Structural Health Monitoring Methodology Research Articles

Multi-objective sensor placement optimization of helicopter rotor blade based on Feature Selection

This work aims to develop a Structural Health Monitoring methodology that maximizes the acquired modal response and minimizes the number of sensors in a helicopter’s main rotor blade. Although this trade-off is a SHM principle, there is no methodology in literature that opposes these objectives for any structure. Firstly, a real AS350 helicopter rotor blade was experimentally tested and a numerical model was elaborated in FEM. An inverse method found the mechanical properties that fit numerical and experimental models. Then, a new methodology is proposed to address the Sensor Placement Optimization problem using the Multi-objective Lichtenberg Algorithm and Feature Selection. The Multi-objective Sensor Selection and Placement Optimization based on the Lichtenberg Algorithm (MOSSPOLA) has as one of the objectives the number of sensors and the other, one of the 7 best-known metrics in SPO: Kinetic Energy, Effective Independence, Average Driving-Point Residue, Eigenvalue Vector Product, Information Entropy, Fisher Information Matrix, and Modal Assurance Criterion. Pareto fronts and sensor configurations were generated and compared. Linear and convex families of Pareto fronts were unprecedentedly identified, showing a correlation between them. Better sensor distributions were associated with higher Hypervolume and the best metrics for each family were applied to damage identification for final comparison. The MOSSPOLA found a sensor configuration for each sensor number and metric, including one with 100% accuracy in identifying delamination considering triaxial modal displacements, minimum number of sensors, and noise for all blade sections.

Toward a Structural Health Monitoring Methodology for Concrete Structures under Dynamic Loads Using Embedded FBG Sensors and Strain Mapping Techniques.

A data-driven-based methodology for SHM in reinforced concrete structures using embedded fiber optic sensors and pattern recognition techniques is presented. A prototype of a reinforced concrete structure was built and instrumented in a novel fashion with FBGs bonded directly to the reinforcing steel bars, which, in turn, were embedded into the concrete structure. The structure was dynamically loaded using a shaker. Superficial positive damages were induced using bonded thin steel plates. Data for pristine and damaged states were acquired. Classifiers based on Mahalanobis’ distance of the covariance data matrix were developed for both supervised and unsupervised pattern recognition with an accuracy of up to 98%. It was demonstrated that the proposed sensing scheme in conjunction with the developed supervised and unsupervised pattern recognition techniques allows the detection of slight stiffness changes promoted by damages, even when strains are very small and the changes of these associated with the damage occurrence may seem negligible.

Detecting Damage Evolution of Masonry Structures through Computer-Vision-Based Monitoring Methods

Detecting the onset of structural damage and its progressive evolution is crucial for the assessment and maintenance of the built environment. This paper describes the application of a computer-vision-based methodology for structural health monitoring to a shake table investigation. Three rubble stone masonry walls, one unreinforced and two reinforced, were tested under natural earthquake base inputs, progressively scaled up to collapse. White noise signals were also applied for dynamic identification purposes. Throughout the experiments, videos were recorded, under both white noise excitation and environmental vibrations, with the table at rest. The videos were preprocessed with motion magnification algorithms and analyzed through a principal component analysis. The natural frequencies of the walls were detected and their progressive decay was associated with damage accumulation. Results agreed with those obtained from another measurement system available in the laboratory and were consistent with the crack pattern development surveyed during the tests. The proposed approach proved useful to derive information on the progressive deterioration of the structural properties, showing the feasibility of this methodology for real field applications.

Shape monitoring and damage identification in stiffened plates using inverse finite element method and Bayesian learning

Stiffened plate structures are common structural forms that are widely used in various fields. Structural Health Monitoring (SHM) is an important tool to maintain their safe operation. The inverse finite element method (iFEM) is a state-of-the-art methodology for SHM that can precisely reconstruct full-field displacements through limited strain sensors. In this paper, we study the stiffened cantilever plate and propose a complete damage localization and quantification system based on iFEM. By solving the damage location index, we can recognize the damage locations in real time, independent of loads and load combinations. Moreover, the strain modes at strain sensors are obtained by modal tests, which can be transformed into full degrees of freedom (DOFs) mode shapes by the iFEM. The damage severities can be further determined using the Bayesian learning (BL) algorithm. In this way, the mode shapes required for damage identification are transformed into strain modes which are easier to obtain, especially for structures with rotational DOFs such as plates and shells. Considering different damage scenarios, the proposed method can detect true locations and severities of damage even with a limited number of strain sensors and under measurement noise.

A population-based SHM methodology for heterogeneous structures: Transferring damage localisation knowledge between different aircraft wings

Population-based structural health monitoring (PBSHM) offers a new viewpoint for structural health monitoring (SHM), allowing diagnostic information to be shared across populations of structures. By extending the set of available damage observations, a population-based approach can diagnose damage previously unseen on a structure of interest by leveraging damage information from other structures in the population. These technologies therefore provide significant benefits for making SHM practicable in a variety of industrial settings. It is proposed that PBSHM methodologies must be comprised of tools to assess similarity coupled with algorithms that perform knowledge transfer. The similarity tools are important for identifying whether an SHM task should be attempted for a given population by assessing both the structural similarities between members of a population and similarities in their data spaces. An abstract representation of structures in a graphical domain is presented as an objective way of assessing structural similarity, with distance metrics utilised for assessing data-space similarities. Knowledge transfer is performed using a branch of transfer learning called domain adaptation. By determining if members of a population are similar in a structural and data-space sense, the risk of negative transfer can be reduced; whereby domain adaptation reduces classification performance. This paper demonstrates a PBSHM methodology for transferring knowledge within a heterogeneous population (a group of non-identical structures). Specifically, the PBSHM methodology is shown to transfer localisation labels from a Gnat aircraft wing to an unlabelled Piper Tomahawk aircraft wing dataset, resulting in 100% classification accuracy.

Use of Remote Structural Tap Testing Devices Deployed via Ground Vehicle for Health Monitoring of Transportation Infrastructure

Transportation infrastructure is an integral part of the world’s overall functionality; however, current transportation infrastructure has aged since it was first developed and implemented. Consequently, given its condition, preservation has become a main priority for transportation agencies. Billions of dollars annually are required to maintain the United States’ transportation system; however, with limited budgets the prioritization of maintenance and repairs is key. Structural Health Monitoring (SHM) methods can efficiently inform the prioritization of preservation efforts. This paper presents an acoustic monitoring SHM method, deemed tap testing, which is used to detect signs of deterioration in structural/mechanical surfaces through nondestructive means. This method is proposed as a tool to assist bridge inspectors, who already utilize a costly form of SHM methodology when conducting inspections in the field. Challenges arise when it comes to this method of testing, especially when SHM device deployment is done by hand, and when the results are based solely upon a given inspector’s abilities. This type of monitoring solution is also, in general, only available to experts, and is associated with special cases that justify their cost. With the creation of a low-cost, cyber–physical system that interrogates and classifies the mechanical health of given surfaces, we lower the cost of SHM, decrease the challenges faced when conducting such tests, and enable communities with a revolutionary solution that is adaptable to their needs. The authors of this paper created and tested a low-cost, interrogating robot that informs users of structural/mechanical defects. This research describes the further development, validation of, and experimentation with, a tap testing device that utilizes remote technology.

Monitoring growing cracks in aircraft lugs by means of the electro-mechanical impedance method

Lugs are common connecting elements in many fields of engineering, e.g., aircraft and automotive. High stress concentrations at the inner edge of the bolt hole lead to early crack initiation and rapid fatigue crack growth under cyclic loading. However, a complete failure of such components is in most cases not acceptable. This contribution presents a model-based methodology to monitor the crack propagation in aircraft lugs by means of the electro-mechanical impedance method. For this purpose, piezoelectric sensors are permanently attached to the monitored structure. The evaluated structural components are necked, straight and tapered lugs. Crack propagation is analyzed numerically by coupled-field finite element models and experimentally using an impedance analyzer. Simulated and measured frequency spectra of pristine (no crack present) and damaged (a crack of various lengths is present) structures show significant deviations in resonance frequencies which are considered as clear indicators of the present crack. Furthermore, the length of artificially introduced cracks is determined by specific resonance frequency shifts for each lug shape. Crack length estimation based on resonance frequency shifts has already been shown in a recent study for necked double shear lugs, which is now extended for straight and tapered lug shapes. Finally, a discussion on the applicability of the presented crack analysis methodology for structural health monitoring of aircraft lugs under fatigue loading is presented.

On ultrasound propagation in composite laminates: advances in numerical simulation

The growing use of composite materials for aerospace applications has resulted in the need for quantitative methods to analyze composite components. Ultrasonic guided waves constitute the physical approach for nondestructive evaluation (NDE) and structural health monitoring (SHM) of solid composite materials, such as carbon fiber reinforced polymer (CFRP) laminates. Ultrasound based NDE methods are commonly used in the aerospace field, but ultrasonic wave behavior can be complicated by the presence of material anisotropy, complex geometries (e.g., highly curved parts, stiffeners, and joints) and complex geometry defects. Common defects occurring in aerospace composites include delaminations, porosity, and microcracking. Computational models of ultrasonic wave propagation in CFRP composites can be extremely valuable in designing practical NDE and SHM hardware, software, and methodologies that accomplish the desired accuracy, reliability, efficiency, and coverage. Physics based simulation tools that model ultrasonic wave propagation can aid in the development of optimized inspection methods and in the interpretation of NDE data. This paper presents a review of numerical methodologies for ultrasound and guided wave simulation in fiber reinforced composite laminates summarizing the relevant works to date, different methods, and their respective applications.

Integrating structural control, health monitoring, and energy harvesting for smart cities

AbstractCities that are adopting innovative and technology‐driven solutions to improve the city's efficiency are consideredsmart cities. With the increased attention on smart cities with self‐driving vehicles, drones, and robots, designing smart infrastructure is only a natural extension. Smart infrastructures aim to self‐diagnose, self‐power, self‐adapt, and self‐heal during normal and extreme operating conditions. Structural vibration control (SVC) and structural health monitoring (SHM) technologies, in particular, are expected to play pivotal roles in the development of modern smart and resilient structures. SVC methodologies intend to provide supplemental damping and reduce the structural dynamic responses during normal and extreme events. SHM methodologies offer valuable information about the structure's condition that is useful for maintenance purposes and rapid damage detection in post‐hazard events. The collapse of the 12‐story Champlain Towers South, a beachfront condominium in the Miami suburb of Surfside, Florida, could have been known in advance with an embedded SHM technology. More recently, the integrated structural control and health monitoring (ISCHM) systems have shown promise in the development of smart cities of the future. The integrated architecture incorporates the control and health monitoring components as complementary technologies and simultaneously takes advantage of both technologies. This article provides a state‐of‐the‐art review of ISCHM ideas and systems. It presents recent significant developments in structural control, SHM, and energy harvesting that are paving the way towards the advent of integrated ISCHM systems, including damage‐tolerant control systems. This article also identifies future promising research areas for designing the next generation of autonomous ISCHM systems for smart cities.

A transfer Bayesian learning methodology for structural health monitoring of monumental structures

A critical aspect in structural health monitoring (SHM) applied to civil engineering structures is the lack of diagnostic labels able to assign a damage class to the measured data. In this context, a semi-supervised learning methodology, designated as transfer Bayesian learning (TBL), is proposed with the main objective of labeling post-processed data in a probabilistic way by selecting a limited number of informative elements. The proposed method allows to define multi-class labels by making use of a surrogate model (SM) of the structure considering specific damage-sensitive mechanical parameters. The methodology is applied in a monumental building, the Consoli Palace, located in Gubbio, central Italy. The structure is instrumented with several sensors in order to measure vibrations, temperature and possible variation of existing cracks’ amplitudes. Several nonlinear pushover analyses are carried out on a calibrated finite element (FE) model to use them in conjunction with Engineering judgment for the definition of the damage-sensitive regions. The SM, consisting of a simplified model that continuously exchanges information with the physical reality observed through the measurement system, is then used as a class classifier by means of a sensitivity damage chart (SDC). Finally, a Bayesian model updating of the damage-dependent parameters allows the probabilistic damage identification.

Purified nonlinear guided waves through a metamaterial filter for inspection of material microstructural changes

Nonlinear guided waves (NGWs) exhibit extraordinary sensitivity to microstructural changes in materials which are often considered as damage precursors in many structural health monitoring (SHM) applications. However, these changes are usually very small at the microstructural level, thus generating weak nonlinear wave components which are prone to or even overwhelmed by other deceptive and non-damage-related nonlinear sources present in a monitoring system. This may jeopardize the practical implementation of the NGW-based SHM methodology. To tackle this problem, this paper proposes a metamaterial filter (meta-filter (MF)), in the form of a patch to be surface-mounted over the structure under inspection. The MF is designed to purify the probing waves during propagation before reaching the inspection area through tactical elimination of non-damage-related nonlinear components from the actuation. Considering different bonding conditions and the lattice symmetry of the MF, two types of bandgaps, corresponding to Bragg scattering and local resonances, alongside their respective band properties are analyzed. Time-domain finite element simulations are carried out to assess the efficacy of the MF in terms of wave purification in the context of SHM, supported by experimental validations. The designed MF is deployed in an aluminum strip to detect the material microstructural changes induced by a thermal ageing treatment. Results demonstrate the designed MF significantly enhances the detection ability of NGW-based SHM system. Both locally resonant and Bragg bandgaps can, in principle, be used for wave purification. While the former requires precise control of the bonding quality of the MF, the latter relies on symmetrical installation of the MF to ensure a reasonably wide wave filtering range. Considering variability factors in the implementation of the MF and better immunity to the nonlinearity of the bonding layers, preference is given to Bragg bandgap for NGW-based SHM applications.

Monitoring and tracking of a suspension railway based on data-driven methods applied to inertial measurements

In this study, the dynamic response of the suspension railway system Skytrain Düsseldorf is exploited in order to localize the train and monitor the infrastructure based on the proposed structural health monitoring methodology. The Skytrain is a driverless monorail train, which operates at the Düsseldorf airport in Germany and connects the railway station with the main terminals. An inertial measurement unit is mounted on the bogie of the Skytrain that continuously acquires in-service data about the vehicle movement. Based on the train–track interaction, the dynamic response will be directly transmitted to the sensor and unique signal features can be extracted. In this context, the dynamic behavior of the train is used to identify turns and stops along the track. Computational intelligence learns from the operational data and can recognize features of the characteristic track profile. In order to localize the vehicle precisely, the proposed methodology applies a k-means clustering algorithm to label the field test data and an artificial neural network to classify the individual track sections. Each prediction is analyzed by an autoencoder in order to detect anomalous vehicle movements, which will be removed from the damage evaluation to avoid false alarms. Additionally, infrastructure monitoring is conducted on a side track of the Skytrain Düsseldorf. The Skytrains test track is modified with a broken bolt connection, which affects the dynamic response of the bypassing train. A damage index is introduced to evaluate the different track conditions and detect the damage along the side track. Furthermore, the results are compared to a one-class support vector machine, which represents a generalizable method for damage detection based on unsupervised anomaly detection. The outcome of the proposed work can be used to optimize the maintenance planning and ensure a high level of reliability and safety.

Application and implementation of the bond graph methodology on the structural damage detection and monitoring of aeroengines

Advancements in structural safety are of prominent importance in various industrial sectors. A significant body of research has been dedicated to proposing theoretical and empirical guidelines for detecting, localizing, and assessing the severity of damage to structures. This research proposes a novel theoretical approach that employs a bond graph methodology to perform structural health assessments of aeroengine structures. In this work, a reduced aeroengine model is proposed. Using the bond graph technique, the governing dynamic equations of motion can be determined. The results obtained from the analysis can then be used to perform a structural health assessment of the aeroengine. The outcomes allow for detection and localization of any damage to the structure, based on the regions identified. In the present study, damage indicator factors have also been determined for specified aeroengine regions, which can be used to localize damage and assess the severity of its propagation in an aeroengine. The proposed theoretical structural health monitoring methodology has notable advantages over other processes proposed in the literature. The present work allows for a reduction in the cost associated with damage detection, localization, and propagation. The proposed theory offers an accurate theoretical benchmark for comparison with experimental data when assessing structural integrity. Moreover, the proposed methodology can be used to develop and implement design modifications based on design objectives, ensuring continuous and safe operation of aeroengine structures.

Structural Health Monitoring Using Machine Learning and Cumulative Absolute Velocity Features

Machine learning (ML)-aided structural health monitoring (SHM) can rapidly evaluate the safety and integrity of the aging infrastructure following an earthquake. The conventional damage features used in ML-based SHM methodologies face the curse of dimensionality. This paper introduces low dimensional, namely, cumulative absolute velocity (CAV)-based features, to enable the use of ML for rapid damage assessment. A computer experiment is performed to identify the appropriate features and the ML algorithm using data from a simulated single-degree-of-freedom system. A comparative analysis of five ML models (logistic regression (LR), ordinal logistic regression (OLR), artificial neural networks with 10 and 100 neurons (ANN10 and ANN100), and support vector machines (SVM)) is performed. Two test sets were used where Set-1 originated from the same distribution as the training set and Set-2 came from a different distribution. The results showed that the combination of the CAV and the relative CAV with respect to the linear response, i.e., RCAV, performed the best among the different feature combinations. Among the ML models, OLR showed good generalization capabilities when compared to SVM and ANN models. Subsequently, OLR is successfully applied to assess the damage of two numerical multi-degree of freedom (MDOF) models and an instrumented building with CAV and RCAV as features. For the MDOF models, the damage state was identified with accuracy ranging from 84% to 97% and the damage location was identified with accuracy ranging from 93% to 97.5%. The features and the OLR models successfully captured the damage information for the instrumented structure as well. The proposed methodology is capable of ensuring rapid decision-making and improving community resiliency.

Polynomial Chaos-Kriging metamodel for quantification of the debonding area in large wind turbine blades

This study aims to investigate the performance of a data-driven methodology for quantifying damage based on the use of a metamodel obtained from the Polynomial Chaos-Kriging method. The investigation seeks to quantify the severity of the damage, described by a specific type of debonding in a wind turbine blade as a function of a damage index. The damage indexes used are computed using a data-driven vibration-based structural health monitoring methodology. The blade’s debonding damage is introduced artificially, and the blade is excited with an electromechanical actuator that introduces a mechanical impulse causing the impact on the blade. The acceleration responses’ vibrations are measured by accelerometers distributed along the trailing and the wind turbine blade. A metamodel is formerly obtained through the Polynomial Chaos-Kriging method based on the damage indexes, trained with the blade’s healthy condition and four damage conditions, and validated with the other two damage conditions. The Polynomial Chaos-Kriging manifests promising results for capturing the proper trend for the severity of the damage as a function of the damage index. This research complements the damage detection analyses previously performed on the same blade.

Formulation of Methodology for Structural Health Analysis and Monitoring of Marine Structures

The structural deterioration of marine structures depends upon various parameters such as their exposure conditions, oceanographic conditions, salinity, ballast water contamination, wind velocity and its direction. Structural health assessment of this marine structures are mainly corresponds to the corrosion of reinforcing steel embedded in the concrete. The present study is carried out to formulate the methodology for structural health analysis and monitoring of marine structures. The inspection reports and NDT test results helps to find the extent of spread of damages and level of deterioration of marine structures. Thus a protocol or handbook will give a clear information about particular type of distress and what should be the remedial measures for that distress. Thus, the most appropriate rehabilitation measures can be suggest.

Damage detection and fatigue life estimation under random loads: A new structural health monitoring methodology in the frequency domain

AbstractThis paper presents a methodology for structural health monitoring of fatigue cracks under stationary random loads. The methodology is composed of two parts: an empirical correlation to monitor the evolution of damage indices and a numerical scheme for fatigue life estimation. The damage indices are based on the variation of the vibrational response of the structure due to crack propagation. Two damage metrics are studied, and the methodology is verified for a cantilever beam subjected to random base excitation. The case study validates the effectiveness of the methodology and shows that both metrics have the potential for damage detection. For the numerical estimation of the fatigue life, the proposed framework uses the probability density function of the stress, which is obtained from frequency‐domain methods, and an equivalent stress approach based on the Walker's equation for fatigue crack growth. Excellent agreement is found between the predicted fatigue life and the experimental values.

Detecting and localizing anomalies on masonry towers from low-cost vibration monitoring

The structural health of masonry towers can be monitored by installing few accelerometers (or seismometers) at the top of the building. This cost-effective setup provides continuous and reliable information on the natural frequencies of the structure and allows to detect the occurrence of structural anomalies; however, to move from anomaly detection to localization with such a simplified distribution of sensors, a calibrated numerical model is needed. The paper summarizes the development of a Structural Health Monitoring (SHM) procedure for the model-based damage assessment in masonry towers using frequency data. The proposed methodology involves the subsequent steps: (i) preliminary analysis including geometric survey and ambient vibration tests; (ii) FE modeling and updating based on the identified modal parameters; (iii) creation of a Damage Location Reference Matrix (DLRM) from numerically simulated damage scenarios; (iv) detection of the onset of damage from the analysis of the continuously collected vibration data, and (v) localization of the anomalies through the comparison between the experimentally identified variations of natural frequencies and the above-defined DLRM matrix. The proposed SHM methodology is exemplified on the ancient Zuccaro tower in Mantua, Italy. Pseudo-experimental monitoring data were generated and employed to assess the reliability of the developed algorithm in identifying the damage location. The results show a promise toward the practical applications of the proposed strategy for the early identification of damage in ancient towers.

Nondestructive Phase Variation-Based Chipless Sensing Methodology for Metal Crack Monitoring

This article presents a new methodology for structural health monitoring (SHM) applications using a passive microwave sensor. This sensor provides sensitivities on metallic structures for nondestructive testing (NDT) and detecting fatigue cracks or damages. In this method, two different microstrip-based designs are mounted on metal, spiral, and comb sensing structures. Both sensing structures are interrogated by a 2.45 GHz signal in CST Microwave Studio, and their sensitivities for crack detection are compared through the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S_{11}$ </tex-math></inline-formula> scattering parameter. We demonstrate that measuring the reflected signal’s phase parameter from a sensor on a damaged metal provides information from the surface crack by comparing it to the same metal without any crack. The vision is to provide a new chipless, low-cost sensor with increased detection reliability and durability in harsh environments. Simulation results of the comb sensing (CS) structure show that the signal phase shift caused by a crack envisions the possibility of submillimeter-width crack detection through smart structures.

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.