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Multiple-input, multiple-output modal testing of a Hawk T1A aircraft: a new full-scale dataset for structural health monitoring
Structural Health Monitoring
Abstract
The use of measured vibration data from structures has a long history of enabling the development of methods for inference and monitoring. In particular, applications based on system identification and structural health monitoring have risen to prominence over recent decades and promise significant benefits when implemented in practice. However, significant challenges remain in the development of these methods. The introduction of realistic, full-scale datasets will be an important contribution to overcoming these challenges. This article presents a new benchmark dataset capturing the dynamic response of a decommissioned BAE Systems Hawk T1A. The dataset reflects the behaviour of a complex structure with a history of service that can still be tested in controlled laboratory conditions, using a variety of known loading and damage simulation conditions. As such, it provides a key stepping stone between simple laboratory test structures and in-service structures. In this article, the Hawk structure is described in detail, alongside a comprehensive summary of the experimental work undertaken. Following this, key descriptive highlights of the dataset are presented, before a discussion of the research challenges that the data present. Using the dataset, non-linearity in the structure is demonstrated, as well as the sensitivity of the structure to damage of different types. The dataset is highly applicable to many academic enquiries and additional analysis techniques which will enable further advancement of vibration-based engineering techniques.
Structural health monitoring (SHM) is crucial for ensuring the safety and longevity of military training aircraft, which face demanding conditions such as high maneuverability, variable loads, and extreme environments, leading to structural fatigue. Traditional methods, such as modal analysis, often struggle to handle the multivariate complexity of operational conditions and data variability. Recently, deep learning has emerged as a promising alternative to overcome these limitations. However, deep learning models typically operate in a unidirectional manner, where feedback to the inputs is often neglected. In contrast, biological neurons utilize feedback mechanisms to refine and adapt their responses in natural ecosystems, enabling adaptive learning and error correction. In this context, this study proposes an innovative Convolutional Neural Network with Reversed Mapping (CNN-RM) approach to SHM, which incorporates feedback loops and self-correcting mechanisms. Before feeding the data into CNN-RM, the dataset complexity is reduced through time-series-to-images Continuous Wavelet Transform (CWT), followed by a denoising CNN (DnCNN) to mitigate complex behavior under various conditions. For application, this study utilizes a massive dataset collected from multivariate sensors installed on a decommissioned military training aircraft previously used by the British Royal Air Force and now housed in a laboratory environment. The results revealed that the overall mean of classification metrics for the CNN is 0.9673 (training) and 0.9422 (testing), while for CNN-MR, it is 0.9764 (training) and 0.9515 (testing), showing an improvement of 0.94% in training and 1.00% in testing. These results highlight significant advancements in SHM, recommending the consideration of such learning mechanisms in neural learning models.
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.
Vibration-based Structural Health Monitoring is an ongoing field of research in many engineering disciplines. As for civil engineering, plenty of experimental structures have been erected in the past decades, both under laboratory and real-life conditions. Some of these facilities became a benchmark for different kinds of methods associated with Structural Health Monitoring such as damage analysis and Operational Modal Analysis, which led to fruitful developments in the global research community. When it comes to the continuous monitoring and assessment of the structural integrity of mechanical systems exposed to environmental and operational variability, the robustness and adaptability of the applied methods is of utmost importance. Such properties cannot be fully evaluated under laboratory conditions, which highlights the necessity of outdoor measurement campaigns. To this end, we introduce a test facility for Structural Health Monitoring comprising a lattice tower exposed to realistic conditions and featuring multiple reversible damage mechanisms. The structure located near Hanover in Northern Germany is densely equipped with sensors to capture the structural dynamics. The environmental conditions are monitored in parallel. The obtained continuous measurement data can be accessed online in an open repository. That is the foundation for benchmarks, consisting of a growing data set that enables the development, evaluation, and comparison of Structural Health Monitoring strategies and methods. In this article, we offer a documentation of the test facility and the data acquisition system. Lastly, we characterize the structural dynamics with the help of a finite element model and by analyzing several month of data.
The published dataset comprises long-term structural measurements of a lattice tower to support and facilitate the research in the field of Structural Health Monitoring. The structure is located near Hanover in Northern Germany and is equipped with reversible damage mechanisms on multiple positions, which have been repeatedly activated and reactivated during the measurement period from August, 2020 to Juli, 2021.
Scientists and engineers want accurate mathematical models of physical systems for understanding, design, and control. To obtain accurate models, persistently exciting rich signals are needed. The MUMI Matlab toolbox creates multisine signals to assess the underlying systems in a time efficient, user-friendly way. In order to avoid any spectral leakage, to reach full nonparametric characterization of the noise, and to be able to detect nonlinearities and time-variations, multisine signals can be used. By the use of the toolbox, inexperienced users can easily create state-of-the-art excitation signals to be used to perturbate almost any physical systems.
This paper constitutes the numerical companion of the experimental work on vibration‐based monitoring of a small‐scale wind turbine (WT) blade. In this second part, a numerical benchmark is established for condition assessment of a Windspot 3.5‐kW WT blade. The aim is to supplement the companion experimental work with a physical model exposed to diverse operational conditions, loading scenarios, and damage patterns that are not easily explorable and controllable in the laboratory. To this end, a finite element (FE) model of the considered blade is developed and subjected to a number of artificial damage scenarios, which are dynamically tested under both environmental and operational variability. The paper offers a detailed description of the numerical benchmark and the underlying assumptions, as well as the spectrum of operational conditions, the measured quantities, and the wind load model. Finally, we provide an overview and demonstration of the stand‐alone application for time history analysis and generation of synthetic vibration data, which is made available via an open‐access code in Sonkyo‐Benchmark repository.
Structural health monitoring (SHM) has been increasingly exploited in recent years as a valuable tool for assessing performance throughout the life cycle of structural systems, as well as for supporting decision-making and maintenance planning. Although a great assortment of SHM methods has been developed, only a limited number of studies exist serving as reference basis for the comparison of different techniques. In this paper, the vibration-based assessment of a small-scale wind turbine (WT) blade is experimentally investigated, with the aim of establishing a benchmark case study for the SHM community. The structure under consideration, provided by Sonkyo Energy as part of the Windspot 3.5 kW WT model, is tested in both healthy and damaged states under varying environmental, that is, temperature, conditions as imposed by means of a climatic chamber. This study offers a thorough documentation of the configuration of this experimental benchmark, including the types of deployed sensors, the nature of excitation and available measurements, and the investigated damage scenarios and environmental variations enforced. Lastly, an overview of the raw and processed measurement data, made available to researchers via an open access Zenodo (https://zenodo.org/record/3229743#.X6p6AnVKiwl) repository, is herein provided.
This is the third and final paper in a series laying foundations for a theory/methodology of Population-Based Structural Health Monitoring (PBSHM). PBSHM involves utilising knowledge from one set of structures in a population and applying it to a different set, such that predictions about the health states of each member in the population can be performed and improved. Central ideas behind PBSHM are those of knowledge transfer and mapping. In the context of PBSHM, knowledge transfer involves using information from a source domain structure, where labels are known for given feature sets, and mapping these onto the unlabelled feature space of a different, target domain structure. This mapping means a classifier trained on the transformed source domain data will generalise to the unlabelled target domain data; i.e. a classifier built on one structure will generalise to another, making Structural Heath Monitoring (SHM) cost-effective and applicable to a wide range of challenging industrial scenarios. This process of mapping features and labels across source and target domains is defined here via domain adaptation, a subcategory of transfer learning. A mathematical underpinning for when domain adaptation is possible in a structural dynamics context is provided, with reference to topology within a graphical representation of structures. Subsequently, a novel procedure for performing domain adaptation on topologically different structures is outlined.
This paper presents a record of the participation of the authors in a workshop on nonlinear system identification held in 2016. It provides a summary of a keynote lecture by one of the authors and also gives an account of how the authors developed identification strategies and methods for a number of benchmark nonlinear systems presented as challenges, before and during the workshop. It is argued here that more general frameworks are now emerging for nonlinear system identification, which are capable of addressing substantial ranges of problems. One of these frameworks is based on evolutionary optimisation (EO); it is a framework developed by the authors in previous papers and extended here. As one might expect from the ‘no-free-lunch’ theorem for optimisation, the methodology is not particularly sensitive to the particular (EO) algorithm used, and a number of different variants are presented in this paper, some used for the first time in system identification problems, which show equal capability. In fact, the EO approach advocated in this paper succeeded in finding the best solutions to two of the three benchmark problems which motivated the workshop. The paper provides considerable discussion on the approaches used and makes a number of suggestions regarding best practice; one of the major new opportunities identified here concerns the application of grey-box models which combine the insight of any prior physical-law based models (white box) with the power of machine learners with universal approximation properties (black box).
Nonlinearities in practical systems can arise in contacts between components, possibly from friction or impacts. However, it is also known that quadratic and cubic nonlinearity can occur in the stiffness of structural elements undergoing large amplitude vibration, without the need for local contacts. Nonlinearity due purely to large amplitude vibration can then result in significant energy being found in frequency bands other than those being driven by external forces. To analyse this phenomenon, a method is developed here in which the response of the structure in the frequency domain is divided into frequency bands, and the energy flow between the frequency bands is calculated. The frequency bands are assigned an energy variable to describe the mean response and the nonlinear coupling between bands is described in terms of weighted summations of the convolutions of linear modal transfer functions. This represents a nonlinear extension to an established linear theory known as statistical energy analysis (SEA). The nonlinear extension to SEA theory is presented for the case of a plate structure with quadratic and cubic nonlinearity.
© 2015 The Author(s).
In the data-based approach to structural health monitoring (SHM), the absence of data from damaged structures in many cases forces a dependence on novelty detection as a means of diagnosis. Unfortunately, this means that benign variations in the operating or environmental conditions of the structure must be handled very carefully, lest they lead to false alarms. If novelty detection is implemented in terms of outlier detection, the outliers may arise in the data as the result of both benign and malign causes and it is important to understand their sources. Comparatively recent developments in the field of robust regression have the potential to provide ways of exploring and visualising SHM data as a means of shedding light on the different origins of outliers. The current paper will illustrate the use of robust regression for SHM data analysis through experimental data acquired from the Z24 and Tamar Bridges, although the methods are general and not restricted to SHM or civil infrastructure.
Before structural health monitoring (SHM) technologies can be reliably implemented on structures outside laboratory conditions the problem of environmental variability in monitored features must be first addressed. Structures that are subjected to changin environmental or operational conditions will often exhibit inherently non-stationary dynamic and quasi-static responses, whic can mask any changes caused by the occurrence of damage. The current work introduces the concept of cointegration, a tool for the analysis of non-stationary time series, as a promising new approach for dealing with the problem of environmenta variation in monitored features. If two or more monitored variables from an SHM system are cointegrated, then some linea combination of them will be a stationary residual purged of the common trends in the original dataset. The stationary residua created from the cointegration procedure can be used as a damage-sensitive feature that is independent of the normal environmenta and operational conditions.
This paper describes a coherent strategy for intelligent fault detection. All of the features of the strategy are discussed in detail. These encompass: (i) a taxonomy for the relevant concepts, i.e. a precise definition of what constitutes a fault etc., (ii) a specification for operational evaluation which makes use of a hierarchical damage identification scheme, (iii) an approach to sensor prescription and optimisation and (iv) a data processing methodology based on a data fusion model.
Based on the extensive literature that has developed on structural health monitoring over the last 20 years, it can be argue that this field has matured to the point where several fundamental axioms, or gen eral principles, have emerged. The intentio of this paper is to explicitly state and justify these axioms. In so doing, it is hoped that two subsequent goals are facilitated.
First, the statement of such axioms will give new researchers in the field a starting point that alleviates the need to revie the vast amounts of literature in this field. Second, the authors hope to stimulate discussion and thought within the communit regarding these axioms.
A new framework for modeling the dynamics of bolted structures is proposed that considers the nonlinear interfacial modeling of bolted structures using multi-scale traction-based contact constitutive laws implemented through Zero-Thickness Elements (ZTE). Using rough contact theory, it is possible to establish fundamental constitutive relationships with parameters estimated from micro-scale surface scans. Such a model is employed in the framework for a three bolt lap-joint benchmark (the so-called ''Brake-Reuß-Beam"). Since the characterization of the interface is conducted in a full-field manner on top of a finite element mesh, the framework is also demonstrated to be applicable for conducting full-field micro-scale interface evolution studies. Preliminary studies are conducted to establish correlations of local changes in relevant roughness parameters with predicted local tractions and dissipation fluxes.
In the real-world, structures are subjected to operational and environmental condition changes that impose difficulties in detecting structural damage. The aim of this paper is to summarize a number of damage detection studies that have been applied to standard data sets. These data sets include the effects of simulated operational and environmental variability and their analysis has been addressed through the application of statistical pattern recognition paradigms for structural health monitoring (SHM). The test structure considered here is a laboratory base-excited three-story structure. Damage is simulated through nonlinear effects produced by a bumper mechanism that introduces an impact-type nonlinearity. The operational and environmental changes are simulated by adding mass and reducing the stiffness at several locations on the structure. This study shows how these data sets can be used to compare different feature extraction and statistical modeling techniques in the SHM process. The relative strengths and limitations of some actual SHM pattern recognition techniques were assessed and quantified. The full descriptions of the test structure, measured data sets, and signal processing algorithms are available for download at: http://institute.lanl.gov/ei.
Understanding the rheological properties of fresh concrete, the hydration phenomenon of cement responsible for structuration, the relationship between the characteristics of the porous solid obtained and its mechanical performances or resistance to the aggressive penetration requires a complex knowledge of the physicochemistry of reactive porous materials. The development of simple formulation rules therefore requires the assimilation of this knowledge and a good command of the properties of these materials. The purpose of this book is to provide the mix designer with useful knowledge on granular materials and porous materials, which will enable the innovative design of concrete. Topics covered include the characterization of granular materials, the concepts of porosity and specific surface area, and the transport properties (diffusion and permeation) of concrete. Some of these topics are already covered in other general books dedicated to granular or porous materials. The objective here is to bring them together in one book by adapting them for use by concrete specialists. Applications in the form of exercises are offered at the end of each chapter to enable readers to assimilate the theoretical knowledge and to apply such knowledge to concrete problems encountered in civil engineering.
Bolted joints are able to provide the majority of damping for an assembled structure when used to connect components. The dynamic frictional contact analysis of a bolted joint under harmonic loading is investigated by the finite element method. Then the detailed finite element model is replaced by a number of Jenkins elements and the Bouc–Wen model by fitting the hysteresis loops produced by the finite element method. These two latter models lead to much reduced computing load. The advantages and drawbacks of these two simplified models are discussed and this understanding is useful in choosing the right simplified model for a bolted joint.
Vibration monitoring is a useful evaluation tool in the development of a non-destructive damage-identification technique, and relies on the fact that occurrence of damage in a structural system leads to changes in its dynamic properties. It can give global information of a structure, and the location of the damage has not to be known in advance. Damage-assessment techniques are validated on the progressively damaged prestressed concrete bridge Z24 in Switzerland, tested in the framework of the Brite Euram project SIMCES. A series of full modal surveys are carried out on the bridge before and after applying a number of damage scenarios.
The real-world structures are subjected to operational and environmental
condition changes that impose difficulties in detecting and identifying structural
damage. The aim of this report is to detect damage with the presence of such
operational and environmental condition changes through the application of the Los
Alamos National Laboratory’s statistical pattern recognition paradigm for structural
health monitoring (SHM). The test structure is a laboratory three-story building,
and the damage is simulated through nonlinear effects introduced by a bumper
mechanism that simulates a repetitive impact-type nonlinearity. The report reviews
and illustrates various statistical principles that have had wide application in many
engineering fields. The intent is to provide the reader with an introduction to feature
extraction and statistical modelling for feature classification in the context of SHM.
In this process, the strengths and limitations of some actual statistical techniques
used to detect damage in the structures are discussed. In the hierarchical structure of
damage detection, this report is only concerned with the first step of the damage
detection strategy, which is the evaluation of the existence of damage in the
structure. The data from this study and a detailed description of the test structure are
available for download at: http://institute.lanl.gov/ei/software-and-data/.
The nonlinear transfer behaviour of an assembled structure such as a large lightweight space structure is caused by the nonlinear influence of structural connections. Bolted or riveted joints are the primary source of damping compared to material damping, if no special damping treatment is added to the structure. Simulation of this damping amount is very important in the design phase of a structure. Several well known lumped parameter joint models used in the past to describe the dynamic transfer behaviour of isolated joints by Coulomb friction elements are capable of describing global states of slip and stick only.
The present paper investigates the influence of joints by a mixed experimental and numerical strategy. A detailed Finite Element model is established to provide understanding of different slip-stick mechanisms in the contact area. An advanced lumped parameter model is developed and identified by experimental investigations for an isolated bolted joint. This model is implemented in a Finite Element program for calculating the dynamic response of assembled structures incorporating the influence of micro- and macroslip of several bolted joints.
A general method is presented for predicting the ensemble average steady-state response of complex vibro-acoustic systems that contain subsystems with uncertain, or random, properties. The method combines deterministic and statistical techniques to produce a non-iterative hybrid method that incorporates equations of dynamic equilibrium and power balance. The method is derived explicitly without reference to statistical energy analysis (SEA); however, it is seen that the wave approach to SEA can be viewed as a special case of the proposed method. The proposed method provides a flexible way to account for necessary deterministic details in a vibro-acoustic analysis without requiring that an entire system be modeled deterministically. The method therefore provides a potential solution to the mid-frequency problem (in which a system is neither entirely deterministic nor entirely statistical). The application of the method is illustrated with a numerical validation example.
The purpose of this publication is to motivate the need of validating numerical models based on time-domain data for non-linear, transient, structural dynamics and to discuss some of the challenges faced by this technology. Our approach is two-fold. First, several numerical and experimental testbeds are presented that span a wide variety of applications (from non-linear vibrations to shock response) and difficulty [from a single-degree-of-freedom (sdof) system with localised non-linearity to a three-dimensional (3-D), multiple-component assembly featuring non-linear material response and contact mechanics]. These testbeds have been developed at Los Alamos National Laboratory in support of the Advanced Strategic Computing Initiative and our code validation and verification program. Conventional, modal-based updating techniques are shown to produce erroneous models although the discrepancy between test and analysis modal responses can be bridged. This conclusion offers a clear justification that metrics based on modal parameters are not well suited to the resolution of inverse, non-linear problems. In the second part of this work, the state-of-the-art in the area of model updating for non-linear, transient dynamics is reviewed. The techniques identified as the most promising are assessed using data from our numerical or experimental testbeds. Several difficulties of formulating and solving inverse problems for non-linear structural dynamics are identified. Among them, we cite the formulation of adequate metrics based on time series and the need to propagate variability throughout the optimisation of the model's parameters. Another important issue is the necessity to satisfy continuity of the response when several finite element optimisations are successively carried out. An illustration of how this problem can be resolved based on the theory of optimal control is provided using numerical data from a non-linear Duffing oscillator. The publication concludes with a brief description of current research directions in inverse problem solving for structural dynamics.
The main problem associated with pattern recognition based approaches to Structural Health Monitoring (SHM) is that damage localisation and quantification almost always require supervised learning. In the case of high-value engineering structures like aircraft, it is simply not possible to generate the training data associated with damage by experiment. It is also unlikely that data can always be generated by simulation as the models required would often need to be of such high fidelity that the costs of development and the run-times would again be prohibitive. The object of this paper is to explore the potential of a simple experimental strategy, which involves adding masses to the structure, in the attempt to extract features for novelty detection. The idea itself is not presented as revolutionary based on the fact that adding masses has been considered as a case of damage before, however, an in-depth investigation of its suitability for guiding feature selection is presented here. The approach is illustrated first on a simple structure by using data generated from a finite-element (FE) simulation and then validated experimentally on a more complicated laboratory structure. Simulated damage, in the form of a loss in the stiffness in the case of the numerical model and of a saw-cut in the case of the structure is used for comparison. The results show similar patterns in both cases which suggests a potential use of the method for higher level damage detection.
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