Optimal methodologies for ultrasonic guided-wave based structural health monitoring

The assessment of structural integrity is a key issue for many industries due to its important implications in safety, maintenance cost reduction, and improved asset availability. In this context, structural health monitoring (SHM) systems using ultrasonic guided-waves are being explored for an efficient diagnosis of damage and prognosis of the remaining useful life of the monitored structure. Nonetheless, addressing this monitoring scenario is a challenge given the inherent complexities associated to each of the diagnosis steps, which encompass the optimal SHM design, the detection of damage, its localisation, and its identification. Among these complexities, uncertainties stemming from several sources such as equipment noise, manufacturing defects, and the lack of conclusive knowledge about wave propagation introduce a high variability in the response of the SHM system. The main objective of this thesis is to provide probabilistic Bayesian and fuzzy logic methodologies to manage global uncertainties for each step in the SHM process. The accuracy and reliability of an ultrasonic guided-wave based SHM system are dependent on the chosen number and location of sensors and actuators. A general framework for optimal sensor configuration based on value of information is proposed in this thesis, which trades-off information gain and cost. This approach optimally chooses the sensor position so that they render the largest information gain when inferring the damage location. The methodology is tested using different case studies in the context of ultrasonic guided waves and piezoelectric sensors. However, although this framework is mathematically rigorous, it is computationally expensive should the actuators be considered in the optimisation problem. To overcome this issue, a cost-benefit analysis is also proposed using both the Shannon's information entropy and a cost function associated to the number of sensors and actuators. The objective function is based on binary decision variables, which are relaxed into continuous variables, hence convexifying the objective function. This optimisation methodology is illustrated in several case studies considering plate-like structures with irregular geometries and different materials, providing a high computational efficiency. The first diagnosis stage requires a robust and computationally efficient damage detection approach in real-life engineering scenarios. To this end, a novel damage index for ultrasonic guided-wave measurements based on fuzzy-logic principles is proposed in this thesis. This approach assesses the time of flight mismatch between signals acquired in undamaged and non-pristine states using fuzzy sets for its evaluation. The robustness partially builds on the use of a large amount of signals stemming from two experimental procedures: the round robin configuration and the transmission beamforming technique. This new damage index is validated in several scenarios with sudden and progressive damage. Once a damage area has been detected, the next diagnosis stage requires a reliable damage localisation. To address this SHM step, a robust methodology is proposed based on two hierarchical levels: (1) a Bayesian time-frequency model class selection to obtain the time of flight of damage scattered waves; and (2) a Bayesian inverse problem of damage localisation that considers as input data the outcome of the first level. The effectiveness and robustness of the proposed methodology is illustrated using two cases studies with one and two areas of damage. Lastly, to provide a complete diagnosis of damage using ultrasonic guided-waves, the identification of damage needs to be addressed. A multi-level hybrid wave and finite element model-based Bayesian approach is proposed to identify the type of damage in composite beams based on posterior probabilities, hence accounting for different sources of uncertainty. In addition to the type of damage, this approach allows the inference of damage-related parameters and the damage location. A carbon fibre beam with two damage modes, i.e. a crack and a delamination, is used to illustrate the methodology

Optimal methodologies for ultrasonic guided-wave based structural health monitoring

The assessment of structural integrity is a key issue for many industries due to its important implications in safety, maintenance cost reduction, and improved asset availability. In this context, structural health monitoring (SHM) systems using ultrasonic guided-waves are being explored for an efficient diagnosis of damage and prognosis of the remaining useful life of the monitored structure. Nonetheless, addressing this monitoring scenario is a challenge given the inherent complexities associated to each of the diagnosis steps, which encompass the optimal SHM design, the detection of damage, its localisation, and its identification. Among these complexities, uncertainties stemming from several sources such as equipment noise, manufacturing defects, and the lack of conclusive knowledge about wave propagation introduce a high variability in the response of the SHM system. The main objective of this thesis is to provide probabilistic Bayesian and fuzzy logic methodologies to manage global uncertainties for each step in the SHM process. The accuracy and reliability of an ultrasonic guided-wave based SHM system are dependent on the chosen number and location of sensors and actuators. A general framework for optimal sensor configuration based on value of information is proposed in this thesis, which trades-off information gain and cost. This approach optimally chooses the sensor position so that they render the largest information gain when inferring the damage location. The methodology is tested using different case studies in the context of ultrasonic guided waves and piezoelectric sensors. However, although this framework is mathematically rigorous, it is computationally expensive should the actuators be considered in the optimisation problem. To overcome this issue, a cost-benefit analysis is also proposed using both the Shannon's information entropy and a cost function associated to the number of sensors and actuators. The objective function is based on binary decision variables, which are relaxed into continuous variables, hence convexifying the objective function. This optimisation methodology is illustrated in several case studies considering plate-like structures with irregular geometries and different materials, providing a high computational efficiency. The first diagnosis stage requires a robust and computationally efficient damage detection approach in real-life engineering scenarios. To this end, a novel damage index for ultrasonic guided-wave measurements based on fuzzy-logic principles is proposed in this thesis. This approach assesses the time of flight mismatch between signals acquired in undamaged and non-pristine states using fuzzy sets for its evaluation. The robustness partially builds on the use of a large amount of signals stemming from two experimental procedures: the round robin configuration and the transmission beamforming technique. This new damage index is validated in several scenarios with sudden and progressive damage. Once a damage area has been detected, the next diagnosis stage requires a reliable damage localisation. To address this SHM step, a robust methodology is proposed based on two hierarchical levels: (1) a Bayesian time-frequency model class selection to obtain the time of flight of damage scattered waves; and (2) a Bayesian inverse problem of damage localisation that considers as input data the outcome of the first level. The effectiveness and robustness of the proposed methodology is illustrated using two cases studies with one and two areas of damage. Lastly, to provide a complete diagnosis of damage using ultrasonic guided-waves, the identification of damage needs to be addressed. A multi-level hybrid wave and finite element model-based Bayesian approach is proposed to identify the type of damage in composite beams based on posterior probabilities, hence accounting for different sources of uncertainty. In addition to the type of damage, this approach allows the inference of damage-related parameters and the damage location. A carbon fibre beam with two damage modes, i.e. a crack and a delamination, is used to illustrate the methodology

Deep learning in automated ultrasonic NDE -- developments, axioms and opportunities

The analysis of ultrasonic NDE data has traditionally been addressed by a trained operator manually interpreting data with the support of rudimentary automation tools. Recently, many demonstrations of deep learning (DL) techniques that address individual NDE tasks (data pre-processing, defect detection, defect characterisation, and property measurement) have started to emerge in the research community. These methods have the potential to offer high flexibility, efficiency, and accuracy subject to the availability of sufficient training data. Moreover, they enable the automation of complex processes that span one or more NDE steps (e.g. detection, characterisation, and sizing). There is, however, a lack of consensus on the direction and requirements that these new methods should follow. These elements are critical to help achieve automation of ultrasonic NDE driven by artificial intelligence such that the research community, industry, and regulatory bodies embrace it. This paper reviews the state-of-the-art of autonomous ultrasonic NDE enabled by DL methodologies. The review is organised by the NDE tasks that are addressed by means of DL approaches. Key remaining challenges for each task are noted. Basic axiomatic principles for DL methods in NDE are identified based on the literature review, relevant international regulations, and current industrial needs. By placing DL methods in the context of general NDE automation levels, this paper aims to provide a roadmap for future research and development in the area.Comment: Accepted version to be published in NDT & E Internationa

Robust optimal sensor configuration using the value of information

This paper is part of the SAFE-FLY project that has received funding from the European Union's Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie (Grant Agreement No. 721455). The authors acknowledge the support acquired by the Brazilian National Council of Research CNPq (Grant Agreement ID: 314168/2020-6).Sensing is the cornerstone of any functional structural health monitoring technology, with sensor number and placement being a key aspect for reliable monitoring. We introduce for the first time a robust methodology for optimal sensor configuration based on the value of information that accounts for (1) uncertainties from updatable and nonupdatable parameters, (2) variability of the objective function with respect to nonupdatable parameters, and (3) the spatial correlation between sensors. The optimal sensor configuration is obtained by maximizing the expected value of information, which leads to a cost-benefit analysis that entails model parameter uncertainties. The proposed methodology is demonstrated on an application of structural health monitoring in plate-like structures using ultrasonic guided waves. We show that accounting for uncertainties is critical for an accurate diagnosis of damage. Furthermore, we provide critical assessment of the role of both the effect of modeling and measurement uncertainties and the optimization algorithm on the resulting sensor placement. The results on the health monitoring of an aluminum plate indicate the effectiveness and efficiency of the proposed methodology in discovering optimal sensor configurations.European Union's Horizon 2020 Research and Innovation Programme 721455Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ) 314168/2020-

Lamb wave-based damage indicator for plate-like structures

Structural health monitoring based on ultrasonics typically involves complex data analysis. Ultrasound monitoring based on Lamb waves techniques are extensively used nowadays due to their efficiency in exploring large areas with relatively small attenuation. In recent years, baseline based methods have been developed to identify structural damage based on the mismatch between the measured signal and the baseline one. To this end, complex time-frequency transformations are required to obtain signal features such as the time of arrival or the energy content, as indicators of damage onset and growth. Notwithstanding this, on-board applications require highly efficient processing techniques due to information storage and exchange limitations. This paper proposes a very high efficiency signal processing methodology to obtain a novel cumulative damage factor using Lamb wave raw data. The new methodology has been tested using ultrasonic and damage data from a fatigue test in carbon-epoxy composite laminates. The data is taken from NASA Prognostics data repository. In view of the results, the method is able to efficiently detect the onset and extent of damage from early stages of degradation. Moreover, the results demonstrate a remarkable agreement between the growth of delamination area and the predicted cumulative damage factor

Structural Health Monitoring Using Ultrasonic Guided-Waves and the Degree of Health Index

This paper proposes a new damage index named degree of health (DoH) to efficiently tackle structural damage monitoring in real-time. As a key contribution, the proposed index relies on a pattern matching methodology that measures the time-of-flight mismatch of sequential ultrasonic guided-wave measurements using fuzzy logic fundamentals. The ultrasonic signals are generated using the transmission beamforming technique with a phased-array of piezoelectric transducers. The acquisition is carried out by two phased-arrays to compare the influence of pulse-echo and pitch-catch modes in the damage assessment. The proposed monitoring approach is illustrated in a fatigue test of an aluminum sheet with an initial notch. As an additional novelty, the proposed pattern matching methodology uses the data stemming from the transmission beamforming technique for structural health monitoring. The results demonstrate the efficiency and robustness of the proposed framework in providing a qualitative and quantitative assessment for fatigue crack damage

Un novedoso algoritmo de parametrización para la detección de daño en laminados de CFRP

A novel blind damage parametrization algorithm is presented for selecting the most plausible parametrization to identify damage in ultrasonic measurements obtained from a damaged carbon fiber reinforced polymer plate. This parametrization is selected among all possible ones, extracting those whose plausibilities are higher, so the ultrasonic signals can be well fitted. The algorithm performance is validated with a set of synthetically damaged signals. The results show that the algorithm is able to detect the right damage positions and amounts, even if the added level of white Gaussian noise less than a signal-to-noise ratio of 25 dB. In addition, some experimental signals obtained from a post-impact fatigued CFRP plate were evaluated with our algorithm under two different damage configurations. The first one, which entails stiffness degradation of the layers, results as more plausible than the second one, which entails stiffness degradation and attenuation increase. The obtained damage patterns were compared to classic observational techniques such as micrography.Un novedoso algoritmo de parametrización de daño se presenta para seleccionar la parametrización más plausible que pueda identificar daño en medidas ultrasónicas obtenidas de un laminado de fibra de carbono. Esta parametrización se elige de entre todas las posibles, extrayendo aquellas cuyas plausibilidades son mayores, de tal manera que las señales ultrasónicas puedan ser mejor ajustadas. La validación del algoritmo se presenta con un set de señales sintéticamente dañadas. Los resultados muestran que el algoritmo es capaz de detectar correctamente la posición y cantidad de daño, incluso si el nivel de ruido blanco Gaussiano añadido a la señal llega hasta un ratio de señal-ruido de 25 dB. Además, algunas señales experimentales escogidas del especimen dañado con fatigua post-impacto fueron introducidas en nuestro algoritmo con dos configuraciones diferentes. La primera, que implica una degradación de la rigidez de las capas, se mostró como más plausible que la segunda, la cual implica una degradación de la rigidez además de un incremento de la atenuación. Los patrones de daño obtenidos fueron comparados con las técnicas observacionales clásicas como la micrografía.Universidad de Granada. Departamento de Mecánica de Estructuras e Ingeniería Hidráulica. Máster Universitario en Estructuras, curso 2013-201

Structural Health Monitoring Using Ultrasonic Guided-Waves and the Degree of Health Index

This paper proposes a new damage index named degree of health (DoH) to efficiently tackle structural damage monitoring in real-time. As a key contribution, the proposed index relies on a pattern matching methodology that measures the time-of-flight mismatch of sequential ultrasonic guided-wave measurements using fuzzy logic fundamentals. The ultrasonic signals are generated using the transmission beamforming technique with a phased-array of piezoelectric transducers. The acquisition is carried out by two phased-arrays to compare the influence of pulse-echo and pitch-catch modes in the damage assessment. The proposed monitoring approach is illustrated in a fatigue test of an aluminum sheet with an initial notch. As an additional novelty, the proposed pattern matching methodology uses the data stemming from the transmission beamforming technique for structural health monitoring. The results demonstrate the efficiency and robustness of the proposed framework in providing a qualitative and quantitative assessment for fatigue crack damage.European Commission 721455Aernnova Engineering Division S.A.University of Basque Countr