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In order to reduce time and costs, while maintaining a high level of product quality, effective implementation of systems engineering processes may be beneficial for small and medium-sized enterprises. However, applying a prescriptive generic system engineering standard in the enterprise, without any consideration of the company's organisation, background and context, is inappropriate. Moreover, due to severe resource limitations, the difficulties in following and complying with a standard are even more obvious in SMEs. In this context, this paper explores a framework to help SMEs evaluate the maturity of processes, promote a progressive deployment and standardisation of their engineering processes to manage manufacturing projects, based on the experience and practices underway in SMEs and with reference to international systems engineering standards. Our proposal is to stand by companies to help them evolve their engineering processes by progressively aligning them with systems engineering standards without disrupting the overall organisation and practices of the company. [ABSTRACT FROM AUTHOR]

This paper aims at identifying retrofitting possibilities to incorporate existing spacecraft into a network of federated satellite systems. The paper presents a systematic review of possible retrofitting options, such as direct modifications including replacement and addition of interfaces, and indirect modifications through the addition of intermediary federated negotiators. The paper considers existing frequency regulations for the analysis in the technical domain, but does not take into consideration how complex or time-consuming any legislative changes might be. Although the paper concludes that direct modifications of existing satellites are nonfeasible from a technical point of view, it identifies a possible scenario of retrofitting by adding intermediary negotiator satellites. The link budget for the intersatellite link between an existing satellite mission, such as SPOT-6, and a conceptual satellite negotiator was estimated. The work concludes that from a link budget point of view and existing technology, such a configuration can provide a slant range from several hundred to thousands of kilometers. The work defines models for trade-off analysis identifying correlations between satellite negotiator parameters and the number of covered satellites. The paper concludes proposing several possible satellite negotiator architectures and high-level technical requirements based on analysis of characteristics of existing and planned satellites. [ABSTRACT FROM AUTHOR]

Obsolescence is the fact that an entity (physical or logical) is becoming outdated or no longer possesses the required level of performance. The objectives of this article are twofold. First, it is intended to contribute to the understanding of obsolescence propagation. Secondly, two supporting approaches for the Identification and Assessment phases are proposed: the House of Obsolescence and the System Obsolescence Criticality Analysis. The former allows the mapping of obsolescence propagation via dependencies, whether imposed changes are desired or imposed, by external actors to the system architecture. Whereas, the objective of the latter is to assign an obsolescence criticality index to the identified risks in order to prioritize them for solution or mitigation determination during the analysis phase. The tools make extensive use of the modeled system knowledge through the application of Systems Engineering. The application of these approaches is presented through an illustrative study. [ABSTRACT FROM AUTHOR]

With the increasing availability of data for Prognostics and Health Management (PHM), Deep Learning (DL) techniques are now the subject of considerable attention for this application, often achieving more accurate Remaining Useful Life (RUL) predictions. However, one of the major challenges for DL techniques resides in the difficulty of obtaining large amounts of labelled data on industrial systems. To overcome this lack of labelled data, an emerging learning technique is considered in our work: Self-Supervised Learning, a sub-category of unsupervised learning approaches. This paper aims to investigate whether pre-training DL models in a self-supervised way on unlabelled sensors data can be useful for RUL estimation with only Few-Shots Learning, i.e. with scarce labelled data. In this research, a fatigue damage prognostics problem is addressed, through the estimation of the RUL of aluminium alloy panels (typical of aerospace structures) subject to fatigue cracks from strain gauge data. Synthetic datasets composed of strain data are used allowing to extensively investigate the influence of the dataset size on the predictive performance. Results show that the self-supervised pre-trained models are able to significantly outperform the non-pre-trained models in downstream RUL prediction task, and with less computational expense, showing promising results in prognostic tasks when only limited labelled data is available. • Self-supervised learning is investigated to address data scarcity in prognostics. • Fatigue crack propagation prognostics in structures based on strain data. • Synthetic dataset composed of multivariate run-to-failure strain time series. • Self-supervised pre-trained models improve RUL estimation performance. [Display omitted] [ABSTRACT FROM AUTHOR]

One of the main goals of predictive maintenance is to be able to trigger the right maintenance actions at the right moment in time building upon the monitoring of the health status of the concerned systems and their components. As such, it allows identifying incipient faults and forecasting the moment of failure at the earliest stage. Many different data-driven methods are used in such approaches (Naderi and Khorasani in 2017 IEEE 30th Canadian conference on electrical and computer engineering (CCECE), Windsor, ON, IEEE, pp 1–6, 2017. 10.1109/ccece.2017.7946715; Sarkar et al. in J Eng Gas Turbines Power 1338(8):081602, 2011. 10.1115/1.4002877; Svärd et al. in Mech Syst Signal Process 45(1):170–192, 2014. 10.1016/j.ymssp.2013.11.002; Pourbabaee et al. Mech Syst Signal Process 76–77:136–156, 2016. 10.1016/j.ymssp.2016.02.023). This work uses the self-organizing maps (SOMs) or Kohonen map, thanks to its ability to emphasize underlying behavior such as fault modes. An automatic fault mode detection is presented based on a SOM network and the kernel density estimation with as less as possible prior knowledge. The different SOM development steps are presented and the suitable solutions proposed to structure the approach are accompanied by mathematical methods. The generated maps are then used with kernel density analysis to isolate fault modes on them. Finally, a methodology is presented to identify the different fault modes. The work is illustrated with an aircraft jet engines case study. [ABSTRACT FROM AUTHOR]