Reconnaissance des défauts de la machine asynchrone : application des modèles d’intelligence artificielle

Les machines asynchrones sont omniprésentes dans les systèmes de production automatisé à cause de leur robustesse et leur facilitée de mise en oeuvre. Néanmoins, ces moteurs électriques concèdent tout de même des défauts (ex : court-circuit entre spires, barre rotoriques rompues) menant à des arrêts non planifiés. Par conséquent, les industries manufacturières investissent des ressources importantes afin de les éviter avec des programmes de maintenance qui sont partiellement inefficace. C’est dans ce contexte que, depuis plusieurs décennies, des chercheurs proposent des travaux permettant de diagnostiquer l’état des machines asynchrones. Cependant, les solutions ne donnent que très rarement la localisation et l’estimation du degré de sévérité des anomalies qui ne permet pas de prioriser les actions pour l’amélioration de la maintenance. De plus, la majorité des moyens de diagnostic ne sont pas adaptifs à d’autres gammes de moteur et les études ne prennent pas en compte la commande des machines asynchrones pour les applications à vitesse et couple variables. Ainsi, nous proposons dans cette thèse une nouvelle approche pour l’amélioration du processus de maintenance par la reconnaissance des défauts de la machine asynchrone reposant principalement sur l’exploitation des modèles d’intelligence artificielle. Celle-ci permettra de détecter, de localiser et d’estimer le degré de sévérité des anomalies du moteur grâce à ses courants statoriques. La solution donnée dans cet ouvrage est adaptif et surtout a été testé pour une machine possédant une commande et un asservissement de vitesse avec des différents profils de vitesse et couple variables. Pour ce faire, la recherche proposée exploite les modèles mathématiques de la machine asynchrone et de ses défauts afin de simuler les différents comportements de celle-ci. Les simulations serviront à créer des bases de données grâce à l’extraction de caractéristiques issue du traitement des signaux. Chacune des séries de données appartient à une catégorie décrivant le défaut du moteur. Par la suite, des algorithmes de classification permettront de reconnaître les anomalies de la machine asynchrone. Nous présentons également une approche hiérarchique qui améliore le taux de reconnaissance des défectuosités du moteur à induction. Ce projet se situant à la frontière des domaines du génie électrique, du génie informatique et des mathématiques constitue un défi complexe et formidable de recherche scientifique. Induction machines are omnipresent in production systems because of their sturdiness and their ease of implementation. Nevertheless, these electrical motors still concede failures (e.g. inter-turn short circuit, broken rotor bar), which may lead to unplanned shutdowns. Consequently, manufacturing industries invest significant resources to avoid them with maintenance, which is partially inefficient. In this context, some studies propose solutions to abnormal diagnostic conditions of the induction machine. Nevertheless, they rarely localize the defect and estimate the severity of the failure, which does not allow prioritizing action for the maintenance improvement. In addition, solutions are not adaptive for other motors, and studies do not include the control part very useful for speed and torque variable applications. Thus, in this thesis, we propose a new approach improving the maintenance process by the recognition of the induction machine failures. It relies mainly on Artificial Intelligence models and will allow to detect, localize and to estimate the degree of severity of the asynchronous motor faults thanks to the exploitation of current signals. The solution given in this project is adaptive and have been tested for induction machines operating with a speed and drives control. In addition, several speed and resistant torque profiles have been applied. To do this, the research proposed exploits the mathematical models of the induction machine operating under the healthy and faulty conditions. Simulations allow creating some datasets thanks to the feature extractions and the signals processing. Each vector of data belongs to a category describing the failure. Then, classification algorithms will recognize the induction machine defects. We also present a hierarchical approach, which improves the recognition rate. This project being a mix of electrical engineering, informatics and mathematic is a complex and amazing challenge of scientific research

Mineral grains recognition using computer vision and machine learning

Identifying and counting individual mineral grainsc composing sand is an important component of many studies in environment, engineering, mineral exploration, ore processing and the foundation of geometallurgy. Typically, silt (32–128 μm) and sand (128–1000 μm) sized grains will be characterized under an optical microscope or a scanning electron microscope. In both cases, it is a tedious and costly process. Therefore, in this paper, we introduce an original computational approach in order to automate mineral grains recognition from numerical images obtained with a simple optical microscope. To the best of our knowledge, it is the first time that the current computer vision based on machine learning algorithms is tested for the automated recognition of such mineral grains. In more details, this work uses the simple linear iterative clustering segmentation to generate superpixels and many of them allow isolating sand grains, which is not possible with classical segmentation methods. Also, the approach has been tested using convolutional neural networks (CNNs). However, CNNs did not give as good results as the superpixels method. The superpixels are also exploited to extract features related to a sand grain. These image characteristics form the raw dataset. Prior to proceed with the classification, a data cleaning stage is necessary to get a usable dataset for machine learning algorithms. In addition, we present a comparison of performances of several algorithms. The overall obtained results are approximately 90% and demonstrate the concept of mineral recognition from a sample of sand grains provided by a numerical image

Activity recognition in smart homes using UWB radars

In the last decade, smart homes have transitioned from a potential solution for aging-in-place to a real set of technologies being deployed in the real-world. This technological transfer has been mostly supported by simple, commercially available sensors such as passive infrared and electromagnetic contacts. On the other hand, many teams of research claim that the sensing capabilities are still too low to offer accurate, robust health-related monitoring and services. In this paper, we investigate the possibility of using Ultra-wideband (UWB) Doppler radars for the purpose of recognizing the ongoing ADLs in smart homes. Our team found out that with simple configuration and classical features engineering, a small set of UWB radars could reasonably be used to recognize ADLs in a realistic home environment. A dataset was built from 10 persons performing 15 different ADLs in a 40 square meters apartment with movement on the other side of the wall. Random Forest was able to attain 80% accuracy with an F1-Score of 79%, and a Kappa of 77%. Those results indicate the use of Doppler radars can be a good research avenue for smart homes

Basic daily activity recognition with a data glove

Many people in the world are affected by the Alzheimer disease leading to the dysfunctionality of the hand. In one side, this symptom is not the most important of this disease and not much attention is given to this one. In the other side, the literrature provides two main solutions such as computer vision and data glove allowing to recognize hand gestures for virtual reality or robotic applications. From this finding and need, we decided to developed our own data glove prototype allowing to monitor the evolution of the dysfunctionality of the hand by recognizing objects in basic daily activities. Our approach is simple, cheap (~220$) and efficient (~100% of correct predictions) considering that we are abstracting all the theory about the gesture recognition. Also, we can access directly and easily to the raw data. Finally, the proposed prototype is described in a way that researchers can reproduce it

Deep-Learning-Based Automatic Mineral Grain Segmentation and Recognition

A multitude of applications in engineering, ore processing, mineral exploration, and environmental science require grain recognition and the counting of minerals. Typically, this task is performed manually with the drawback of monopolizing both time and resources. Moreover, it requires highly trained personnel with a wealth of knowledge and equipment, such as scanning electron microscopes and optical microscopes. Advances in machine learning and deep learning make it possible to envision the automation of many complex tasks in various fields of science at an accuracy equal to human performance, thereby, avoiding placing human resources into tedious and repetitive tasks, improving time efficiency, and lowering costs. Here, we develop deep-learning algorithms to automate the recognition of minerals directly from the grains captured from optical microscopes. Building upon our previous work and applying state-of-the-art technology, we modify a superpixel segmentation method to prepare data for the deep-learning algorithms. We compare two residual network architectures (ResNet 1 and ResNet 2) for the classification and identification processes. We achieve a validation accuracy of 90.5% using the ResNet 2 architecture with 47 layers. Our approach produces an effective application of deep learning to automate mineral recognition and counting from grains while also achieving a better recognition rate than reported thus far in the literature for this process and other well-known, deep-learning-based models, including AlexNet, GoogleNet, and LeNet

Harmonization and standardization of nucleus pulposus cell extraction and culture methods

Background: In vitro studies using nucleus pulposus (NP) cells are commonly used to investigate disc cell biology and pathogenesis, or to aid in the development of new therapies. However, lab‐to‐lab variability jeopardizes the much‐needed progress in the field. Here, an international group of spine scientists collaborated to standardize extraction and expansion techniques for NP cells to reduce variability, improve comparability between labs and improve utilization of funding and resources. Methods: The most commonly applied methods for NP cell extraction, expansion, and re‐differentiation were identified using a questionnaire to research groups worldwide. NP cell extraction methods from rat, rabbit, pig, dog, cow, and human NP tissue were experimentally assessed. Expansion and re‐differentiation media and techniques were also investigated. Results: Recommended protocols are provided for extraction, expansion, and re‐differentiation of NP cells from common species utilized for NP cell culture. Conclusions: This international, multilab and multispecies study identified cell extraction methods for greater cell yield and fewer gene expression changes by applying species‐specific pronase usage, 60–100 U/ml collagenase for shorter durations. Recommendations for NP cell expansion, passage number, and many factors driving successful cell culture in different species are also addressed to support harmonization, rigor, and cross‐lab comparisons on NP cells worldwide

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P < 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men

Aging, physical activity and postural control : Analysis of the interaction through the use of multiple and combined sensory manipulations

L’objectif général de ce travail doctoral était de étudier la résultante entre les bénéfices induits par l’activité physique chronique et les effets délétères de l’avancée en âge sur la fonction d’équilibration. Pour cela, différentes techniques de manipulations sensorielles (e.g. stimulation vestibulaire galvanique, vibration tendineuse, électromyostimulation, tapis de mousse) ont été employées dans le cadre de tâches posturales bipodales. Les principaux résultats montrent que les manipulations sensorielles affectent le contrôle postural quel que soit l’âge et le niveau de pratique physique du sujet. Par ailleurs, l’avancée en âge semble majorer les effets perturbateurs des manipulations sensorielles. Le comportement postural observé pourrait, en grande partie, résulter d’une dégradation de la proprioception. Lorsque l’information proprioceptive est manipulée (i.e. vibration tendineuse), le groupe de sujets âgés ne pratiquant aucune activité physique saturerait plus rapidement le système proprioceptif que les autre groupes de sujets. En revanche, l’activité physique chronique limiterait la dégradation de la capacité d’équilibration. Elle pourrait améliorer la capacité des sujets âgés à recalibrer l’information sensorielle erronée et renforcerait l’efficacité de la proprioception. En filigrane, une optimisation fonctionnelle du système postural permettrait de compenser partiellement les effets du vieillissement. En effet, les involutions qui s’opèrent au cours de l’avancée en âge au niveau des systèmes sensoriels et du système nerveux central s’avèrent inéluctables. Elles empêchent les sujets âgés pratiquant une activité physique régulière de maintenir une habileté à compenser une perturbation posturale similaire à celle de sujets jeunes sportifs.The overall objective of this thesis was to analyse the benefits resulting from the chronic physical activity and the deleterious effects induced by aging on postural control. To this end, different sensory manipulation techniques (e.g. vestibular galvanic stimulation, tendon vibration, electromyostimulation, foam surface) were used in the context of bipedal postural tasks. The main results showed that sensory manipulations affect postural control whatever the age and the level of physical practice of the subject. In addition, the disruptive effects of the sensory manipulations on postural control seem to increase with aging. This postural behaviour could largely result from the involution of the proprioception. When proprioception is disrupted (i.e. tendon vibration), the non-active old subjects group would saturate the proprioceptive system more quickly than the other groups. In contrast, the chronic physical activity would limit the involution of the postural control effectiveness. It could improve the ability of the old subjects to reweight sensory information and enhance the proprioception effectiveness. Hence, a functional postural control optimization might partly compensate the aging effects. Indeed, age-related involutions of sensory systems and central nervous system occurring across life span are inevitable. They prevent the older subjects who practice regular physical activity to maintain a similar ability to cope with postural disruptions in comparison with young athletes

Détection et analyse des signaux faibles. Développement d’un framework d’investigation numérique pour un service caché Lanceurs d’alerte

This manuscript provides the basis for a complete chain of document analysis for a whistleblower service, such as GlobalLeaks. We propose a chain of semi-automated analysis of text document and search using websearch queries to in fine present dashboards describing weak signals. We identify and solve methodological and technological barriers inherent to : 1) automated analysis of text document with minimum a priori information,2) enrichment of information using web search 3) data visualization dashboard and 3D interactive environment. These static and dynamic approaches are used in the context of data journalism for processing heterogeneous types of information within documents. This thesis also proposed a feasibility study and prototyping by the implementation of a processing chain in the form of a software. This construction requires a weak signal definition. Our goal is to provide configurable and generic tool. Our solution is based on two approaches : static and dynamic. In the static approach, we propose a solution requiring less intervention from the domain expert. In this context, we propose a new approach of multi-leveltopic modeling. This joint approach combines topic modeling, word embedding and an algorithm. The use of a expert helps to assess the relevance of the results and to identify topics with weak signals. In the dynamic approach, we integrate a solution for monitoring weak signals and we follow up to study their evolution. Wetherefore propose and agent mining solution which combines data mining and multi-agent system where agents representing documents and words are animated by attraction/repulsion forces. The results are presented in a data visualization dashboard and a 3D interactive environment in Unity. First, the static approach is evaluated in a proof-of-concept with synthetic and real text corpus. Second, the complete chain of document analysis (static and dynamic) is implemented in a software and are applied to data from document databases.Ce manuscrit s’inscrit dans le cadre du développement d’une plateforme d’analyse automatique de documents associée à un service sécurisé lanceurs d’alerte, de type GlobalLeaks. Nous proposons une chaine d’extraction à partir de corpus de document, d’analyse semi-automatisée et de recherche au moyen de requêtes Web pour in fine, proposer des tableaux de bord décrivant les signaux faibles potentiels. Nous identifions et levons un certain nombre de verrous méthodologiques et technologiques inhérents : 1) à l’analyse automatique de contenus textuels avec un minimum d’a priori, 2) à l’enrichissement de l’information à partir de recherches Web 3) à la visualisation sous forme de tableau de bord et d’une représentation dans un espace 3D interactif. Ces approches, statique et dynamique, sont appliquées au contexte du data journalisme, et en particulier, au traitement, analyse et hiérarchisation d’informations hétérogènes présentes dans des documents. Cette thèse propose également une étude de faisabilité et de prototypage par la mise en œuvre d’une chaine de traitement sous forme d’un logiciel. La construction de celui-ci a nécessité la caractérisation d’un signal faible pour lequel nous avons proposé une définition. Notre objectif est de fournir un outil paramétrable et générique à toute thématique. La solution que nous proposons repose sur deux approches : statique et dynamique. Dans l’approche statique, contrairement aux approches existantes nécessitant la connaissance de termes pertinents dans un domaine spécifique, nous proposons une solution s’appuyant sur des techniques nécessitant une intervention moindre de l’expert du domaine. Dans ce contexte, nous proposons une nouvelle approche de modélisation thématique multi-niveaux. Cette méthode d’approche conjointe combine une modélisation thématique, un plongement de mots et un algorithme où le recours à un expert du domaine permet d’évaluer la pertinence des résultats et d’identifier les thèmes porteurs de signaux faibles potentiels. Dans l’approche dynamique, nous intégrons une solution de veille à partir des signaux faibles potentiels trouvées dans les corpus initiaux et effectuons un suivi pour étudier leur évolution. Nous proposons donc une solution d’agent mining combinant data mining et système multi-agents où des agents animés par des forces d’attraction/répulsion représentant documents et mots se déplacent. La visualisation des résultats est réalisée sous forme de tableau de bord et de représentation dans un espace 3D interactif dans unclient Unity. Dans un premier temps, l’approche statique a été évaluée dans une preuve de concept sur des corpus synthétiques et réelles utilisés comme vérité terrain. L’ensemble de la chaine de traitement (approches statique et dynamique), mise en œuvre dans le logiciel WILD, est dans un deuxième temps appliquée sur des données réelles provenant de bases documentaires