Automatic Crack Segmentation for UAV-assisted Bridge Inspection

Bridges are a critical piece of infrastructure in the network of road and rail transport system. Many of the bridges in Norway (in Europe) are at the end of their lifespan, therefore regular inspection and maintenance are critical to ensure the safety of their operations. However, the traditional inspection procedures and resources required are so time consuming and costly that there exists a significant maintenance backlog. The central thrust of this paper is to demonstrate the significant benefits of adapting a Unmanned Aerial Vehicle (UAV)-assisted inspection to reduce the time and costs of bridge inspection and established the research needs associated with the processing of the (big) data produced by such autonomous technologies. In this regard, a methodology is proposed for analysing the bridge damage that comprises three key stages, (i) data collection and model training, where one performs experiments and trials to perfect drone flights for inspection using case study bridges to inform and provide necessary (big) data for the second key stage, (ii) 3D construction, where one built 3D models that offer a permanent record of element geometry for each bridge asset, which could be used for navigation and control purposes, (iii) damage identification and analysis, where deep learning-based data analytics and modelling are applied for processing and analysing UAV image data and to perform bridge damage performance assessment. The proposed methodology is exemplified via UAV-assisted inspection of Skodsberg bridge, a 140 m prestressed concrete bridge, in the Viken county in eastern Norway.publishedVersio

Caracterização do litoral central de Pernambuco (Brasil) quanto ao processo erosivo em curto e médio-termo

Este artigo tem por objetivo melhorar o entendimento acerca do processo erosivo em curso junto ao litoral central da Região Metropolitana do Recife em termos de espacialização do processo e escalas temporais envolvidas. Para tanto, foram analisadas variações de curto e médio-termo, além de revisados estudos anteriores, analisados indicadores de erosão e inventariados registros de danos noticiados nos principais jornais em circulação no estado. Os resultados obtidos mostraram que o processo não se comporta de maneira homogênea ao longo do litoral, o qual apresenta áreas de erosão intensa e outras nas quais o processo não ocorre ou é incipiente. Tampouco sob o ponto de vista temporal a área é homogênea, apresentando segmentos com distintos comportamentos da linha de costa nas diferentes escalas temporais consideradas. Ademais, foi possível observar um importante componente antrópico atuando sobre a redução da largura da pós-praia em determinados segmentos, representado pelo avanço da ocupação sobre setores do sistema praial. Os resultados do estudo evidenciaram a necessidade de se considerar a variabilidade espaço-temporal das praias estudadas antes de qualquer intervenção e demonstraram a importância de se desenvolver programas ou ações de manejo costeiro que preconizem a manutenção/restauração dos setores do sistema praial, uma das principais demandas ambientais atuais na região.

Análise bayesiana da confiabilidade de produtos em desenvolvimento Reliability assessment of products under development

Este artigo apresenta um método para a avaliação da confiabilidade de produtos em desenvolvimento. O método proposto permite a utilização de diversas fontes de informação comumente encontradas nas etapas de desenvolvimento de um produto tais como dados de campo ou dados de garantia (na forma de taxas de falha), dados de teste e evidência subjetiva (opiniões de especialistas com relação ao impacto de modificações de projeto na confiabilidade do produto). Este método também possibilita a incorporação de evidência referente a revisões prévias do mesmo produto ou da mesma informação sobre produtos que são apenas semelhantes ao produto em desenvolvimento. O método proposto é ilustrado pela análise de confiabilidade de tubos de raios X de alta potência, em que se verifica que a avaliação da confiabilidade de um novo projeto antes da execução de testes com unidades, incorporando as modificações de projeto sugeridas, fornece ao fabricante uma relevante fonte de informações para tomadas de decisão referentes à efetiva implementação das modificações de projeto.This paper presents a method to assess the reliability of products under development. The proposed methodology allows for the use of various sources of information commonly found in the development stages of a product, such as field or warranty data (failure rates), test data and subjective evidence (engineers/operators' opinions about the impact of design changes on the product's reliability). The method also allows for the incorporation of evidence of previous revisions of the same product or even information about products that are only similar to the one under investigation. The proposed methodology is illustrated through an example of the reliability assessment of high power X-ray tubes, which confirms that the reliability assessment of a new design before the actual testing of units containing the proposed design modifications offers the manufacturer a relevant source of information upon which to base decisions about the effective implementation of the design modifications

System-level prognostics and health management: A graph convolutional network-based framework

Sensing technologies have been used to gather massive amounts of data to improve system reliability analysis with the use of deep learning. Their use has been mainly focused on specific components or for the whole system, resulting in a drawback when dealing with complex systems as the interactions among components are not explicitly taken into account. Here, we propose a system-level prognostics and health management framework based on geometrical deep learning where a system, its components with their interactions, and sensor data are represented as a graph. This enables reliability analysis at different hierarchical levels by means of (1) a system-level module for system health diagnosis and prognosis based on embeddings of the system's learned features from a graph convolutional network; (2) a component-level module based on a deep graph convolutional network for health state diagnosis for the system's components; (3) a component interactions module based on a graph convolutional network autoencoder that allows for the identification of interactions among components when the system is in a degraded state. The framework is exemplified via a case study involving a chlorine dioxide generation system, in which it is shown that integrating both components' interactions and sensor data in the form of a graph improves health state diagnosis capabilities.Comisión Nacional de Investigación Científica y Tecnológica (CONICYT) CONICYT FONDECYT 119072

Time to failure assessment of products at service conditions from accelerated lifetime tests with stress-dependent spread in life

In accelerated lifetime testing (ALT) the assumption of stress-independent spread in life is commonly used and accepted because the resulting models are typically easier to use and data or past experience suggest that such a constrain is sometimes valid. However in many situations and with a variety of products the spread in life does depend on stress, i.e., the failure mechanism is not the same for all stress levels. In this paper the assessment of product time to failure at service conditions from ALT with stress-dependent spread is addressed by formulating a Bayesian framework where the time to failure follows a Weibull distribution, scale parameter dependency on stress is given by the Power Law, and two cases for the dependency between shape parameter and stress are discussed: linear relationship and, in order to allow a comparative analysis, stress-independent shape parameter. A previously published dataset is used to illustrate the procedure.<br>Em testes acelerados de vida (ALT) a suposição de que a dispersão do tempo de falha é independente do stress é freqüentemente empregada e aceita pois os modelos resultantes são tipicamente mais fáceis de utilizar e dados ou experiência adquirida sugerem que tal simplificação é algumas vezes válida. Entretanto, em muitas situações e para uma variedade de produtos, a dispersão do tempo de falha depende do stress, i.e., o mecanismo de falha não é o mesmo em todos os níveis de stress. Neste artigo, a estimação do tempo de falha do produto nas condições de serviço a partir de ALT com dispersão dependente do stress é discutida através da formulação de um modelo Bayesiano onde o tempo de falha segue uma distribuição de Weibull e o parâmetro de forma é dependente do stress via uma relação linear. Um conjunto de dados anteriormente publicado é utilizado para ilustrar o procedimento

A prognostics approach based on the evolution of damage precursors using dynamic Bayesian networks

During the lifetime of a component, microstructural changes emerge at its material level and evolve through time. Classical empirical degradation models (e.g. Paris Law in fatigue crack growth) are usually established based on monitoring and estimating well-known direct damage indicators such as crack size. However, by the time the usual inspection techniques efficiently identify such damage indicators, most of the life of the component would have been expended, and usually it would be too late to save the component. Therefore, it is important to detect damage at the earliest possible time. This article presents a new structural health monitoring and damage prognostics framework based on evolution of damage precursors representing the indirect damage indicators, when conventional direct damage indicator, such as a crack, is unobservable, inaccessible, or difficult to measure. Dynamic Bayesian network is employed to represent all the related variables as well as their causal or correlation relationships. Since the degradation model based on damage precursor evolution is not fully recognized, the methodology needs to be capable of online-learning the degradation process as well as estimating the damage state. Therefore, the joint particle filtering technique is implemented as an inference method inside the dynamic Bayesian network to assess both model parameters and damage states simultaneously. The proposed framework allows the integration of any related sources of information in order to reduce the inherent uncertainties. Incorporating different types of evidences in dynamic Bayesian network entails advance techniques to identify and formulate the possible interaction between potentially nonhomogenous variables. This article uses the support vector regression in order to define generally unknown nonparametric and nonlinear correlation between the input variables. The methodology is successfully applied to damage estimation and prediction of crack initiation in a metallic alloy under fatigue. The proposed framework is intended to be general and comprehensive so that it can be implemented in different applications.Chilean National Fund for Scientific and Technological Development (Fondecyt) 116049

An improved multi-unit nuclear plant seismic probabilistic risk assessment approach

This paper proposes an improved approach to external event probabilistic risk assessment for multi-unit sites. It considers unit-to-unit dependencies based on the integration of the copula notion, importance sampling, and parallel Monte Carlo simulation, including their implementation on standard PRA software tools. The multi-unit probabilistic risk assessment (MUPRA) approach and issues related to the current methods for seismic dependencies modeling are discussed. The seismic risk quantification is discussed in the context of two typical numerical schemes: the discretization-based scheme and simulation-based scheme. The issues related to the current discretization-based scheme are also highlighted. To address these issues and to quantify the seismic risk at the site level, an improved approach is developed to quantify the site-level fragilities. The approach is based on a hybrid scheme that involves the simulation-based method to account for the dependencies among the multi-unit structures, systems and components (SSCs) at the group level of dependent SSCs, and the discretization-based scheme. Finally, a case study is developed for the seismic-induced Small Loss of Coolant Accident (SLOCA) for a hypothetical nuclear plant site consisting of two identical advanced (GEN-III) reactor units. The results from this case study summarize the effects of correlation across multiple reactor units on the site-level core damage frequency (CDF). Three multi-unit CDF metrics (site, concurrent and marginal) were calculated for this case study. It is concluded that based on correlations between the SSCs, the total site CDF metric would be the most appropriate multi-unit CDF metric for seismic risk. (C) 2017 Elsevier Ltd. All rights reserved.US NRC grant NRCHQ6014G001

A common cause failure model for components under age-related degradation

This paper discusses component age-related degradation and failure initiated from a shared cause and coupling factor (or mechanism) and the likelihood of the resulting common cause failure (CCF). For these components a CCF model that includes the impacts of any maintenance-related renewal is proposed. Limitations and gaps in the state-of-the-art parametric CCF models for properly handling impacts of shared causes leading to accelerated degradation and aging have been discussed. The proposed approach characterizes the likelihood of CCF based on the conventional parametric CCF model, but unlike the parametric CCF models, time-dependent CCF parameters are estimated from the degradation states including any component rejuvenation achieved through preventive maintenance. Accelerated degradation tests of three identical centrifugal pumps under shared but harsh operating conditions generated several types of sensor monitoring data until failure. Correlation between the sensor monitoring data and observed aging and pump failure times were used to infer the degradation states of the pumps tested. The results concluded that undetected shared causes that could accelerate degradation and aging, for example due to poor maintenance, could significantly affect the CCF parametric model and CCF probability. This could potentially underestimate risk estimates as the undetected components degradation accumulates. The proposed parametric CCF model would be able to determine component-specific dynamic CCF probability, for condition monitored comments using sensor information relatable to degradation and aging.US NRC grant: NRCHQ6014G0015 Center for Risk and Reliability at the University of Marylan