Objective: To evaluate whether introducing gamification in BCI rehabilitation of the upper limbs of post-stroke patients has a positive impact on their experience without altering their efficacy in creating motor mental images (MI). Design: A game was designed purposely adapted to the pace and goals of an established BCI-rehabilitation protocol. Rehabilitation was based on a double feedback: functional electrostimulation and animation of a virtual avatar of the patient's limbs. The game introduced a narrative on top of this visual feedback with an external goal to achieve (protecting bits of cheese from a rat character). A pilot study was performed with 10 patients and a control group of six volunteers. Two rehabilitation sessions were done, each made up of one stage of calibration and two training stages, some stages with the game and others without. The accuracy of the classification computed was taken as a measure to compare the efficacy of MI. Users' opinions were gathered through a questionnaire. No potentially identifiable human images or data are presented in this study. Results: The gamified rehabilitation presented in the pilot study does not impact on the efficacy of MI, but it improves users experience making it more fun. Conclusion: These preliminary results are encouraging to continue investigating how game narratives can be introduced in BCI rehabilitation to make it more gratifying and engaging.
Condition monitoring, diagnostics, and prognostics are key factors in today’s competitive industrial sector. Equipment digitalisation has increased the amount of available data throughout the industrial process, and the development of new and more advanced techniques has significantly improved the performance of industrial machines. This publication focuses on surveying the last decade of evolution of condition monitoring, diagnostic, and prognostic techniques using machine-learning (ML)-based models for the improvement of the operational performance of gas turbines. A comprehensive review of the literature led to a performance assessment of ML models and their applications to gas turbines, as well as a discussion of the major challenges and opportunities for the research on these kind of engines. This paper further concludes that the combination of the available information captured through the collectors and the ML techniques shows promising results in increasing the accuracy, robustness, precision, and generalisation of industrial gas turbine equipment.
Maintenance is the process of preserving the good condition of a system to ensure its reliability and availability to perform specific operations. The way maintenance is nowadays performed in industry is changing thanks to the increasing availability of data and condition assessment methods. Soft sensors have been widely used over last years to monitor industrial processes and to predict process variables that are difficult to measured. The main objective of this study is to monitor and evaluate the condition of the compressor in a particular industrial gas turbine by developing a soft sensor following an autoencoder architecture. The data used to monitor and analyze its condition were captured by several sensors located along the compressor for around five years. The condition assessment of an industrial gas turbine compressor reveals significant changes over time, as well as a drift in its performance. These results lead to a qualitative indicator of the compressor behavior in long-term performance.
Machine learning algorithms and the increasing availability of data have radically changed the way how decisions are made in today’s Industry. A wide range of algorithms are being used to monitor industrial processes and predict process variables that are difficult to be measured. Maintenance operations are mandatory to tackle in all industrial equipment. It is well known that a huge amount of money is invested in operational and maintenance actions in industrial gas turbines (IGTs). In this paper, two variations of autoencoders were used to analyse the performance of an IGT after major maintenance. The data used to analyse IGT conditions were ambient factors, and measurements were performed using several sensors located along the compressor. The condition assessment of the industrial gas turbine compressor revealed significant changes in its operation point after major maintenance; thus, this indicates the need to update the internal operating models to suit the new operational mode as well as the effectiveness of autoencoder-based models in feature extraction. Even though the processing performance was not compromised, the results showed how this autoencoder approach can help to define an indicator of the compressor behaviour in long-term performance.
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