Multi-Temporal InSAR for transport infrastructure monitoring: Recent trends and challenges

Worldwide, transport infrastructure is increasingly vulnerable to ageing-induced deterioration and climate-related hazards. Oftentimes inspection and maintenance costs far exceed available resources, and numerous assets lack any rigorous structural evaluation. Space-borne Synthetic Aperture Radar Interferometry (InSAR) is a powerful remote-sensing technology, which can provide cheaper deformation measurements for bridges and other transport infrastructure with short revisit times, while scaling from the local to the global scale. As recent studies have shown the InSAR accuracy to be comparable with traditional monitoring instruments, InSAR could offer a cost-effective tool for long-term, near-continuous deformation monitoring, with the possibility to support inspection planning and maintenance prioritisation, while maximising functionality and increasing the resilience of infrastructure networks. However, despite the high potential of InSAR for structural monitoring, some important limitations need to be considered when applying it in reality. This paper identifies and discusses the challenges of using InSAR for the purpose of structural monitoring, with a specific focus on bridges and transport networks. Examples are presented to illustrate current practical limitations of InSAR; possible solutions and promising research directions are identified. The aim of this study is to motivate future action in this area and highlight the InSAR advances needed to overcome current challenges.

A probabilistic fatigue crack growth life approach to the definition of inspection intervals for railway axles

Different options that rely on fracture mechanics are currently used in engineering during the design and assessment of components. One of the most important aspects is the time taken for a crack to extend to its critical size. If this time is long enough, a design concept based on inspection intervals can be applied, as is it the case of a railway axle component. To define inspection intervals that ensure the continuous and safe operation of a damage-tolerant railway axle, a reliable estimation of its fatigue crack growth life is required. Due to the uncertainties involved in the fatigue process, inspections must be devised not only considering the uncertainties in the performance of the inspection technique, but also based on a probabilistic lifespan prediction. From this premise, this paper presents a procedure for determination of inspection intervals that uses a conservative fatigue crack growth life estimation based on the lifespan probability distribution. A practical example to illustrate the reliability-based inspection planning methodology in a railway axle under random bending loading is given. The inspection intervals are further assessed in terms of overall probability of detecting cracks in successive inspections and in terms of probability of failure, considering the probability of detection curve of the non-destructive testing technique. The procedure developed provides recommendation for the definition of inspection intervals and associated inspection techniques.

Mooring Sense Project: A Risk-Based Integrity Management Strategy for Mooring System of Floating Offshore Wind

While Floating Offshore Wind (FOW) represents a significant opportunity to foster wind energy development and to contribute to remarkable CO2 emissions reductions, its associated operational costs are still substantially above grid parity, and significant innovation is needed. MooringSense is a research and innovation project which explores digitisation technologies to enable the implementation of risk-based integrity management strategies for mooring systems in the FOW sector with the aim to optimise Operations and Maintenance (O&M) activities, reduce costs, and increase energy production. As part of this project, a risk-based assessment methodology specific for the mooring system of Floating Offshore Wind Turbines (FOWT) has been developed; this allows the development of a risk-based Mooring Integrity Management Strategy that can result in more cost-effective inspection planning. The methodology shall utilise the information made available by numerical tools, sensors, and algorithms developed in the project to update the risk level of the mooring system and set the required plan to mitigate the risk. Leveraging the additional information from monitoring technologies and predictive capabilities to determine the mooring system condition and remaining lifetime, the strategy provides the criteria for optimal decision making with regards to selection of O&M activities. The risk-based strategy developed allows for optimal planning of inspection and maintenance activities based on dynamic risk level that is periodically updated through the interface with the Digital Twin (DT). The validation of the strategy will demonstrate potential cost saving and economic advantages, however, it is expected that the overall MooringSense approach can reduce FOW farm operational costs by 10-15% and increase operational efficiency by means of an Annual Energy Production increase by 2-3%. The MooringSense project comprises of the development and validation of innovative solutions coming from multiple disciplines such as numerical modelling, simulation, Global Navigation Satellite System (GNSS), Structural Health Monitoring (SHM), and control systems which will provide valuable input to the risk-based mooring integrity management strategy.

Viewpoint placement for inspection planning

Inspection planning approaches so far have focused on automatically obtaining an optimal set of viewpoints required to cover a given object. While research has provided interesting results, the automatic inspection planning has still not been made a part of the everyday inspection system development process. This is mostly because the plans are difficult to verify and it is impossible to compare them to laboratory-developed plans. In this work, we give an overview of available generate-and-test approaches, evaluate their results for various objects and finally compare them to plans created by inspection system development experts. The comparison emphasizes both benefits and downsides of automated approaches and highlights problems which need to be tackled in the future in order to make the automated inspection planning more applicable.

Development of a Coordinate Measuring Machine—Based Inspection Planning System for Industry 4.0

Industry 4.0 represents a new paradigm which creates new requirements in the area of manufacturing and manufacturing metrology such as to reduce the cost of product, flexibility, mass customization, quality of product, high level of digitalization, optimization, etc., all of which contribute to smart manufacturing and smart metrology systems. This paper presents a developed inspection planning system based on CMM as support of the smart metrology within Industry 4.0 or manufacturing metrology 4.0 (MM4.0). The system is based on the application of three AI techniques such as engineering ontology (EO), GA and ants colony optimization (ACO). The developed system consists of: the ontological knowledge base; the mathematical model for generating strategy of initial MP; the model of analysis and optimization of workpiece setups and probe configuration; the path simulation model in MatLab, PTC Creo and STEP-NC Machine software, and the model of optimization MP by applying ACO. The advantage of the model is its suitability for monitoring of the measurement process and digitalization of the measurement process planning, simulation carried out and measurement verification based on CMM, reduction of the preparatory measurement time as early as in the inspection planning phase and minimizing human involvement or human errors through intelligent planning, which directly influences increased production efficiency, competitiveness, and productivity of enterprises. The measuring experiment was performed using a machined prismatic workpiece (PW).