Multivariate statistical analysis for early damage detection

Since stringers are often applied in engineering constructions to improve thin-walled structures’ strength, methods for damage detection at the joints between the stringer and the thin-walled structure are necessary. A 2D mathematical model was employed to simulate Lamb wave excitation and sensing via rectangular piezoelectric-wafer active transducers mounted on the surface of an elastic plate with rectangular surface-bonded obstacles (stiffeners) with interface defects. The results of a 2D simulation using the finite element method and the semi-analytical hybrid approach were validated experimentally using laser Doppler vibrometry for fully bonded and semi-debonded rectangular obstacles. A numerical analysis of fundamental Lamb wave scattering via rectangular stiffeners in different bonding states is presented. Two kinds of interfacial defects between the stiffener and the plate are considered: the partial degradation of the adhesive at the interface and an open crack. Damage indices calculated using the data obtained from a sensor are analyzed numerically. The choice of an input impulse function applied at the piezoelectric actuator is discussed from the perspective of the development of guided-wave-based structural health monitoring techniques for damage detection.

As one of the most common structural forms in port engineering, the operation environment of high-pile wharf is quite harsh and complex, and its pile foundation often produces structural damage of different degrees. Until now, there is a lack of efficient, safe and economic damage detection methods. A novel and precise real-time structural damage detection (SDD) method using both finite element modelling (FEM) and 1D convolutional neural networks (CNNs) is established in this study. The results indicate that the proposed method could accurately identify the presence and location of damage in real time. The results also demonstrated that the proposed 1D CNNs based model are more sensitive to the longitudinal and lateral displacement responses of the high-pile wharf structure.

Functional magnetic resonance imaging (fMRI) is a technique to indirectly measure activity in the brain through the flow of blood. fMRI has been a powerful tool in helping us gain a better understanding of the human brain since it appeared over 20 years ago. However, fMRI poses many challenges for engineers. In particular, to detect and interpret the blood oxygen level-dependent (BOLD) signals on which fMRI is based is a challenge; For example, fMRI activation may be caused by a local neural population (activation detection) or by a distant brain region (effective connectivity). Although many advanced statistical methods have been developed for fMRI data analysis, many problems are still being addressed to maximize the accuracy in activation detection and effective connectivity analysis. This chapter presents general statistical methods for activation detection and effective connectivity analysis in fMRI scans of the human brain. A linear regression model for activation detection is introduced (Sect. 38.2.1), and a detailed statistical inference method for activation detection (Sect. 38.2.2) when applying an autoregression model to correct residual terms of the linear model is presented in Sect. 38.2.3. We adopt a two-stage mixed model to combine different subjects (Sect. 38.3) for second-level data analysis. To estimate the variance for the mixed model, a modified expectation-maximization algorithm is employed. Finally, due to the false positives in the activation map, a Bonferroni-related threshold correction method is developed to control the false positives (Sect. 38.3.3). An fMRI dataset from retinotopic mapping experiments was employed to test the feasibility of the methods for both first- and second-level analysis.

Integration of Information and Communication Technology (ICT) in modern smart grids (SGs) offers many advantages including the use of renewables and an effective way to protect, control and monitor the energy transmission and distribution. To reach an optimal operation of future energy systems, availability, integrity and confidentiality of data should be guaranteed. Research on the cyber-physical security of electrical substations based on IEC 61850 is still at an early stage. In the present work, we first model the network traffic data in electrical substations, then, we present a statistical Anomaly Detection (AD) method to detect Denial of Service (DoS) attacks against the Generic Object Oriented Substation Event (GOOSE) network communication. According to interpretations on the self-similarity and the Long-Range Dependency (LRD) of the data, an Auto-Regressive Fractionally Integrated Moving Average (ARFIMA) model was shown to describe well the GOOSE communication in the substation process network. Based on this ARFIMA-model and in view of cyber-physical security, an effective model-based AD method is developed and analyzed. Two variants of the statistical AD considering statistical hypothesis testing based on the Generalized Likelihood Ratio Test (GLRT) and the cumulative sum (CUSUM) are presented to detect flooding attacks that might affect the availability of the data. Our work presents a novel AD method, with two different variants, tailored to the specific features of the GOOSE traffic in IEC 61850 substations. The statistical AD is capable of detecting anomalies at unknown change times under the realistic assumption of unknown model parameters. The performance of both variants of the AD method is validated and assessed using data collected from a simulation case study. We perform several Monte-Carlo simulations under different noise variances. The detection delay is provided for each detector and it represents the number of discrete time samples after which an anomaly is detected. In fact, our statistical AD method with both variants (CUSUM and GLRT) has around half the false positive rate and a smaller detection delay when compared with two of the closest works found in the literature. Our AD approach based on the GLRT detector has the smallest false positive rate among all considered approaches. Whereas, our AD approach based on the CUSUM test has the lowest false negative rate thus the best detection rate. Depending on the requirements as well as the costs of false alarms or missed anomalies, both variants of our statistical detection method can be used and are further analyzed using composite detection metrics.

Environmental variability is a major challenging issue in bridge health monitoring because bridges are more prone to such variability than other civil structures. To deal with this challenge, this article proposes a new machine-learning method for early damage detection under environmental variability by means of the k-medoids clustering, a new damage indicator, and an innovative approach for selecting a proper cluster number. Estimation of a reliable alarming threshold is another important challenge for early damage detection via most of the machine-learning methods. On this basis, a novel probabilistic approach using the theory of extreme value and a goodness-of-fit measure is proposed to estimate an alarming threshold. The major contributions of this article include proposing a new damage indicator suitable for decision making by clustering-based algorithms, an innovative cluster selection algorithm for dealing with the problem of environmental variability and increasing damage detectability, and a novel probabilistic method for threshold estimation. Modal-based features of the well-known Z24 Bridge are considered to verify the accuracy and effectiveness of the proposed approaches along with several comparative studies. Results show that the methods presented here are highly able to detect early damage even under strong environmental variations and estimate a reliable threshold.

<P>Structural health is a vital aspect of infrastructure sustainability. As a part of a vital infrastructure and transportation network, bridge structures must function safely at all times. However, due to heavier and faster moving vehicular loads and function adjustment, such as Busway accommodation, many bridges are now operating at an overload beyond their design capacity. Additionally, the huge renovation and replacement costs are a difficult burden for infrastructure owners. The structural health monitoring (SHM) systems proposed recently are incorporated with vibration-based damage detection techniques, statistical methods and signal processing techniques and have been regarded as efficient and economical ways to assess bridge condition and foresee probable costly failures. In this chapter, the recent developments in damage detection and condition assessment techniques based on vibration-based damage detection and statistical methods are reviewed. The vibration-based damage detection methods based on changes in natural frequencies, curvature or strain modes, modal strain energy, dynamic flexibility, artificial neural networks, before and after damage, and other signal processing methods such as Wavelet techniques, empirical mode decomposition and Hilbert spectrum methods are discussed in this chapter.   </P>

Large scale tests with progressive damage on a prestressed concrete highway bridge have been performed to investigate the sensitivity of several damage detection, localization, and quantification methods based on modal parameters. To investigate the quality of modal parameters, the data set of one damage step was analyzed by several output-only identification techniques. Although the bridge was severely cracked, natural frequencies as well as mode shapes display only minor changes. However, the relative changes of mode shapes are larger than those observed for natural frequencies. A novel damage indicator, called mode shape area index, based on changes of mode shapes, has been developed and found as the most sensitive damage detection approach. Damage detection or localization via changes of the flexibility matrix performed better than natural frequencies or mode shapes alone. The application of the direct stiffness calculation and a sensitivity-based model update technique showed results having a high level of ambiguity about the location and quantification of damage also at the highest damage level. Evaluating the information collected in this study the test results indicate that an early stage damage identification in prestressed concrete bridges is hardly possible because of the nearly complete recovery of stiffness after closing of cracks in prestressed concrete and the effect of environmental parameters on modal data.

A damage detection method using BP neural network based on a novel damage index, the correlation characteristic of the acceleration response, is proposed, and is evaluated through the FEM simulation and experiment verification. On the basis of achievements in existence, the feasibility of using the correlation characteristic as damage index is validated theoretically. The damage detection for a simple-supported beam using the proposed method was FEM simulated. The results showed that the trained BP neural network can correctly detect the location and extent of damages in both single damage case and multi-damage case. A model test of a reinforced concrete simple-supported beam was performed to verify the validity and efficiency of the damage detection method. From the results of the model test, it is shown that the trained BP neural network can correctly locate the damage mostly detect the extent of damage. A conclusion is given that the novel damage detection method using the correlation characteristic of the acceleration response as damage index is feasible and efficient.

In this paper a vibration-based approach to identify a crack in a wind turbine blade is described and demonstrated numerically and experimentally. Operational modal analysis has been performed before and after a buckling test, and vibration data, gathered from some accelerometers placed along the blade, was used to monitor the integrity of the structure, since the modal parameters are directly influenced by the physical properties of the structures. Additionally a numerical prediction has been done both with a full-scale model and with a one-dimensional model. The results show that this approach is able to estimate successfully the presence of damage and a good numerical and experimental correlation has been found. Finally, some considerations regarding the rotation of the blade in the undamaged and damaged cases have been done.

Filament wound pressure vessels have been extensively used in industry and engineering. The existing damage detection and health monitoring methods for these vessels, such as X-ray and ultrasonic scan, can not meet the requirement of online damage detection; moreover optical grating fibre can only sense the local damage, but not the damage far away from the location of sensors. Vibration-based damage detection methods have the potential to meet such requirements. There methods are based on the fact that damages in a structure results in a change in structural dynamic characteristics. A damage detection method based on a residual associated with output-only subspace-based modal identification and global or focused chi^2-tests built on that residual has been proposed and successfully experimented on a variety of test cases. The purpose of this work is to describe the damage detection method and apply this method to assess the composite structure filled with fluid. The results of identification and damage detection will be presented.

Proportional flexibility-based damage detection for buildings in unknown mass scenarios: The case of severely truncated modal spaces

In principal, a proper analysis of the dynamic response of a structure can provide general indicators of its operational conditions. When the dynamic response changes due to a variation of the physical properties of a structure, then one may conclude that some kind of damages has occurred. This paper presents investigation of the robustness and comparison of four simple methodologies to both identify and quantify the damages in structures, based on the use of Frequency Response Functions (FRF) signals, Principal Component Analysis technique (PCA) and Transmissibility. A steel beam with constant rectangular cross-section is used to compare the proposed approaches. At first, nine damaged scenarios are created and for each of them numerical examples are discussed; a database of FRFs is measured using modal testing. Then, PCA theory is applied to the FRF matrix and global damage detection and quantification indices are defined by using the first 3 Principal Components; Hotelling's T-squared distribution is also applied and by using transmissibility two other indicators, Transmissibility Damage Indicator and Weighted Damage Indicator, are computed for the assessment of damage. The reported examples show that all proposed methods are able to detect and quantify damages at the initial stage.

Handling parameterized models for monitoring structures or processes is a natural approach to damage or fault detection and diagnosis. Modeling damages and faults as deviations in the parameter vector with respect to its nominal value in safe conditions calls for the use of statistical change detection and isolation methods. Such methods share the ability of handling noises and uncertainties with other statistical approaches.The purpose of this article is to describe the key elements in the design of change detection algorithms. Basic concepts (likelihood ratio, estimation, and innovation) are introduced. These tools involve a number of familiar operations (integration, averaging, sensitivity, adaptive thresholds, and windows). It is suggested that a statistical framework enlightens the meaning and increases the power of those operations. Vibration‐based structural health monitoring is addressed within this framework.

Active structural health monitoring methods are becoming increasingly popular for defect detection in hollow cylindrical structures utilized in aerospace applications. Electro mechanical impedance (EMI) based methods are commonly used to study the health of hollow cylinders in broad frequency ranges. However, the EMI analyzers are bulky and costly. In addition, EMI based methods require high quantity of sensors for monitoring a large area. The Surface Response to Excitation (SuRE) method was developed to eliminate the need for EMI analyzers in broad frequency range inspections and to decrease the number of sensors in monitoring of large structures. In this study, the performance of the SuRE method in detection of internal defects is evaluated theoretically and experimentally. Different internal damages were created in an aluminum pipe specimen. The data collected from four bonded piezoelectric elements were used for damage detection and quantification. The test results showed the high sensitivity and robustness of the SuRE method in internal damage detection.

Damage detection procedure of offshore structures based on data-driven techniques is of paramount importance to ensure their safety and integrity, especially in real-world applications. This procedure become more challenging in case of long-term structural health monitoring of offshore jacket platforms, which are inevitably subjected to the marine environmental conditions and various uncertainty sources the leads to variability in their dynamic characteristics. Due to the importance of the mentioned challenge and enhance damage detectability in these structures, the main aim of this article is to propose an improved density-based clustering method for data-driven damage detection with the aid of a distance scaling technique. The major contribution of the proposed method is to deal with the problem of finding clusters among large datasets with varied densities based on a multi-dimensional scaling technique. This method not only suppresses the effect of uncertainty sources always available in offshore structures but also enhances the performance of data-driven damage detection methodology. The feasibility and reliability of the method presented in this study is exemplified by application in a laboratory jacket-type offshore platform under different damage scenarios along with several comparative studies. Results are demonstrated to be effective and successful in detecting early damage of the offshore structure in the presence of various uncertainty sources.