Gamification in stroke rehabilitation

Stroke has a high incidence in the population and it is one of the leading causes of functional impairments among adults. Brain damage rehabilitation is still a relatively undeveloped field and some research lines are following functional motor recovery. Brain-Computer Interface (BCI) provides new techniques to overcome stroke-related motor impairments. Recent studies present the brain’s capacity in order to promote the brain plasticity. The use of the BCI for rehabilitation tries to foster three mechanisms of neuropsychology that have proven to be of radical impact in brain function recovery: Motor Imagery, Mirror Neuron and Sensoriomotor loop. In this project, we present a pilot study with a rehabilitation session based on BCI system combined with gamification. We try to demonstrate that including gamification in the rehabilitation sessions the performance is the same as the base avatar, but the engagement and entertainment of patients increase. In this pilot study is explained the whole design and development of the gamification session as well as the intervention with real patients

Analysis of gas turbine compressor performance after a major maintenance operation using an autoencoder architecture

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.This research was funded by Siemens Energy.Peer ReviewedPostprint (published version

Machine-learning-based condition assessment of gas turbine: a review

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 machinelearning (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.This research was funded by Siemens Energy.Peer ReviewedPostprint (published version

DA&AI supporting tools for gas turbine’s efficiency improvement: maintenance, operation modes and performance enhancement

(English) Digitalization has revolutionized many industries, including the power generation sector. The availability of a vast amount of data from various systems has transformed decision-making processes in Industry. Advances in artificial intelligence and machine learning have enabled the development of sophisticated algorithms that can process large datasets and uncover patterns and insights that were previously difficult to detect. This thesis is part of a collaborative project between the Universitat Politècnica de Catalunya (UPC) and Siemens Energy (SE), aimed at creating digital tools for monitoring and improving the efficiency of industrial gas turbines used in power generation. The focus of this study is on developing maintenance-related support tools, as it is a key factor in the equipment performance as well as the cost of it is a significant expense for gas turbine operators. Maintenance is a critical process for ensuring the reliability and availability of industrial systems. Key Performance Indicators (KPIs) and soft sensors have become increasingly popular for monitoring industrial processes and predicting variables that are difficult to measure. Therefore, the main goal driving this thesis is to develop an AI-based indicator that can help assess equipment performance and recommend maintenance actions. To achieve this goal, an autoencoder-based architecture is used, incorporating several different structures and two types of autoencoder. These models are tested to determine which performs the best and is most suited to the equipment requirements. A detailed study is presented, evaluating the performance of the models using two different metrics: absolute error and Fréchet distance, combined with two time-averaging calculations: moving average and incremental window average. The results of this analysis reveal a clear drift in the model output. Moreover, further results are obtained by modifying the autoencoder structure which lead to detect significant changes in equipment performance associated with major maintenance events. Time series decomposition, wavelet transform, and clustering methods are used to further analyze the findings and obtain additional insights into gas turbine performance. The outcome derived from this study strengthen the drift detection in gas turbine performance and the identification of significant change in its behavior due to major maintenance events. This research can serve as a foundation for future studies and investigations in this area, as it has laid the groundwork for the potential development of more sophisticated and accurate models that can effectively monitor and diagnose potential issues in Siemens Energy gas turbines. This doctoral thesis contributes to reducing the gap between academia and industry by applying novel technologies and algorithms to real plant problems. It provides valuable insights and understanding of gas turbine systems and presents a framework for developing more accurate and targeted models. By leveraging the power of machine learning and advanced analytics, researchers and industry professionals can work together to improve the efficiency, reliability, and safety of gas turbines in a wide range of industrial applications.(Català) La digitalització ha revolucionat el sector industrial. L'augment en la captura de dades i el desenvolupament en els camps de la intel·ligència artificial i l'aprenetatge automàtic han significat un canvi transformador en la forma com es prenen decisions a la indústria. Aquests avenços han permès el desenvolupament d'algorismes sofisticats que poden processar grans quantitats de dades i descobrir patrons i estructures que abans eren difícils de detectar. Aquesta tesi forma part d'un projecte de col·laboració entre la Universitat Politècnica de Catalunya (UPC) i Siemens Energy (SE), amb la idea de crear eines digitals pel seguiment i la millora de l'eficiència de les turbines de gas en aplicacions industrials. L'objectiu d'aquest estudi es centra en el desenvolupament d'eines de suport relacionades amb el manteniment, ja que és un dels factors clau en el rendiment de l'equipament i el seu cost és una despesa important per als operadors d'aquest sector. El manteniment és un procés crític per garantir la fiabilitat i disponibilitat dels sistemes industrials. Els indicadors (KPI) i els sensors digitals s'han tornat cada cop més populars per monitoritzar processos industrials i predir variables difícils de mesurar. Per tant, l'objectiu principal d'aquesta tesi és desenvolupar un indicador basat en IA que pugui ajudar a avaluar el rendiment dels equips i recomanar accions de manteniment. Per aconseguir aquest objectiu, s'utilitza una arquitectura basada en autoencoder, que incorpora estructures i tipus diversos. Aquests models s'han posat a prova per determinar quin ofereix un millor rendiment i s'adapta bé més als requisits de les màquines. Es presenta un estudi detallat, avaluant el rendiment dels models mitjançant dues mètriques diferents: l'error absolut i la distància de Fréchet, combinats amb dos càlculs de mitjana temporal: mitjana mòbil i mitjana incremental. Els resultats d'aquest anàlisis revelen una clara desviació en els resultats del model. A més, s'obtenen resultats addicionals modificant l'estructura de l'espai latent que permeten detectar canvis significatius en el rendiment de la màquina associats a esdeveniments significatius de manteniment. S'utilitzen mètodes de descomposició de sèries temporals, transformació de wavelet i d'agrupació de dades per analitzar més els resultats i obtenir informació addicional sobre el rendiment de les turbines de gas. El resultat derivat d'aquest estudi consisteix en la detecció d'una desviació en el rendiment de la màquina i la identificació de canvis significatius en el seu comportament a causa d'activitats significatives de manteniment. Aquesta investigació pot servir de base per a futurs estudis i investigacions en aquesta àrea, ja que ha assentat les bases per al desenvolupament potencial de models més sofisticats i precisos que puguin controlar i diagnosticar eficaçment problemes potencials a les turbines de gas de Siemens Energy. Aquesta tesi doctoral contribueix a reduir la diferència entre la investigació i la indústria mitjançant l'aplicació de noves tecnologies i algorismes a problemes industrials reals. També pretén aportar informació valuosa sobre el funcionament dels sistemes de turbines de gas des d'un punt de vista de dades i presenta unes eines per desenvolupar models més precisos i orientats a tasques específiques i necessàries de les turbines de gas. Aprofitant el desenvolupament de l'aprenentatge automàtic i l'anàlisi avançada, es pretén millorar l'eficiència, la fiabilitat i la seguretat d'aquestes màquines per ampli rang d'usos en aplicacions industrials.DOCTORAT EN INTEL·LIGÈNCIA ARTIFICIAL (Pla 2012

Gamification in stroke rehabilitation

Stroke has a high incidence in the population and it is one of the leading causes of functional impairments among adults. Brain damage rehabilitation is still a relatively undeveloped field and some research lines are following functional motor recovery. Brain-Computer Interface (BCI) provides new techniques to overcome stroke-related motor impairments. Recent studies present the brain’s capacity in order to promote the brain plasticity. The use of the BCI for rehabilitation tries to foster three mechanisms of neuropsychology that have proven to be of radical impact in brain function recovery: Motor Imagery, Mirror Neuron and Sensoriomotor loop. In this project, we present a pilot study with a rehabilitation session based on BCI system combined with gamification. We try to demonstrate that including gamification in the rehabilitation sessions the performance is the same as the base avatar, but the engagement and entertainment of patients increase. In this pilot study is explained the whole design and development of the gamification session as well as the intervention with real patients

Analysis of Gas Turbine Compressor Performance after a Major Maintenance Operation Using an Autoencoder Architecture

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

Condition assessment of industrial gas turbine compressor using a drift soft sensor based in autoencoder

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.Peer ReviewedPostprint (published version

Effects of gamification in BCI functional rehabilitation

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.Peer ReviewedPostprint (published version