In the hope to increase the detection rate of faults in combined heat and power plant boilers thus lowering unplanned maintenance three machine learning models are constructed and evaluated. The algorithms; k-Nearest Neighbor, One-Class Support Vector Machine, and Auto-encoder have a proven track record in research for anomaly detection, but are relatively unexplored for industrial applications such as this one due to the difficulty in collecting non-artificial labeled data in the field.The baseline versions of the k-Nearest Neighbor and Auto-encoder performed very similarly. Nevertheless, the Auto-encoder was slightly better and reached an area under the precision-recall curve (AUPRC) of 0.966 and 0.615 on the trainingand test period, respectively. However, no sufficiently good results were reached with the One-Class Support Vector Machine. The Auto-encoder was made more sophisticated to see how much performance could be increased. It was found that the AUPRC could be increased to 0.987 and 0.801 on the trainingand test period, respectively. Additionally, the model was able to detect and generate one alarm for each incident period that occurred under the test period.The conclusion is that ML can successfully be utilized to detect faults at an earlier stage and potentially circumvent otherwise costly unplanned maintenance. Nevertheless, there is still a lot of room for improvements in the model and the collection of the data. / I hopp om att öka identifieringsgraden av störningar i kraftvärmepannor och därigenom minska oplanerat underhåll konstrueras och evalueras tre maskininlärningsmodeller.Algoritmerna; k-Nearest Neighbor, One-Class Support Vector Machine, och Autoencoder har bevisad framgång inom forskning av anomalidetektion, men är relativt outforskade för industriella applikationer som denna på grund av svårigheten att samla in icke-artificiell uppmärkt data inom området.Grundversionerna av k-Nearest Neighbor och Auto-encoder presterade nästan likvärdigt. Dock var Auto-encoder-modellen lite bättre och nådde ett AUPRC-värde av 0.966 respektive 0.615 på träningsoch testperioden. Inget tillräckligt bra resultat nåddes med One-Class Support Vector Machine. Auto-encoder-modellen gjordes mer sofistikerad för att se hur mycket prestandan kunde ökas. Det visade sig att AUPRC-värdet kunde ökas till 0.987 respektive 0.801 under träningsoch testperioden. Dessutom lyckades modellen identifiera och generera ett larm vardera för alla incidenter under testperioden. Slutsatsen är att ML framgångsrikt kan användas för att identifiera störningar iett tidigare skede och därigenom potentiellt kringgå i annat fall dyra oplanerade underhåll. Emellertid finns det fortfarande mycket utrymme för förbättringar av modellen samt inom insamlingen av data.

Pour maintenir leur compétitivité, les industries du semi-conducteur doivent être en mesure de produire des circuits intégrés en technologies avancées, avec des temps de cycle de plus en plus courts et à des coûts raisonnables. Un des axes d’amélioration réside dans le traitement des défaillances des équipements de production tenus responsables de plus de 50%des rejets produits. Cette thèse se fixe comme objectif de contribuer au développement d’une boucle réactive partant d’une dérive produit à la mise en place d’une solution appropriée tout en assurant un meilleur compromis entre disponibilité des équipements, coûts d’exploitation, qualité et compétitivité du produit. Joignant l’expertise humaine et les événements réels, nous nous sommes proposé ici de développer une méthodologie générique permettant de construire un modèle d’estimation du comportement des équipements de production (Equipment Health Factor EHF) à partir d’un raisonnement mathématique centré sur un formalisme probabiliste. L’approche a été amenée à sa validation expérimentale sur des outils, à base de réseaux Bayésiens, que nous avons développés. Les résultats obtenus amènent des éléments de décision permettant à l’industriel d’intervenir au plus tôt pour envisager par exemple de maintenir l’équipement avant qu’il n’ait dérivé. Cette thèse a été préparée dans le cadre du projet européen IMPROVE en collaboration avec STMicroelectronics, Lfoundry et Probayes / Today, the semiconductor industry must be able to produce Integrated Circuit (IC) withreduced cycle time, improved yield and enhanced equipment effectiveness. Besides thesechallenges IC manufacturers are required to address the products scrap and equipment driftsin a complex and uncertain environment which otherwise shall severely hamper the maximumproduction capacity planned. The objective of this thesis is to propose a generic methodologyto develop a model to predict the Equipment Health Factor (EHF) which will define decisionsupport strategies on maintenance tasks to increase the semiconductor industry performance.So, we are interested here to the problem of equipment failures and drift. We propose apredictive approach based on Bayesian technique allowing intervene early to maintain, forexample, the equipment before its drift. The study presented in this thesis is supported by theIMPROVE European project

Recent years have seen an influx of medical technologies capable of remotely monitoring the health and behaviours of individuals to detect, manage and prevent health problems. Known collectively as personal health monitoring (PHM), these systems are intended to supplement medical care with health monitoring outside traditional care environments such as hospitals, ranging in complexity from mobile devices to complex networks of sensors measuring physiological parameters and behaviours. This research project assesses the potential ethical implications of PHM as an emerging medical technology, amenable to anticipatory action intended to prevent or mitigate problematic ethical issues in the future. PHM fundamentally changes how medical care can be delivered: patients can be monitored and consulted at a distance, eliminating opportunities for face-to-face actions and potentially undermining the importance of social, emotional and psychological aspects of medical care. The norms evident in this movement may clash with existing standards of 'good' medical practice from the perspective of patients, clinicians and institutions. By relating utilitarianism, virtue ethics and theories of surveillance to Habermas' concept of colonisation of the lifeworld, a conceptual framework is created which can explain how PHM may be allowed to change medicine as a practice in an ethically problematic way. The framework relates the inhibition of virtuous behaviour among practitioners of medicine, understood as a moral practice, to the movement in medicine towards remote monitoring. To assess the explanatory power of the conceptual framework and expand its borders, a qualitative interview empirical study with potential users of PHM in England is carried out. Recognising that the inherent uncertainty of the future undermines the validity of empirical research, a novel epistemological framework based in Habermas' discourse ethics is created to justify the empirical study. By developing Habermas' concept of translation into a procedure for assessing the credibility of uncertain normative claims about the future, a novel methodology for empirical ethical assessment of emerging technologies is created and tested. Various methods of analysis are employed, including review of academic discourses, empirical and theoretical analyses of the moral potential of PHM. Recommendations are made concerning ethical issues in the deployment and design of PHM systems, analysis and application of PHM data, and the shortcomings of existing research and protection mechanisms in responding to potential ethical implications of the technology.