The path towards increasing RAMS for novel complex missions based on CubeSat technology

The paper presents the initial outcomes of a project, currently ongoing under the supervision of the European Space Agency, having the main objective to specify and design a Fault Detection Isolation and Recovery (FDIR) system by making use of relevant RAMS (Reliability, Availability, Maintainability, Safety) analyses for missions in non-deterministic environment with limited resources. The initial project tasks have been to select a study case represented by a CubeSat complex mission, analyse in detail both its mission and system requirements and, based on them, define a set of relevant RAMS analyses to be carried out in the second phase of the project, as inputs for the development of a FDIR concept aimed at a careful balance of the limited spacecraft resources in case of critical failures. Two possible study cases have been identified: LUMIO, a 12U CubeSat mission for the observation of micro-meteoroid impacts on the Lunar farside, and M-ARGO, a 12U deep-space CubeSat which will rendezvous with a near-Earth asteroid and characterize its physical properties for the presence of in-situ resources. Although both missions are characterized by a high level of autonomy and complexity in a harsh environment, LUMIO has been eventually selected as study case for the project. In the paper, the challenges and features of this mission are shortly presented. The specificities of the RAMS analysis and FDIR concept for this specific class of small satellite missions (including the selected study case) are highlighted in the paper, looking in particular at aspects such as the improvement of reliability while maintaining the CubeSat philosophy, the tuning of mission and system requirements in view of facilitating the design and implementation of the FDIR concept, and the current gaps within the RAMS/FDIR body of knowledge. The conclusions drawn during this first project phase provide a real view of how systems engineering must work in tandem with RAMS analyses and FDIR to achieve a more robust and functional mission architecture, thus improving the mission reliability

Exploiting Virtual Reality to Design Exercises for the Recovery of Stroke Patients at Home

Stroke affects approximately fifteen million people worldwide annually, with im- paired hand function being one of its most common effects. Hemiparetic post-stroke patients suffer a mild loss of strength involving one side of their body: though not fully debilitating, it still impacts their everyday life activities. To prevent mobility deterioration, patients must perform well-focused and repetitive exercises during chronic rehabilitation. Virtual Reality (VR) emerges as an interesting tool in this framework, offering the possibility of training and measuring the patient’s performances in ecologically valid, engaging, and challenging environments. In recent years, there has been an increasing diffusion of accessible head-mounted displays that enhance the sense of realism and immersion in a virtual scene. To explore the feasibility and efficacy of VR immersion and game mechanics in rehabilitation programs, a VR system that allows users to rehabilitate their motor skills in a home-based environment has been designed and tested considering standard measures related to usability, immersion, workload, and simulator sickness, and with the involvement of rehabilitation experts. The results demonstrate how users and experts have received the application positively, highlighting the potential of VR applications for the future development of home-based rehabilitation programs

Digital Twin for Factories: Challenges and Industrial Applications

The widespread adoption of digital technologies in factories has resulted in the generation of a vast amount of data, which has the potential to enhance efficiency and effectiveness in the manufacturing industry. However, collecting and analyzing these data require approaches and tools to design and operate complex digital models and infrastructures, also requiring transversal competencies. The Digital Twin approach can be exploited to couple assets with their digital counterparts to support analyses and decisions. In particular, a Digital Twin can be associated with a product, a specific machine tool or process, a production system, or an entire factory. This paper focuses on the application of Digital Twins in factories, proposing a framework to identify data flows and relevant digital tools for applications throughout the different phases of the factory lifecycle. Despite the great potential, Digital Twin in manufacturing is still hindered by certain limitations. Therefore, by drawing upon relevant literature, we define and highlight the key challenges that need to be addressed. Finally, the framework and the challenges are exploited to characterize three case studies, which demonstrate the application of Digital Twins during the design and execution phases

Attractors for a fluid-structure interaction problem in a time-dependent phase space

This paper is concerned with the long-time dynamics of a fluid-structure interaction problem describing a Poiseuille inflow through a 2D channel containing a rectangular obstacle. Physically, this models the interaction between the wind and the deck of a bridge in a wind tunnel experiment, as time goes to infinity. Due to this interaction, the fluid domain depends on time in an unknown fashion and the problem needs a delicate functional analytic setting. As a result, the solution operator associated to the system acts on a time- dependent phase space, and it cannot be described in terms of a semigroup nor of a process. Nonetheless, we are able to extend the notion of global attractor to this particular setting, and prove its existence and regularity. This provides a strong characterization of the asymptotic behavior of the problem. Moreover, when the inflow is sufficiently small, the attractor reduces to the unique stationary solution of the system, corresponding to a perfectly symmetric configuration

Hybrid prognostics to estimate cutting inserts remaining useful life based on direct wear observation

The aim of this study is to monitor tool wear through recurrent direct observation, to automatically and optimally assess when it is really necessary to change tools. To achieve this goal, a hybrid prognosis algorithm is formulated to estimate cutting tools’ remaining useful life. Since cutting speed, and more generally process parameters, influences the rate of tool degradation, an adaptive prognosis strategy is presented on the basis of flank wear measurements through the application of a Particle Filter (PF) framework. The adaptability feature allows tracking changes in flank wear evolution. The idea is to fit available degradation curves of cutting tool flank land measurements, through the use of data-driven models, i.e. Multi-Layer Perceptrons (MLP) and cubic polynomials (P3). The Remaining Useful Life of the cutting tool is estimated together with its probability density function, by using a PF framework to adapt MLP weights (or P3 coefficients) along with online flank wear measurements. The devised algorithm was proven to adapt to wear trends from the field, obtained with cutting parameters not previously tested, making it suitable for a robust implementation. The approach was tested when trained upon a single run-to-failure, and validated upon four run-to-failures in different cutting conditions according to a cross-validation inspired technique. P3 was found to be more reliable (from the metrics perspective), whereas MLP allowed to be accurate with greater advance, offering a practical advantage. The proposed algorithm may also be adapted to integrate physical features, like specific force coefficients, with direct wear measurements

Special issue on artificial intelligence in thermal engineering systems

The special issue “AI in Thermal Engineering” covers the most recent studies with a focus on the applications of artificial intelligence (AI) technologies in thermal engineering systems. The overall aim is to report the latest advances of research and development, discuss the pros and cons, and explore the future perspectives on the synergy of AI and thermal engineering. Articles reporting original research contributions and critical reviews on adopting AI to address engineering problems of modeling, prediction, control, optimization, performance assessment, diagnosis of thermal engineering devices, components and systems are welcome. Special focus is given to those problems which have not been adequately addressed by adopting traditional methods due to limited knowledge and information, computation efficiency, capability of generalization and adaptation in applications, etc. The targeted engineering systems include energy storage devices, power plants, heat pumps and cooling or refrigeration plants, combined heat and power plants, buildings and district energy systems, renewable and clean energy systems, and other engineering systems involving thermal engineering processes. The special issue has received over 30 submissions, and a total of 10 technical papers are selected for publication which cover a broad range of applications including building energy systems, thermal power units, vehicles, and heat transfer processes. These papers addressed challenges and research gaps in adopting AI in real applications, such as model development and adaption, model interpretability, data imbalance, missing data imputation, etc

A 66.7fs-Integrated-Jitter Fractional-N Digital PLL Based on a Resistive-Inverse-Constant-Slope DTC

Modern fractional-N PLLs used as low-jitter local oscillators for wireless systems generally adopt a digital-to-time converter (DTC) to cancel-out the quantization-error (QE) induced by dithering the modulus control of the frequency divider in feedback [1], [2], [4], [5]. Unfortunately, DTC non-linearity distorts the QE sequence fed to DTC input, thus causing significant fractional spurs at the PLL output and limiting spectral purity and jitter (Fig. 1 top). The inverse-constant-slope DTC (ICS-DTC), recently introduced in [1], has improved linearity over prior-art DTC architectures; however, this comes at the price of a larger DTC jitter, caused by the current generators (CGs) adopted in that circuit. This work introduces an 8.75-10.25GHz fractional-N digital PLL leveraging a resistor-based ICS-DTC circuit, which significantly improves phase-noise while retaining high-linearity. The implemented PLL prototype achieves 66.7fs rms jitter (including spurs), -63.8dBc fractional spur and - 108.5dBc/Hz in-band phase noise (PN) at 10kHz offset, using a 125MHz reference frequency