Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

Special Session on Industry 4.0

No abstract available

Eco-efficient process based on conventional machining as an alternative technology to chemical milling of aeronautical metal skin panels

El fresado químico es un proceso diseñado para la reducción de peso de pieles metálicas que, a pesar de los problemas medioambientales asociados, se utiliza en la industria aeronáutica desde los años 50. Entre sus ventajas figuran el cumplimiento de las estrictas tolerancias de diseño de piezas aeroespaciales y que pese a ser un proceso de mecanizado, no induce tensiones residuales. Sin embargo, el fresado químico es una tecnología contaminante y costosa que tiende a ser sustituida. Gracias a los avances realizados en el mecanizado, la tecnología de fresado convencional permite alcanzar las tolerancias requeridas siempre y cuando se consigan evitar las vibraciones y la flexión de la pieza, ambas relacionadas con los parámetros del proceso y con los sistemas de utillaje empleados. Esta tesis analiza las causas de la inestabilidad del corte y la deformación de las piezas a través de una revisión bibliográfica que cubre los modelos analíticos, las técnicas computacionales y las soluciones industriales en estudio actualmente. En ella, se aprecia cómo los modelos analíticos y las soluciones computacionales y de simulación se centran principalmente en la predicción off-line de vibraciones y de posibles flexiones de la pieza. Sin embargo, un enfoque más industrial ha llevado al diseño de sistemas de fijación, utillajes, amortiguadores basados en actuadores, sistemas de rigidez y controles adaptativos apoyados en simulaciones o en la selección estadística de parámetros. Además se han desarrollado distintas soluciones CAM basadas en la aplicación de gemelos virtuales. En la revisión bibliográfica se han encontrado pocos documentos relativos a pieles y suelos delgados por lo que se ha estudiado experimentalmente el efecto de los parámetros de corte en su mecanizado. Este conjunto de experimentos ha demostrado que, pese a usar un sistema que aseguraba la rigidez de la pieza, las pieles se comportaban de forma diferente a un sólido rígido en términos de fuerzas de mecanizado cuando se utilizaban velocidades de corte cercanas a la alta velocidad. También se ha verificado que todas las muestras mecanizadas entraban dentro de tolerancia en cuanto a la rugosidad de la pieza. Paralelamente, se ha comprobado que la correcta selección de parámetros de mecanizado puede reducir las fuerzas de corte y las tolerancias del proceso hasta un 20% y un 40%, respectivamente. Estos datos pueden tener aplicación industrial en la simplificación de los sistemas de amarre o en el incremento de la eficiencia del proceso. Este proceso también puede mejorarse incrementando la vida de la herramienta al utilizar fluidos de corte. Una correcta lubricación puede reducir la temperatura del proceso y las tensiones residuales inducidas a la pieza. Con este objetivo, se han desarrollado diferentes lubricantes, basados en el uso de líquidos iónicos (IL) y se han comparado con el comportamiento tribológico del par de contacto en seco y con una taladrina comercial. Los resultados obtenidos utilizando 1 wt% de los líquidos iónicos en un tribómetro tipo pin-on-disk demuestran que el IL no halogenado reduce significativamente el desgaste y la fricción entre el aluminio, material a mecanizar, y el carburo de tungsteno, material de la herramienta, eliminando casi toda la adhesión del aluminio sobre el pin, lo que puede incrementar considerablemente la vida de la herramienta.Chemical milling is a process designed to reduce the weight of metals skin panels. This process has been used since 1950s in the aerospace industry despite its environmental concern. Among its advantages, chemical milling does not induce residual stress and parts meet the required tolerances. However, this process is a pollutant and costly technology. Thanks to the last advances in conventional milling, machining processes can achieve similar quality results meanwhile vibration and part deflection are avoided. Both problems are usually related to the cutting parameters and the workholding. This thesis analyses the causes of the cutting instability and part deformation through a literature review that covers analytical models, computational techniques and industrial solutions. Analytics and computational solutions are mainly focused on chatter and deflection prediction and industrial approaches are focused on the design of workholdings, fixtures, damping actuators, stiffening devices, adaptive control systems based on simulations and the statistical parameters selection, and CAM solutions combined with the use of virtual twins applications. In this literature review, few research works about thin-plates and thin-floors is found so the effect of the cutting parameters is also studied experimentally. These experiments confirm that even using rigid workholdings, the behavior of the part is different to a rigid body at high speed machining. On the one hand, roughness values meet the required tolerances under every set of the tested parameters. On the other hand, a proper parameter selection reduces the cutting forces and process tolerances by up to 20% and 40%, respectively. This fact can be industrially used to simplify workholding and increase the machine efficiency. Another way to improve the process efficiency is to increase tool life by using cutting fluids. Their use can also decrease the temperature of the process and the induced stresses. For this purpose, different water-based lubricants containing three types of Ionic Liquids (IL) are compared to dry and commercial cutting fluid conditions by studying their tribological behavior. Pin on disk tests prove that just 1wt% of one of the halogen-free ILs significantly reduces wear and friction between both materials, aluminum and tungsten carbide. In fact, no wear scar is noticed on the ball when one of the ILs is used, which, therefore, could considerably increase tool life

Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Offshore Wind has become the most profitable renewable energy source due to the remarkable development it has experienced in Europe over the last decade. In this paper, a review of Structural Health Monitoring Systems (SHMS) for offshore wind turbines (OWT) has been carried out considering the topic as a Statistical Pattern Recognition problem. Therefore, each one of the stages of this paradigm has been reviewed focusing on OWT application. These stages are: Operational Evaluation; Data Acquisition, Normalization and Cleansing; Feature Extraction and Information Condensation; and Statistical Model Development. It is expected that optimizing each stage, SHMS can contribute to the development of efficient Condition-Based Maintenance Strategies. Optimizing this strategy will help reduce labor costs of OWTs׳ inspection, avoid unnecessary maintenance, identify design weaknesses before failure, improve the availability of power production while preventing wind turbines׳ overloading, therefore, maximizing the investments׳ return. In the forthcoming years, a growing interest in SHM technologies for OWT is expected, enhancing the potential of offshore wind farm deployments further offshore. Increasing efficiency in operational management will contribute towards achieving UK׳s 2020 and 2050 targets, through ultimately reducing the Levelised Cost of Energy (LCOE)

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Structural and seismic monitoring of historical and contemporary buildings: general principles and applications

Structural Health Monitoring (SHM) indicates the continuous or periodic assessment of the conditions of a structure or a set of structures using information from sensor systems, integrated or autonomous, and from any further operation that is aimed at preserving structural integrity. SHM is a broad and multidisciplinary field, both for the spectrum of sciences and technologies involved and for the variety of applications. The technological developments that have made the advancement of this discipline possible come from many fields, including physics, chemistry, materials science, biology, but above all aerospace, civil, electronic and mechanical engineering. The first applications, at the turn of the sixties and seventies, concerned the integrity control of remote structural elements, such as foundation piles and submerged parts of off-shore platforms, but nowadays this type of monitoring is practiced on airplanes, vehicles spacecraft, ships, helicopters, automobiles, bridges, buildings, civil infrastructure, power plants, pipelines, electronic systems, manufacturing and processing facilities, and biological systems. This paper carries out an extensive examination of the theoretical and applicative foundations of structural and seismic monitoring, focusing in particular on methods that exploit natural vibrations and their use both in the diagnosis and in the prediction of the seismic response of civil structures, infrastructure networks, and traditional and modern architectural heritage

Unsupervised identification of topological order using predictive models

Machine-learning driven models have proven to be powerful tools for the identification of phases of matter. In particular, unsupervised methods hold the promise to help discover new phases of matter without the need for any prior theoretical knowledge. While for phases characterized by a broken symmetry, the use of unsupervised methods has proven to be successful, topological phases without a local order parameter seem to be much harder to identify without supervision. Here, we use an unsupervised approach to identify topological phases and transitions out of them. We train artificial neural nets to relate configurational data or measurement outcomes to quantities like temperature or tuning parameters in the Hamiltonian. The accuracy of these predictive models can then serve as an indicator for phase transitions. We successfully illustrate this approach on both the classical Ising gauge theory as well as on the quantum ground state of a generalized toric code.Comment: 12 pages, 13 figure