Optimization of aviation maintenance, repair, and overhaul (MRO) operations has been of high interest in recent years for both the knowledge institutions and the industrial community as a total of approximately $70 billion has been spent on MRO activities in 2018 which represents around 10% of an airline’s annual operational cost (IATA, 2019). Moreover, the aircraft MRO tasks vary from routine inspections to heavy overhauls and are typically characterized by unpredictable process times and material requirements. Especially nowadays due to the unprecedent COVID-19 crisis, the aviation sector is facing significant challenges, and the MRO companies strive to strengthen their competitive position and respond to the increasing demand for more efficient, cost-effective, and sustainable processes. Currently, most maintenance strategies employ preventive maintenance as an industrial standard, which is based on fixed and predetermined schedules. Preventive maintenance is a long-time preferred strategy, due to increased flight safety and relatively simple implementation (Phillips et al., 2010). However, its main drawback stems from the fact that the actual time of failure and the replacement interval of a component are hard to predict resulting in an inevitable suboptimal utilization of material and labor. This has two repercussions: first, the reduced availability of assets, the reduced capacity of maintenance facilities, and the increased costs for both the MRO provider and the operator. Second, the increased waste from an environmental standpoint, as the suboptimal use of assets, is also associated with wasted remaining lifetime for aircraft parts which are replaced, while this isn’t yet necessary (e.g., Nguyen et al., 2019).The recently introduced, condition-based maintenance (CBM) and predictive maintenance (PdM) data-driven strategies aim to reduce maintenance costs, maxi-mize availability, and contribute to sustainable operations by offering tailored pro-grams that can potentially result in optimally planned, just-in-time maintenance meaning reduction in material waste and unneeded inspections.
This paper reports about preparatory work for future standardization that is carried out through an EU coordination and support action titled IM-SAFE. It focuses on applied digital technologies for monitoring and safety, including predictive maintenance of bridges and tunnels. Amidst the improved affordability of digitalization technologies and techniques, the biggest challenge in monitoring and maintenance of bridges and tunnels is no longer about collecting data as much as possible, but about obtaining and exploiting meaningful data throughout the lifecycle of the built assets. An effective and efficient data-driven approach is important to al-low both human experts and computers to make accurate diagnostics, predictions, and decisions. Further standardization is seen as an important part to reach that goal. The work in IM-SAFE related to ICT standardization focuses on the following topics: (1) the general requirements and preconditions for high quality and cost-effective acquisition, transmission, storage and processing of monitoring datasets to ensure the data is fully accessible and machine-interpretable; (2) the relations between the future standards in structural engineering with the open ICT standards for interoperability, especially on Internet of Things (IoT), Building Information Model (BIM), Geographical Information System (GIS), and Semantic Linked Data (LD); (3) a common design of IT platforms to manage monitoring and asset management data of transport infrastructures; (4) the ways to facilitate data analytics technologies, including AI, to be applied for monitoring and asset management of transport infrastructures, and to assess the added value of data-driven approach next to physics-based modelling. With regard to these topics, this paper reports the outcomes from the expert and stakeholder consultations that recently took place within the IM-SAFE pan-European Community of Practice.
During crime scene investigations, numerous traces are secured and may be used as evidence for the evaluation of source and/or activity level propositions. The rapid chemical analysis of a biological trace enables the identification of body fluids and can provide significant donor profiling information, including age, sex, drug abuse, and lifestyle. Such information can be used to provide new leads, exclude from, or restrict the list of possible suspects during the investigative phase. This paper reviews the state-of-the-art labelling techniques to identify the most suitable visual enhancer to be implemented in a lateral flow immunoassay setup for the purpose of trace identification and/or donor profiling. Upon comparison, and with reference to the strengths and limitations of each label, the simplistic one-step analysis of noncompetitive lateral flow immunoassay (LFA) together with the implementation of carbon nanoparticles (CNPs) as visual enhancers is proposed for a sensitive, accurate, and reproducible in situ trace analysis. This approach is versatile and stable over different environmental conditions and external stimuli. The findings of the present comparative analysis may have important implications for future forensic practice. The selection of an appropriate enhancer is crucial for a well-designed LFA that can be implemented at the crime scene for a time- and cost-efficient investigation.
