a least-squares model for theoretical strains and measured values. Following that, the higher-order element formula, optimized for minimal DOF, is then inferred from varied displacement data. Finally, simulations and experiments demonstrate this method's effectiveness, accuracy, and reliability in analyzing shallow shell elements.

Effect of Signal Features and Model Variables on Energy-Traced Arrival Time Picking of Acoustic Signals Used for Structural Damage Detection

To monitor damage developments in structures, various structural health monitoring methods based on different principles are used. The common aspect of elastic wave-based methods is to place appropriate sensors on the structure, to detect acoustic wave propagation and to analyze these signals the sensors transformed. The arrival time of these recorded signals to the sensors is the most significant parameter used to determine critical information such as the time and location of the damage. Therefore, the accurate calculation of the arrival time affects the accuracy of the damage detection. In this study, effects of the signal-to-noise ratio (SNR), sampling frequency, length of the signal, and length of the focal window on determining the arrival time of the signals to the sensors were investigated. For this purpose, an energy-traced arrival time picking approach (Akaike Information Criterion, AIC), which is the frequently used method in the literature, has been applied to a typical acoustic signal originated from a concrete cracking. The results of the study suggest the necessity of noise elimination, the optimum level of data logging and the ratios of focal window lengths for accurate time of arrival detection in the field monitoring of the structures using acoustic methods.

Loose bolt localization and torque prediction in a bolted joint using lamb waves and explainable artificial intelligence

With the increasing application of artificial intelligence (AI) techniques in the field of structural health monitoring (SHM), there is a growing interest in explaining the decision-making of the black-box models in deep learning-based SHM methods. In this work, we take explainability a step further by using it to improve the performance of AI models. In this work, the results of explainable artificial intelligence (XAI) algorithms are used to reduce the input size of a one-dimensional convolutional neural network (1D-CNN), hence simplifying the CNN structure. To select the most accurate XAI algorithm for this purpose, we propose a new evaluation method, feature sensitivity (FS). Utilizing XAI and FS, a reduced dimension 1D-CNN regression model (FS-X1D-CNN) is proposed to locate and predict the torque of loose bolts in a 16-bolt connected aluminum plate under varying temperature conditions. The results were compared with 1D CNN with raw input vector (RI-1D-CNN) and deep autoencoders-1D-CNN (DAE-1D-CNN). It is shown that FS-X1D-CNN achieves the highest prediction accuracy with 5.95 mm in localization and 0.54 Nm in torque prediction, and converges 10 times faster than RI-1D-CNN and 15 times faster than DAE-1D-CNN, while only using a single lamb wave signal path.

Maximum likelihood estimation of probabilistically described loads in beam structures

In recent years, focus has been shifted towards predictive maintenance in an effort to improve the reliability of operating structures. Processing structural response data obtained from in-situ sensors during operation can provide added value towards this direction. Structural Health Monitoring (SHM) methods are uniquely suited for this task; however, accounting for the effect of stochastic structural loads is critical for their robustness. In this work, a framework based on Maximum Likelihood Estimation (MLE) is presented, whose goal is to obtain inferences on typically unobservable quantities that describe stochastic structural loading. A structural beam is employed as a demonstrative case study, that is subjected to point loads with stochastic magnitude and application points. The hyperparameters that govern their underlying probability distribution functions (pdf) are the quantities of inferential interest. The inverse (load) identification process is performed using a marginalized MLE objective, where stochastic Monte Carlo (MC) integration is employed to perform the marginalization and Genetic Algorithms (GAs) are used as the optimizer. The Cramer–Rao (CR) lower bound is used to produce 95 % Confidence Intervals (CIs) to quantify estimation uncertainty.

Augmented Reality-Based Real-Time Visualization for Structural Modal Identification.

In the era of aging civil infrastructure and growing concerns about rapid structural deterioration due to climate change, the demand for real-time structural health monitoring (SHM) techniques has been predominant worldwide. Traditional SHM methods face challenges, including delays in processing acquired data from large structures, time-intensive dense instrumentation, and visualization of real-time structural information. To address these issues, this paper develops a novel real-time visualization method using Augmented Reality (AR) to enhance vibration-based onsite structural inspections. The proposed approach presents a visualization system designed for real-time fieldwork, enabling detailed multi-sensor analyses within the immersive environment of AR. Leveraging the remote connectivity of the AR device, real-time communication is established with an external database and Python library through a web server, expanding the analytical capabilities of data acquisition, and data processing, such as modal identification, and the resulting visualization of SHM information. The proposed system allows live visualization of time-domain, frequency-domain, and system identification information through AR. This paper provides an overview of the proposed technology and presents the results of a lab-scale experimental model. It is concluded that the proposed approach yields accurate processing of real-time data and visualization of system identification information by highlighting its potential to enhance efficiency and safety in SHM by integrating AR technology with real-world fieldwork.

Development and experimental validation of a prototype system for Machine Learning based SHM in composite aerostructures

The development and experimental validation of a Machine Learning (ML) vibration based Structural Health Monitoring (SHM) prototype system for composite aerostructures operating under uncertainty is presented. Following initial training, it is intended to operate autonomously, in almost real-time, without interrupting normal operation, relying on a limited number of acceleration and strain measurements from properly selected locations on the aerostructure. It is equipped with a multi-level information fusion methodology comprising multiple sensor technologies and diagnostic algorithms for optimized SHM performance in terms of damage detection and characterization. Through its flexible software, the system provides multiple options of ML based SHM methods targeting wide applicability to various types of structures. The system is validated based on numerous experiments with healthy and damaged full-scale composite bonded spars operating under multiple uncertainty factors. Its diagnostic performance indicates that the current progress in hardware and robust vibration-based Machine Learning algorithms may lead to compact, effective, and low cost SHM systems for composite aerostructures under normal operating conditions.

Advanced methods for the detection of measurement outliers in fibre optic strain measurements for non-destructive monitoring of the structural health of composite over-wrapped pressure vessels of type-IV

To analyse the applicability of fibre optic measurements as a measurement method for structural health monitoring, fibre-optic sensors are placed on the vessel surface. The influence of the surface profile of the vessel on the measurement result is analysed by correlating the 3D scanned surface and measurement results. One outcome is that the measurement is substantially influenced by the surface topography. The surface height can be directly correlated to the strain magnitude, which is also verified by analytical calculations. Outliers and invalid values can be traced back to surface-related influences on the sensor fibre. For the outlier detection, a novel method is presented, which is based on quantitative evaluation criteria and inductive reasoning and enables transient cycle- by-cycle outlier detection adapted to the load case. Based on the results an estimate for the expanded measurement uncertainty is given for of the measurement chain consisting of vessel, adhesive and sensor fibre.

Adaptive crack damage identification based on multi-scale sample entropy under variable temperature environment

High-speed trains inevitably experience the impact of complex temperature loads during operation, which can easily result in cracks. Structural health monitoring methods based on Lamb waves are affected by the impact of temperature on their propagation mechanism, leading to difficulties in constructing accurate damage diagnostic models. This paper proposes a crack damage identification method based on adaptive multi-scale sample entropy under variable temperature environment. A sliding window method is proposed to obtain multiple wave packets in a Lamb wave, which avoids the disadvantage of insufficient information of a single wave packet and improves the robustness and reliability of diagnosis. A variance-based multiscale transform method is proposed to process Lamb wave signals, which reduces the sensitivity of Lamb wave signals to temperature compared to the traditional mean-based multiscale transform method. Lamb waves in different sliding windows are transformed at multiple scales to extract damage Multi-scale sample entropy (MSE) at different scales. The Quantum genetic algorithm (QGA) is introduced to optimize sliding windows and MSE, achieving adaptive MSE extraction under variable temperature environments. Based on the advantage of adaptive multiscale entropy, a quantitative crack diagnosis model is established. To verify the effectiveness of the proposed method, crack detection experiments under variable temperatures were conducted. The results show that the proposed adaptive MSE has good resistance to temperature changes and can accurately identify crack length.

A new PZT-enabled device for monitoring prestress loss in post-tensioned prestressed structures

ABSTRACT Anchorage performance monitoring plays an important role in ensuring the safety of prestressed structures. Over the past decades, traditional structural health monitoring (SHM) methods, such as vibration, magnetoelastic, and acoustoelastic methods, have been plagued by low sensitivity and high susceptibility to environmental and temperature interference. Lead-Zirconate-Titanate (PZT)-enabled active sensing method has proven its effectiveness in prestress monitoring. The main contribution of this paper is the development of a new device that utilises the relationship between energy transfer and contact stress at the interface. This device addresses the existing problem of signal saturation and offers higher sensitivity for practical implementation. First, the time reversal (TR) method was used to overcome the problem of low signal-to-noise ratio (SNR) in traditional methods. Based on this, the empirical mode decomposition (EMD) method was employed to process the acquired signals, thereby improving the stability of the data. A quantitative index for monitoring prestress loss was proposed by normalising the peak value of the reconstructed signals. Finally, several repeated experiments were conducted to verify the accuracy of the proposed method.

Detection, Localization, and Quantification of Damage in Structures via Artificial Neural Networks

This paper presents a structural health monitoring method based on artificial neural networks (ANNs) capable of detecting, locating, and quantifying damage in a single stage. The proposed framework employs a supervised neural network model that uses input factors calculated by modal parameters (natural frequencies or mode shapes), and output factors that represent the damage situation of elements or regions in a structural system. Unlike many papers in the literature that test damage detection methods only in numerical examples or simple experimental tests, this work also assesses the presented method in a real structure showing that it has potential for applications in real practical situations. Three different cases are evaluated through the methodology: numerical simulations, an experimental lab structure, and a real bridge. Initially, a cantilever beam and a 10-bar truss were numerically analyzed under ambient vibrations with different damage scenarios and noise levels. Afterward, the method is assessed in an experimental beam structure and in the Z24 bridge benchmark. The numerical simulations showed that the methodology is promising for identifying, locating, and quantifying single and multiple damages in a single stage, even with noise in the acceleration signals and changes in the first vibration mode of 0.015%. In addition, the Z24 bridge study confirmed that the damage detection method can localize damage in real civil structures considering only natural frequencies in the input factors, despite a mean difference of 4.08% between the frequencies in the healthy and damaged conditions.

A multi-layer ML model evolutionary paradigm for high-accuracy individual aircraft SHM

Evolutionary Machine Learning (EML) has emerged as a novel direction for providing robust methodologies for effectively tackling complex problems. However, most current research focuses on multi-objective optimization problems in machine learning (ML). There is limited research on the EML mechanism along the temporal application process, where a fixed machine learning model trained with the prior dataset will inevitably be not representative of the target problem with time. Taking the Structural Health Monitoring (SHM)-ML damage diagnosis model as an example, this paper proposes a novel multi-layer ML model evolutionary paradigm with multi-source data for high-accuracy individual aircraft SHM. The proposed paradigm considers three interconnected layers for the SHM-ML damage diagnosis model, including the dataset to establish the diagnosis model, the model structure and its parameter training, and mapping relationship used in the model to give the damage size. Besides, for a specific model of aircraft, there exist multiple stages from design to service, where multi-source of data is fused for the multi-layer evolution, enabling continuous improvement of damage diagnosis accuracy. Experimental validation is conducted on critical connection structures of aircraft, specifically the attachment lug structure, using the active guided wave SHM method. The results demonstrate the effectiveness of the proposed multi-layer ML model evolutionary paradigm.

Deep Learning Methods for Vibration-Based Structural Health Monitoring: A Review

Deep Learning Methods for Vibration-Based Structural Health Monitoring: A Review

Advancing Bridge Structural Health Monitoring: Insights into Knowledge-Driven and Data-Driven Approaches

Structural health monitoring (SHM) is increasingly being used in the field of bridge engineering, and the technology for monitoring bridges has undergone a radical change. It has evolved from the initial local monitoring and assessment, which relied mainly on manual work, to the current all-round and full-time intelligent assessment provided by intelligent monitoring systems. This paper reviews the development of structural health monitoring technology in the civil engineering field and examines two current AI methods in bridge structural health monitoring, namely knowledge-driven and data-driven approaches. The advantages and disadvantages of these two AI methods are analyzed, and future development trends are also discussed. The overview results reveal that knowledge-driven methods have the advantages of interpretability and stability. However, their current application is limited, and significant technical bottlenecks remain. On the other hand, the data-driven approach demonstrates higher efficiency and accuracy. Nevertheless, it is characterized by instability and insecurity due to its "black-box" nature, which hinders its ability to explain the internal operation mechanism. Given these findings, the hybrid knowledge-data-driven approach emerges as a potential solution. This approach can effectively integrate the advantages of both knowledge-driven and data-driven methods while avoiding their respective disadvantages. Consequently, the hybrid approach proves to be more stable, safe, and efficient in practical applications.

Debonding Detection in Grouted Sleeves Using Axisymmetric Longitudinal Guided Waves.

Grouted sleeves (GSs) are a type of precast joint that can effectively connect steel rebars with excellent performance. However, the grouting debonding problem, which can occur due to the leakage of the glue plug, can seriously affect the properties of GSs. In this paper, a guided-wave-based structural health monitoring (SHM) method is used to detect debonding in GSs. The axisymmetric longitudinal mode is selected as the incident wave since it is sensitive to axial damage. Eight piezoelectrics (PZTs) are then symmetrically installed to actuate signals. The proposed samples are GSs with four different debonding sizes. First, the relationship between the arrival time of the first wave packet and the debonding size is explored through theoretical derivation. The arrival time decreases linearly with an increasing debonding size. A similar trend is observed when the relationship is verified via a numerical simulation and experimental results. This method will provide a reference for detecting debonding in similar GS multilayer structures.

Hierarchical verification and validation in a forward model-driven structural health monitoring strategy

This paper presents a demonstrative application of a forward model-driven approach to structural health monitoring (SHM), incorporating hierarchical validation methods. A key tenet of the approach is that an SHM system can be constructed that is capable of diagnosing damage at the full system level, without full system damage-state data having been used in its development; achieving this would be highly impactful as the system-level damage state data is generally not feasible to acquire (previous SHM methods such as data-driven SHM have been hindered by their dependence on these data). This is achieved by carrying out validation activities on the damage model at the subassembly level of the structure. The particular focus of the present paper is on damage detection and assessment, although the approach offers a natural basis for extension to other damage identification activities such as damage location and prognosis. The present study focuses on two of the key elements of the model-driven approach: validation of the predictive substructure models and their application in the assembled model. The ideas discussed are demonstrated in a case study based on a laboratory-scale truss bridge structure.

Research on structural health monitoring methods for bridge structures

With the development of science and technology, China is building more and more Bridges. The inspection and maintenance of bridge during operation plays an important role in the safety of bridge. Firstly, the research background and significance of bridge structure health inspection are introduced, and the research status and progress of health inspection are analyzed. Then, several commonly used monitoring methods are listed. Based on the comprehensive study of the bridge health monitoring system, the main conclusions are as follows: the bridge health monitoring system is realized through the coordination of several subsystems, including: bridge working environment monitoring, bridge structure overall performance monitoring and bridge structure local performance monitoring. The bridge structural health monitoring system is mainly used in bridge damage identification and bridge safety warning. The bridge structure health monitoring system conforms to the development trend of high technology and will become an indispensable part of the bridge operation in the future.

An enhanced topological analysis for Lamb waves based SHM methods

Topological data analysis (TDA) is a powerful and promising tool for data analysis, but yet not exploited enough. It is a multidimensional method which can extract the topological features contained in a given dataset. An original TDA-based method allowing to monitor the health of structures when equipped with piezoelectric transducers (PZTs) is introduced here. Using a Lamb wave based Structural Health Monitoring (SHM) approach, it is shown that with specific pre-processing of the measured time-series data, the TDA (persistent homology) for damage detection and classification can be greatly improved. The TDA tool is first applied directly in a traditional manner in order to use homology classes to assess damage. After that, another method based on slicing the temporal data is developed to improve the persistence homology perception and to leverage topological descriptors to discriminate different damages. The dataset used to apply both methods comes from experimental campaigns performed on aeronautical composite plates with embedded PZTs where different damage types have been investigated such as delamination, impacts and stiffness reduction. The proposed approach enables to consider a priori physical information and provides a better way to classify damages than the traditional TDA approach. In summary, this article demonstrates that manipulating the topological the features of PZTs time-series signals using TDA provides an efficient mean to separate and classify the damage natures and opens the way for further developments on the use of TDA in SHM.

Baseline-Free Detection Method for Change of Lateral Stiffness of High-Rise Building Based on Statistical Moment Curvature

In recent times, there has been a notable increase in the quantity of high-rise buildings, attributed to the swift advancements in both economic growth and construction technology. Assessing the structural integrity of high-rise buildings is important to ensure their safe operation. However, existing structural health monitoring methods typically require a baseline, involving either the measured dynamic and static responses from an intact structure or the finite element (FE) model corresponding to an undamaged state. These prerequisites are often challenging to acquire in practical scenarios. This study introduces a novel baseline-free method for detecting reduction in the lateral stiffness of high-rise buildings. The method is based on the statistical moment curvature (SMC) concept, determined through applying central difference to the second-order statistical moment of displacement. Initially, theoretical formulas were derived to demonstrate the viability of utilizing SMC for identifying reduction in the lateral stiffness of high-rise buildings. Subsequently, a FE model of a representative high-rise building was constructed to validate the proposed approach and assess its sensitivity, where different structural types and noise levels were considered. Lastly, a field test was conducted on a 33-story shear wall structure to provide additional validation for the proposed method. The results confirmed its effectiveness in accurately detecting reduction in the lateral stiffness of high-rise building. It offers two primary benefits: firstly, it obviates the need for a baseline, rendering it more convenient and applicable in real-world scenarios; secondly, its heightened sensitivity to sudden drops in lateral stiffness allows for early-stage detection of structural damage.

A method to maximise the information obtained from low signal-to-noise acceleration data by optimising SSI-COV input parameters

Structural Health Monitoring (SHM) has mainly been undertaken on larger bridges and on a case-by-case basis. This is due to a range of factors, such as the high installation costs and the effort required to install and commission the monitoring systems. One way in which SHM systems can become more feasible for widespread adoption at a network level is to reduce the number and cost of sensors used. However, this comes with a trade-off as low-cost sensors will typically have a worse signal-to-noise ratio and the reduced number of sensors requires careful placement to maximise the amount of information acquired. One of the simplest/cheapest methods of bridge SHM is long-term tracking of the bridge frequency to identify a change in stiffness. Using data collected from five in-service bridges, this research shows that the user-defined SSI-COV input parameters can significantly impact the quality of extracted natural frequencies. Consequently, a novel method is developed to aid in choosing the inputs used in the SSI-COV method. The method developed also showed that the determined inputs resulted in the extraction of accurate natural frequencies with minimal apparent outliers on all tested bridges. The developed method allows the extraction of quality natural frequencies from low signal-to-noise acceleration data which are vital when undertaking frequency-based SHM.