Model‐Based Statistical Signal Processing for Change and Damage Detection

This thesis is devoted to present some novel techniques in Structural Health Monitoring (SHM). SHM is a developing field that tries to monitor structures to make sure that they remain in their desired condition to avoid any catastrophe. SHM includes different levels from damage detection area to prognosis field. This work is dedicated to the first level, which might be considered the main and most important level. New techniques presented in this work are based on different statistical and signal processing methods such as Principal Component Analysis and its robust counterpart, Wavelet Transform, Fuzzy similarity, Andrew plots, etc. These techniques are applied on the propagated waves that are activated and captured in the structure using appropriate transducers. Piezoceramic (PZT) devices are chosen in this work to capture the signals due to their special characteristics such as high performance, low energy consumption and reasonable price. To guarantee the efficiency of the suggested techniques, they are tested on different laboratory and real scale test benchmarks, such as aluminum and composite plates, fuselage, wing skeleton, tube, etc. Because of the variety of tested benchmarks, this thesis is called damage detection in smart structures. This variety may promise the ability and capability of the proposed methods on different fields such as aerospace and gas/oil industry. In addition to the normal laboratory conditions, it is shown in this work that environmental changes can affect the performance of the damage detection and wave propagation significantly. As such, there is a vital need to consider their effect. In this work, temperature change is chosen as it is one of the main environmental fluctuation factors. To scrutinize its effect on damage detection, first, the effect of temperature is considered on wave propagation and then all the proposed methods are tested to check whether they are sensitive to temperature change or not. Finally, a temperature compensation method is applied to ensure that the proposed methods are stable and robust even when structures are subjected to variant environmental conditions. La presente tesis doctoral se dedica a la exploración y presentación de técnicas novedosas para la Monitorización y detección de defectos en estructuras (Structural Health Monitoring -SHM-) SHM es un campo actualmente en desarrollo que pretende asegurarse que las estructuras permanecen en su condición deseada para evitar cualquier catástrofe. En SHM se presentan diferentes niveles de diagnóstico, Este trabajo se concentra en el primer nivel, que se considera el más importante, la detección de los defectos. Las nuevas técnicas presentadas en esta tesis se basan en diferentes métodos estadísticos y de procesamiento de señales tales como el Análisis de Componentes Princpales (PCA) y sus variaciones robustas, Transformada wavelets, lógica difusa, gráficas de Andrew, etc. Estas técnicas de aplican sobre las ondas de vibración que se generan y se miden en la estructura utilizando trasductores apropiados. Dispositivos piezocerámicos (PZT's) se han escogido para este trabajo ya que presentan características especiales tales como: alto rendimiento, bajo consumo de energia y bajo costo. Para garantizar la eficacia de la metodología propuesta,se ha validado en diferentes laboratorios y estructuras a escala real: placas de aluminio y de material compuesto, fuselage de un avión, revestimiento del ala de un avóin, tubería, etc. Debido a la gran variedad de estructuras utilizadas, su aplicación en la industria aeroespacial y/o petrolera es prometedora. Por otra parte, los cambios ambientales pueden afectar al rendimiento de la detección de daños y propagación de la onda significativamente . En este trabajo , se estudia el efecto de las variaciones de temperatura ya que es uno de los principales factores de fluctuación del medio ambiente . Para examinar su efecto en la detección de daños, en primer lugar, todos los métodos propuestos se prueban para comprobar si son sensibles a los cambios de temperatura o no. Finalmente , se aplica un método de compensación de temperatura para garantizar que los métodos propuestos son estables y robustos incluso cuando las estructuras se someten a condiciones ambientales variantes

In the present work, the design of thermoplastic induced welded joints was carried out considering different aspects related to structural performance, manufacturability and structural health monitoring capability. Three lap-joints configurations were considered: two of them having welded plates with two different carbon fibers plies stacked layups and one obtained by welding carbon fibers fabric laminates. A numerical analysis was carried out by performing (i) structural simulations to assess the mechanical properties of the joints, (ii) induction welding process simulations to evaluate temperature distribution in the welded interface area and (iii) damage detection assessment by reproducing the response of a structural health monitoring sensor bonded in correspondence of the joint overlap area. Electromagnetic-based structural health monitoring techniques were employed to evaluate local variation of electromagnetic characteristics due to defect presence. The results obtained show that the design choices have a significant influence on the physical properties of the welded material such as the electrical conductivity anisotropy. The different induced currents distribution obtained during both the fabrication and damage inspection phase, affects respectively the manufacturing and detection capability. An experimental validation was carried out by mechanical tests under quasi-static loading for the case with the best tradeoff between structural performance, manufacturing feasibility and damage detection capability. The proposed approach show the importance of taking into account the requirement of damage detectability from an early stage of the design.

first_page settings Order Article Reprints Font Type: Arial Georgia Verdana Font Size: Aa Aa Aa Line Spacing:    Column Width:    Background: Open AccessAbstract Structural Health Monitoring Using Carbon Nanotube Yarns: Sensing Concept and Applications in Composites † by Jandro L. Abot Department of Mechanical Engineering, The Catholic University of America, Washington, DC 20064, USA † Presented at the 5th International Symposium on Sensor Science (I3S 2017), Barcelona, Spain, 27–29 September 2017. Proceedings 2017, 1(8), 852; https://doi.org/10.3390/proceedings1080852 Published: 4 December 2017 (This article belongs to the Proceedings of Proceedings of the 5th International Symposium on Sensor Science (I3S 2017)) Download Download PDF Download PDF with Cover Download XML Versions Notes Non-destructive evaluation and structural health monitoring techniques can provide frequent or immediate feedback of the condition of a structure including potential damage. However, these techniques cannot detect initiating damage in composite materials with high compaction or multifaceted construction. More critically, they are unable to achieve damage detection without altering the microstructure of the composite material. An alternative method of strain monitoring and damage detection that may offer the advantages of structural health monitoring without their drawbacks consists of using piezoresistive-based carbon nanotube (CNT) yarns integrated in polymers and composite materials. The concept is that the CNT yarns form a continuous sensor circuit and their inherent piezoresistive sensitivity detects strain within the host material through resistance measurements without adding much weight or altering the integrity of the host material. This presentation includes a summary of the piezoresistive response of CNT yarns and the concept and latest experimental results on damage detection in laminated polymeric composite materials and distributed and localized strain measurement. Experimental results also show the ability of a combination of different yarn sensors to detect the exact location and extent of delamination in real time. CNT yarn sensors may provide an adaptive, practical, and sensitive structural health monitoring technique. © 2017 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Share and Cite MDPI and ACS Style Abot, J.L. Structural Health Monitoring Using Carbon Nanotube Yarns: Sensing Concept and Applications in Composites. Proceedings 2017, 1, 852. https://doi.org/10.3390/proceedings1080852 AMA Style Abot JL. Structural Health Monitoring Using Carbon Nanotube Yarns: Sensing Concept and Applications in Composites. Proceedings. 2017; 1(8):852. https://doi.org/10.3390/proceedings1080852 Chicago/Turabian Style Abot, Jandro L. 2017. "Structural Health Monitoring Using Carbon Nanotube Yarns: Sensing Concept and Applications in Composites" Proceedings 1, no. 8: 852. https://doi.org/10.3390/proceedings1080852 Find Other Styles Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here. Article Metrics No No Article Access Statistics Multiple requests from the same IP address are counted as one view.

Damage due to age or accumulated damage from hazards on existing structures poses a worldwide problem. In order to evaluate the current status of aging, deteriorating and damaged structures, it is vital to accurately assess the present conditions. It is possible to capture the in situ condition of structures by using laser scanners that create dense 3D point clouds. However, extracting meaningful information from point clouds automatically is challenging, and the current state-of-the-art requires a certain amount of user-interaction. Previously in our research, widely accepted point cloud processing steps were used to divide laser scanner data into meaningful object clusters, on which several damage detection methods were applied. Our current focus is on combining useful information extracted from laser scanner data with color information since novel laser scanners are capable of recording color data mapped on 3D point clouds. The color data provides additional information in the 4 th dimension, which makes it possible to detect damage types such as cracks, corrosion, and related surface defects that are generally difficult to detect by using only laser scanner data, and also helps to track volumetric changes on structures such as spalling. Even though crack and corrosion detection from images with varying resolution are extensively researched topics, damage detection by using laser scanners with and without images is a new research area that holds many opportunities for enhancing current practice in structural health monitoring. The aim of our research is to benefit from the best features of both laser scans and images, and combine them to create an automatic and effective damage detection method, which will complement the need for skilled labor during visual inspections of damaged structures and allow documenting related information automatically. This work is of interest to researchers who are focused on structural health monitoring and interested in the use of new laser scanners and damage detection algorithms for structures, as well as practitioners, who will benefit exploring new methods for improving their understanding of laser scanning technology and its capabilities for robust damage detection and documentation.

Damage detection using permanently installed guided wave sensors typically involves identifying changes in A-scan data from a nominally defect-free state. A well-recognised challenge is the influence of changing operational and environmental conditions which can lead to complex evolution of the A-scan data which may mask the presence of damage. This paper is based on the observation that the amplitude at each location within the A-scan has a unique functional dependence with temperature and a unique associated uncertainty in the functional dependence. The uncertainty in the functional dependence is based on both the repeatability of the measurements and the amount of data available for characterisation. The fact that each location in an A-scan has a different uncertainty leads to the concept of an adaptive threshold. In contrast to typical damage detection that utilises a flat threshold for the whole A-scan, an adaptive threshold will be lower in locations of greater predictability and rise in locations of poorer repeatability. To achieve the aim of an adaptive threshold, the amplitude–temperature dependence and associated uncertainty is characterised for each data-point in an A-scan based on a training set of defect-free data. The inverse process can then be performed to generate a new reference A-Scan with associated confidence bounds for a given temperature. The confidence bounds act as the adaptive threshold; if a new measurement passes outside the confidence bounds then it indicates statistically significant difference between the new measurement and the training set. To demonstrate the method, pitch-catch data between two sensors of a pipe-bend over 5 months and a temperature range of approximately 50°C is used. To demonstrate the damage detection capability in a controlled way synthetic damage is added to the A-scans to produce test data. The performance is compared to Optimal baseline subtraction (OBS). In this example, the performance of the Gaussian process regression adaptive threshold method exceeds OBS when the training set exceeds around 40 measurements.

Impact of time synchronization error on the mode-shape identification and damage detection/localization in WSNs for structural health monitoring

The purpose of this study was to examine the influence of image properties on the accuracy of remote sensing change detection methods. Spectral class separability, radiometric normalization and image band correlation were evaluated through experiments with simulated data. The experimental results were then evaluated as a tool for predicting the relative accuracy of change detection results obtained from Landsat TM satellite image pairs of three U.S. cities: Las Vegas, Nevada; Phoenix, Arizona; and Atlanta, Georgia. The change detection methods used were post-classification comparison, direct classification, image differencing, principal component analysis, and change vector analysis. Results of the simulated experiments confirmed that the relative accuracy of the change detection methods varied with changes in image properties. For the class separability experiments, post-classification comparison, direct classification, image differencing, and PCA with a large number of the principal components, were found generally to have higher accuracies than CVA and PCA with a small number of the principal components. For classes with very good separability, image differencing is an excellent method; for classes with poor spectral separability, image differencing was found to have the lowest accuracy. The influence of the error in radiometric normalization on the accuracy of change detection techniques varied greatly with different degrees of class separability. This can be seen particularly well with image differencing, which showed the highest sensitivity to large changes in radiometric error and very poor class separability. Image differencing and PCA were also found to be more sensitive to band correlation. The classification of the real change detection data from the Landsat pairs showed complex and varying patterns, depending on whether complete (mapping all unchanged and changed transitions) or partial (grouping all unchanged transitions into a single class) change analysis was conducted. However, image differencing was relatively consistent in producing good results for the real data. Also, PCA produced satisfactory results for all three cities. On the other hand, the CVA was found to have amongst the lowest accuracies of the partial change detection methods, though this was higher than most of the complete change accuracy. The variation of accuracy results obtained from the change detection methods used in this study suggests that the contradictory results found in previous change detection studies is likely at least partially a result of varying image properties.

PurposeOne of the paramount challenges in the field of structural health monitoring (SHM) is the precise assessment of structural damage. Recently, many methods have been proposed to diagnose damage in structures using time series data. Nevertheless, these techniques are restricted in their ability to capture the bidirectional temporal and spatial relationships among the data. To remedy these shortcomings, this study proposes an advanced deep learning (DL) technique that integrates one-dimensional convolutional neural networks (1DCNN) with bidirectional long short-term memory (BiLSTM) networks, termed BiLSTM-1DCNN.Design/methodology/approachThis study proposes an advanced deep learning (DL) technique that integrates one-dimensional convolutional neural networks (1DCNN) with bidirectional long short-term memory (BiLSTM) networks, termed BiLSTM-1DCNN. The novelty of BiLSTM-1DCNN comes in its effective fusion of 1DCNN’s local feature extraction and BiLSTM’s capacity to capture long-term information from both directions (forward and backward).FindingsValidation using a dataset from the Nam O steel truss bridge demonstrates that the proposed method surpasses conventional models, including 1DCNN (87.9%), LSTM (72.5%), BiLSTM (76.1%) and LSTM-1DCNN (91.1%), achieving an accuracy rate of 96.4%. This approach provides a significant advancement in SHM, paving the way for more accurate and reliable damage detection.Research limitations/implicationsThe study’s limitations include potential overfitting due to the model’s complexity and the high computational resources required. The BiLSTM-1DCNN model’s performance is also influenced by the quality and quantity of the training data as well as the specific characteristics of the dataset. Additionally, while the model performs well in detecting damage in the Nam O steel truss bridge, its generalizability to other types of structures and environmental conditions remains untested.Practical implicationsThe BiLSTM-1DCNN model offers significant practical benefits for structural health monitoring by enhancing damage detection accuracy. Its ability to effectively capture both local features and long-term temporal dependencies makes it a valuable tool for real-time monitoring and early detection of structural issues. This can lead to more timely maintenance actions, potentially reducing repair costs and improving safety. The model’s high accuracy in detecting damage can also support infrastructure management decisions, contributing to better resource allocation and enhanced overall safety of critical structures.Social implicationsThe BiLSTM-1DCNN model’s advanced damage detection capabilities have positive social implications, such as improving the safety and reliability of public infrastructure like bridges. Enhanced structural health monitoring can reduce the risk of catastrophic failures, protecting lives and reducing accidents. By providing more accurate and timely assessments, the model can also lead to more efficient use of maintenance and repairs, ensuring that resources are allocated effectively.Originality/valueThe originality of the BiLSTM-1DCNN model lies in its innovative integration of one-dimensional convolutional neural networks (1DCNN) with bidirectional long short-term memory (BiLSTM) networks. This unique combination leverages 1DCNN’s strength in local feature extraction and BiLSTM’s capacity to capture temporal dependencies in both directions, addressing the limitations of existing methods. The model’s high accuracy in detecting structural damage, as demonstrated by its performance on the Nam O steel truss bridge dataset, highlights its value in advancing structural health monitoring technologies and offers a significant improvement over traditional approaches.

Change detection using deep learning approach with object-based image analysis

This article suggests the process of structural health monitoring (SHM) in the context of a nonstatistical damage detection paradigm. We particularly focus on applying the theory of possibility to the damage detection problem. The basic idea behind the proposed approach is that the application of possibility theory does not require probabilistic knowledge or assumptions on the damage feature and thus encompasses aleatoric and epistemic types of uncertainties. The approach is not damage feature dependent and thus is generic for use in many SHM systems. Additionally, two new damage metrics are introduced. These metrics extract information concerning damage evidence from observations performed at unknown health states of structures. Damage detection with the aid of the proposed approach is demonstrated by means of a case study.

In civil engineering, health monitoring of structure have been treated as an important subject. Health monitoring by using experimental measurements such as strain, acceleration, velocity, displacement, rotation and other parameters has been used for detecting changes, which may indicate damage or degradation. In recent, the applications of health monitoring of large structure have been facilitated by the developments in measurement, communications and computational technology gradually. Especially, large civil structures as bridges are damaged by fatigue loads or unexpected load such as earthquakes and finally reach to failure. Thus, a structural health monitoring and damage detection methods have been developed and attracted a great deal of public attention in recent decades. Actually many research efforts have been conducted to develop a health monitoring and damage detection techniques of existing civil structures. However, these methods are needed to improve to apply to a real civil structure. The purpose of this study is to develop a practical instrument to identify dynamic parameters of real civil structural systems.

Structural health monitoring (SHM) and damage detection have attracted great interest in recent decades, in meeting the challenges of assessing the safety condition of large-scale civil structures. By wiring remote sensors directly to a centralized data acquisition system, traditional structural health monitoring systems are usually costly and the installation is time-consuming. Recent advances in wireless sensing technology have made it feasible for structural health monitoring; furthermore, the computational core in a wireless sensing unit offers onboard data interrogation. In addition to wireless sensing, the authors have recently developed a mobile sensing system for providing high spatial resolution and flexible sensor deployment in structural health monitoring. In this study, transmissibility function analysis is embedded in the mobile sensing node to perform onboard and in-network structural damage detection. The system implementation is validated using a laboratory 2D steel portal frame. Simulated damage is applied to the frame structure, and the damage is successfully identified by two mobile sensing nodes that autonomously navigate through the structure.

In the present paper, Structural health monitoring has become an evolving area of research in last few decades with increasing need of online monitoring the health of large structures. The damage detection by visual inspection of the structure can prove impractical, expensive and ineffective in case of large structures like offshore platforms, multistoried buildings and bridges. Structural health monitoring is defined as the process of detecting damage in a structural system. Damage in the system causes a change in dynamic properties of a system. The structural damage is typically a local phenomenon, which tends to be captured by higher frequency signals. Most of vibration-based damage detection methods require the modal properties that are obtained from measured signals through the system identification techniques. However, the modal properties such as natural frequencies and mode shapes are not such a good sensitive indication of structural damage. Structural damage detection and damage localization of jacket platforms, based on wavelet packet transforms is presented in this paper. Dynamic signals measured from the structure by the finite element software package ANSYS are first decomposed into wavelet packet components. Component energies are then calculated and used for damage assessment. The results show that the WPT-based component energies are good candidate indices that are sensitive to structural damage. These component energies can be used for damage assessment including identifying damage occurrence and location.

ABSTRACTIn order to improve the accuracy of change detection and reduce the running time, a change detection method based on equal weight image fusion and adaptive threshold in the NSST domain is proposed. First, the logarithmic transformation is used to transform images and the mean filter is applied to the transformed images. The log-ratio method and the mean ratio method are adopted to generate two kinds of difference images. The final difference image is achieved by equal weight image fusion method. Then, an adaptive threshold denoising method based on non-subsampled shearlet transform (NSST) is used to achieve noise reduction. Finally, the k-means clustering algorithm is utilized to get the change detection results. The experimental results show that the proposed algorithm has better change detection performance than the reference algorithms in visual effect and objective parameters.

With its synoptic view and the repeatability, satellite remote sensing can provide timely, accurate and consistent information about earth’s surface for cost-effective monitoring of environmental changes. In this chapter, recent development in change detection techniques using multitemporal remotely sensed images were reviewed. The chapter covers change detection methods for both optical and SAR images. Various aspects of change detection processes were presented including data preprocessing, change image generation and change detection algorithms such as unsupervised and supervised change detection as well as pixel-based and object-based change detection. The review shows that significant progress has been made in the field of change detection and innovative methods have been developed for change detection using both multitemporal SAR and optical data. Attempts have been made for change detection using multitemporal multisensor/cross-sensor images. The review also identified a number of challenges and opportunities in change detection.