Condition assessment of bridge structures using statistical analysis of wavelets

La surveillance à distance des structures a émergé comme une préoccupation importante pour les ingénieurs afin de maintenir la sécurité et la fiabilité des infrastructures civiles pendant leur durée de vie. Les techniques de surveillance structurale (SHM) sont de plus en plus populaires pour fournir un diagnostic de "l'état" des structures en raison de leur vieillissement, de la dégradation des matériaux ou de défauts survenus pendant leur construction. Les limites de l'inspection visuelle et des techniques non destructives, qui sont couramment utilisées pour détecter des défauts extrêmes sur les parties accessibles des structures, ont conduit à la découverte de nouvelles technologies qui évaluent d’un seul tenant l'état global d'une structure surveillée. Les techniques de surveillance globale ont été largement utilisées pour la reconnaissance d'endommagement dans les grandes infrastructures civiles, telles que les ponts, sur la base d'une analyse modale de la réponse dynamique structurale. Cependant, en raison des caractéristiques complexes des structures oeuvrant sous des conditions environnementales variables et des incertitudes statistiques dans les paramètres modaux, les techniques de diagnostic actuelles n'ont pas été concluantes pour conduire à une méthodologie robuste et directe pour détecter les incréments de dommage avant qu'ils n'atteignent un stade critique. C’est ainsi que des techniques statistiques de reconnaissance de formes sont incorporées aux méthodes de détection d'endommagement basées sur les vibrations pour fournir une meilleure estimation de la probabilité de détection des dommages dans des applications in situ, ce qui est habituellement difficile compte tenu du rapport bruit à signal élevé. Néanmoins, cette partie du SHM est encore à son stade initial de développement et, par conséquent, d'autres tentatives sont nécessaires pour parvenir à une méthodologie fiable de détection de l'endommagement. Une stratégie de détection de dommages basée sur des aspects statistiques a été proposée pour détecter et localiser de faibles niveaux incrémentiels d'endommagement dans une poutre expérimentale pour laquelle tant le niveau d'endommagement que les conditions de retenue sont réglables (par exemple ancastrée-ancastrée et rotulée-rotulée). Premièrement, des expériences ont été effectuées dans des conditions de laboratoire contrôlées pour détecter de faibles niveaux d'endommagement induits (par exemple une fissure correspondant à 4% de la hauteur d’une section rectangulaire équivalente) simulant des scénarios d'endommagement de stade précoce pour des cas réels. Différents niveaux d'endommagement ont été simulés à deux endroits distincts le long de la poutre. Pour chaque série d'endommagement incrémentiel, des mesures répétées (~ 50 à 100) ont été effectuées pour tenir compte de l'incertitude et de la variabilité du premier mode de vibration de la structure en raison d'erreurs expérimentales et du bruit. Une technique d'analyse par ondelette basée sur les modes a été appliquée pour détecter les changements anormaux survenant dans les modes propres causées par le dommage. La réduction du bruit ainsi que les caractéristiques des agrégats ont été obtenues en mettant en œuvre l'analyse des composantes principales (PCA) pour l'ensemble des coefficients d'ondelettes calculés à des nœuds (ou positions) régulièrement espacés le long du mode propre. En rejetant les composantes qui contribuent le moins à la variance globale, les scores PCA correspondant aux premières composantes principales se sont révélés très corrélés avec de faibles niveaux d'endommagement incrémentiel. Des méthodes classiques d'essai d'hypothèses ont été effectuées sur les changements des paramètres de localisation des scores pour conclure objectivement et statistiquement, à un niveau de signification donné, sur la présence du dommage. Lorsqu'un dommage statistiquement significatif a été détecté, un nouvel algorithme basé sur les probabilités a été développé pour déterminer l'emplacement le plus probable de l'endommagement le long de la structure. Deuxièmement, se basant sur l'approche probabiliste, une série de tests a été effectuée dans une chambre environnementale à température contrôlée pour étudier les contributions relatives des effets de l’endommagement et de la température sur les propriétés dynamiques de la poutre afin d’estimer un facteur de correction pour l'ajustement des scores extraits. Il s'est avéré que la température avait un effet réversible sur la distribution des scores et que cet effet était plus grand lorsque le niveau d'endommagement était plus élevé. Les résultats obtenus pour les scores ajustés indiquent que la correction des effets réversibles de la température peut améliorer la probabilité de détection et minimiser les fausses alarmes. Les résultats expérimentaux indiquent que la contribution combinée des algorithmes utilisés dans cette étude était très efficace pour détecter de faibles niveaux d'endommagement incrémentiel à plusieurs endroits le long de la poutre tout en minimisant les effets indésirables du bruit et de la température dans les résultats. Les résultats de cette recherche démontrent que l'approche proposée est prometteuse pour la surveillance des structures. Cependant, une quantité importante de travail de validation est attendue avant sa mise en œuvre sur des structures réelles. Mots-clés : Détection et localisation des dommages, Poutre, Mode propre, Ondelette, Analyse des composantes principales, Rapport de probabilité, TempératureRemote monitoring of structures has emerged as an important concern for engineers to maintain safety and reliability of civil infrastructure during its service life. Structural Health Monitoring (SHM) techniques are increasingly becoming popular to provide ideas for diagnosis of the "state" of potential defects in structures due to aging, deterioration and fault during construction. The limitations of visual inspection and non-destructive techniques, which were commonly used to detect extreme defects on only accessible portions of structures, led to the discovery of new technologies which assess the "global state" of a monitored structure at once. Global monitoring techniques have been used extensively for the recognition of damage in large civil infrastructure, such as bridges, based on modal analysis of structural dynamic response. However, because of complicated features of real-life structures under varying environmental conditions and statistical uncertainties in modal parameters, current diagnosis techniques have not been conclusive in ascertaining a robust and straightforward methodology to detect damage increments before it reaches its critical stage. Statistical pattern recognition techniques are incorporated with vibration-based damage detection methods to provide a better estimate for the probability of the detection of damage in field applications, which is usually challenging given the high noise to signal ratio. Nevertheless, this part of SHM is still in its initial stage of development and, hence, further attempts are required to achieve a reliable damage detection methodology. A statistical-based damage detection strategy was proposed to detect and localize low levels of incremental damage in an experimental beam in which the level of damage and beam restraint conditions are adjustable (e.g. fixed-fixed and pinned-pinned). First, experiments were performed in controlled laboratory conditions to detect small levels of induced-damage (e.g. 4% crack height for an equivalent rectangular section) simulated for early stage damage scenarios in real cases. Various levels of damage were simulated at two distinct locations along the beam. For each sate of incremental damage, repeat measurements (~ 50 to 100) were performed to account for uncertainty and variability in the first vibration mode of the structure due to experimental errors and noise. A modal-based wavelet analysis technique was applied to detect abnormal changes occurring in the mode shapes caused by damage. Noise reduction as well as aggregate characteristics were obtained by implementing the Principal Component Analysis (PCA) into the set of wavelet coefficients computed at regularly spaced nodes along the mode shape. By discarding components that contribute least to the overall variance, the PCA scores corresponding to the first few PCs were found to be highly correlated with low levels of incremental damage. Classical hypothesis testing methods were performed on changes on the location parameters of the scores to conclude damage objectively and statistically at a given significance level. When a statistically significant damage was detected, a novel Likelihood-based algorithm was developed to determine the most likely location of damage along the structure. Secondly, given the likelihood approach, a series of tests were carried out in a climate-controlled room to investigate the relative contributions of damage and temperature effects on the dynamic properties of the beam and to estimate a correction factor for the adjustment of scores extracted. It was found that the temperature had a reversible effect on the distribution of scores and that the effect was larger when the damage level was higher. The resulted obtained for the adjusted scores indicated that the correction for reversible effects of temperature can improve the probability of detection and minimize false alarms. The experimental results indicate that the combined contribution of the algorithms used in this study were very efficient to detect small-scale levels of incremental damage at multiple locations along the beam, while minimizing undesired effects of noise and temperature in the results. The results of this research demonstrate that the proposed approach may be used as a promising tool for SHM of actual structures. However, a significant amount of challenging work is expected for implementing it on real structures. Key-words: Damage Detection and Localization, Beam, Mode Shape, Wavelet, Principal Component Analysis, Likelihood Ratio, Temperatur

Natural Disaster Detection Using Wavelet and Artificial Neural Network

Indonesia, by the location of its geographic and geologic, it have more potential encounters for natural disasters. This nation is traversed by three tectonic plates, namely: IndoAustralian, the Eurasian and the Pacific plates. One of the tools employed to detect danger and send an early disaster warning is sensor device for ocean waves, but it has drawbacks related to the very limited time gap between information/warnings obtained and the real disaster event, which is only less than 30 minutes. Natural disaster early detection information system is essential to prevent potential danger. The system can make use of the pattern recognition of satellite imagery sequences that take place before and during the natural disaster. This study is conducted to determine the right wavelet to compress the satellite image sequences and to perform the pattern recognition process of a natural disaster employing an artificial neural network. This study makes use of satellite imagery sequences of tornadoes and hurricanes

Acquisition and processing of new data sources for improved condition monitoring of mechanical systems

190 p.Este trabajo está centrado en el desarrollo de nuevas formas de monitorización en línea del estado de salud de sistemas mecánicos mediante tecnologías poco utilizadas hasta ahora en este campo. En particular, se han investigado el uso de la monitorización de la viscosidad del aceite lubricante y la tecnología de análisis de las características de la corriente que alimenta el motor para obtener conocimiento sobre el estado de las cajas de engranajes. Por un lado, se presenta una nueva solución basada en materiales magnetoelásticos para la monitorización de la viscosidad del aceite lubricante. Por el otro, el análisis de la corriente alimentación del motor (MCSA por sus siglas en inglés) se presenta como alternativa de los acelerómetros tradicionales para la monitorización de anomalías mecánicas.En particular, se ha desarrollado un sensor magnetoelástico de viscosidad cinemática para mediciones en línea. La principal ventaja del sensor propuesto es su capacidad de medir en una amplia gama de valores de viscosidad (desde 32 cSt hasta 320 cSt). No se conoce ningún otro sensor equivalente comercialmente disponible con un rango similar.Con respecto al análisis de las características de la corriente de alimentación del motor (MCSA), el objetivo de la Tesis es poder diseñar un sistema para monitorizar una caja de engranajes en funcionamiento normal. En este sentido, se ha abordado el análisis de transitorios de velocidad, manteniendo la carga fija. Se ha utilizado un banco de pruebas de cajas de engranajes para reproducir diferentes fallos y adquirir datos en diferentes condiciones de operación

Acquisition and processing of new data sources for improved condition monitoring of mechanical systems

190 p.Este trabajo está centrado en el desarrollo de nuevas formas de monitorización en línea del estado de salud de sistemas mecánicos mediante tecnologías poco utilizadas hasta ahora en este campo. En particular, se han investigado el uso de la monitorización de la viscosidad del aceite lubricante y la tecnología de análisis de las características de la corriente que alimenta el motor para obtener conocimiento sobre el estado de las cajas de engranajes. Por un lado, se presenta una nueva solución basada en materiales magnetoelásticos para la monitorización de la viscosidad del aceite lubricante. Por el otro, el análisis de la corriente alimentación del motor (MCSA por sus siglas en inglés) se presenta como alternativa de los acelerómetros tradicionales para la monitorización de anomalías mecánicas.En particular, se ha desarrollado un sensor magnetoelástico de viscosidad cinemática para mediciones en línea. La principal ventaja del sensor propuesto es su capacidad de medir en una amplia gama de valores de viscosidad (desde 32 cSt hasta 320 cSt). No se conoce ningún otro sensor equivalente comercialmente disponible con un rango similar.Con respecto al análisis de las características de la corriente de alimentación del motor (MCSA), el objetivo de la Tesis es poder diseñar un sistema para monitorizar una caja de engranajes en funcionamiento normal. En este sentido, se ha abordado el análisis de transitorios de velocidad, manteniendo la carga fija. Se ha utilizado un banco de pruebas de cajas de engranajes para reproducir diferentes fallos y adquirir datos en diferentes condiciones de operación

Advanced Information Processing Methods and Their Applications

This Special Issue has collected and presented breakthrough research on information processing methods and their applications. Particular attention is paid to the study of the mathematical foundations of information processing methods, quantum computing, artificial intelligence, digital image processing, and the use of information technologies in medicine

Wavelet Theory

The wavelet is a powerful mathematical tool that plays an important role in science and technology. This book looks at some of the most creative and popular applications of wavelets including biomedical signal processing, image processing, communication signal processing, Internet of Things (IoT), acoustical signal processing, financial market data analysis, energy and power management, and COVID-19 pandemic measurements and calculations. The editor’s personal interest is the application of wavelet transform to identify time domain changes on signals and corresponding frequency components and in improving power amplifier behavior

Signal Based Data Mining For Feature Extraction And Fault Detection

Tez (Doktora) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2012Thesis (PhD) -- İstanbul Technical University, Institute of Science and Technology, 2012Bu çalışmada, işaret işleme teknikleri ve veri madenciliği yöntemleri kullanılarak özellik çıkarımı ve hata tespiti için yeni bir yöntem geliştirilmiştir. Uygulama ana olarak iki aşamadan oluşmaktadır. Bunlar sırasıyla veri ön-işleme ve yapay sinir ağı aşamalarıdır. Veri ön-işleme aşamasında asenkron bir elektrik motorunun sağlam durumundaki titreşim işareti ham veri olarak kullanılmıştır. Bu aşamada işarete ilk olarak sürekli dalgacık dönüşümü ve Fourier dönüşümü (Güç spektrumu yoğunluğu - GSY) teknikleri uygulanmıştır. Frekans domeninde temsil edilen bu yeni alt-işaret, yapay sinir ağı aşamasında bir öz-ilişkili yapay sinir ağının eğitimi için kullanılmıştır. Eğitim aşamasından sonra benzer veri kümesi ağın geri çağrılmasında kullanılmış ve bu sayede bir arıza eşik değeri belirlenmiştir. Bu yapay sinir ağı, sağlam durumdaki titreşim işaretinin güç spektrumu yoğunluğu ile test edilmiş ve eşik değerini aşan değerler arıza olarak değerlendirilmiştir. Bu model, yapay sinir ağı test aşamasında elde edilen sonuçlarla karşılaştırılıp özellik çıkarımı ve potansiyel hata tespiti açısından yorumlanmıştır. Buna ek olarak yöntem titreşim işaretinin haricinde iki adet simulasyon verisinde de uygulanmıştır. Sonuçlara bakıldığında yöntemin kullanılan tüm veriler için potansiyel arızaları belirlediği gözlenmiştir. Son olarak, çalışmada verilen işlem ve tekniklerin bir kullanıcı tarafından gerçekleştirilebilmesi amacıyla bir bilgisayar uygulaması oluşturulmuştur.In this study, a new method for fault detection and feature extaction is introduced by using signal processing and data mining techniques. The application is consisted of two main phases: data pre-processing and artificial neural network. Vibration signal measurements from the healthy state of an electric motor is used as the raw data for the application. At the data pre-processing phase, continuous wavelet transform and Fourier transform techniques are applied to the vibration signal. At the artificial neural network phase, this sub-signal is given as an input to an auto-associative neural network for training. After the training, the neural network is recalled by the same type of data for identifying a threshold value. At the test phase, the network is tested by the Fourier transform of the signal. According to the comparison with the threshold values, the faulty states are identified. The results are concluded in terms of feature extraction and fault detection of potential defects. In addition to this, the method is applied on two different types of simulation data. The method is capable of identifying the potential defects and faults for all the data provided. Finally, a computer application is developed in order to perform the method.DoktoraPh

Application of Wavelet Analysis and Random Field in Integrity Management of Pipelines Containing Dents and Corrosions

Metal loss corrosions and dents are two major threats to the integrity of oil and natural gas pipelines. In the pipeline industry, the Fitness-For-Service (FFS) assessment is commonly employed for pipelines containing these defects. However, FFS assessment usually assumes that a defect has a simple shape, and such a simplification may significantly affect the accuracy of the assessment. Therefore, retaining the actual shapes of defects and incorporating them into the FFS assessment can improve assessment accuracy. The main objective of the present thesis is to extract key information about the sizes, directions, and shapes of corrosions and dents from the measurement of in-service and excavated pipelines, and then improve the accuracy of FFS assessment based on the extracted information. The first study develops a wavelet transform-based denoising method for the measured inner surface of in-service dented pipelines obtained from caliper tools. Since the inner surface is differently sampled along the longitudinal and circumferential directions, the commonly used denoising methods cannot sufficiently remove measurement errors from the signal. The proposed method is based on overcomplete expansion, and the overcomplete dictionary is constructed from the hyperbolic wavelet transform and stationary transform. The strain estimated from the signal denoised by the proposed method is closer to the actual strain than the other denoising method. An overcomplete dictionary that can effectively denoise the dent signal is then constructed based on the statistics of the measurement of in-service dented pipelines. The second study explores the vital directional features and length scales of natural corrosion clusters that govern the burst capacity of corroded pipelines. The corrosion depths in a cluster are measured by high-resolution laser scans, and two-dimensional (2D) discrete wavelet transform (DWT) with a suitable wavelet function is employed to decompose the corrosion cluster. A methodology is proposed to determine level- and sub-band-dependent thresholds such that those wavelet coefficients below the thresholds have a negligible impact on the burst capacity predicted by the widely used RSTRENG model and can be ignored for the reconstruction of the cluster. The preserved wavelet coefficients show that longitudinally orientated features with 4 – 32 mm in length have a greater influence on the remaining burst capacity than other features. This facilitates FFS assessment of corroded pipelines. The third study aims to simulate the corrosion fields whose morphology and marginal distribution are close to the actual corrosion fields from limited information summarized from the ILI data. The corrosion field containing multiple corrosion anomalies is modelled as a nonhomogeneous non-Gaussian random field, where the spatial correlation and marginal distribution of anomalies are estimated from their sizes. The proposed methodology provides realizations of corrosion fields with the RSTRENG-predicted burst capacity closer to the actual burst capacity than the commonly used methodology that idealizes anomalies as cuboids. The fourth study presents a framework to analyze and simulate nonhomogeneous non-Gaussian corrosion fields on the external surface of buried in-service pipelines by using continuous and discrete wavelet transforms. Continuous wavelet transform (CWT), dual-tree complex discrete wavelet transform (DT-CDWT), and dual-tree complex discrete wavelet with hyperbolic wavelet transform scheme (DT-CHWT) are incorporated into the iterative power and amplitude correction (IPAC) algorithm to extract the features of the natural corrosion field measured by a high-resolution laser scan and generate synthetic corrosion fields. The results indicate that the proposed framework can generate synthetic corrosion fields that effectively capture probabilistic characteristics of the measured corrosion field in terms of the scalogram, textural features, and burst capacity of the pipe segment containing the corrosion field