Memoria para optar al título de Ingeniero Civil Mecánico / Con el uso de las redes neuronal profundas ganando terreno en el área de PHM, los sensores disminuyendo progresivamente su precio y mejores algoritmos, la falta de datos se ha vuelto un problema principal para los modelos enfocados en datos. Los datos etiquetados y aplicables a escenarios específicos son, en el mejor de los casos, escasos. El objetivo de este trabajo es desarrollar un método para diagnosticas el estado de un rodamiento en situaciones con datos limitados. Hoy en día la mayoría de las técnicas se enfocan en mejorar la precisión del diagnóstico y en estimar la vida útil remanente en componentes bien documentados. En el presente, los métodos actuales son ineficiente en escenarios con datos limitados. Se desarrolló un método en el cual las señales vibratorias son usadas para crear escalogramas y espectrogramas, los cuales a su vez se usan para entrenar redes neuronales generativas y de clasificación, en función de diagnosticar un set de datos parcial o totalmente desconocido, en base a uno conocido. Los resultados se comparan con un método más sencillo en el cual la red para clasificación es entrenada con el set de datos conocidos y usada directamente para diagnosticar el set de datos desconocido. El Case Western Reserve University Bearing Dataset y el Machine Failure Prevention Technology Bearing Dataset fueron usados como datos de entrada. Ambos sets se usaron como conocidos tanto como desconocidos. Para la clasificación una red neuronal convolucional (CNN por sus siglas en inglés) fue diseñada. Una red adversaria generativa (GAN por sus siglas en inglés) fue usada como red generativa. Esta red fue basada en una introducida en el paper StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation. Los resultados fueron favorables para la red CNN mientras que fueron -en general- desfavorables para la red GAN. El análisis de resultados sugiere que la función de costo es inapropiada para el problema propuesto. Las conclusiones dictaminan que la traducción imagen-a-imagen basada en la función ciclo no funciona correctamente en señal vibratorias para diagnóstico de rodamientos. With the use of deep neural networks gaining notoriety on the prognostics & health management field, sensors getting progressively cheaper and improved algorithms, the lack of data has become a major issue for data-driven models. Data which is labelled and applicable for specific scenarios is scarce at best. The purpose of this works is to develop a method to diagnose the health state of a bearing on limited data situations. Now a days most techniques focus on improving accuracy for diagnosis and estimating remaining useful life on well documented components. As it stands, current methods are ineffective on limited data scenarios. A method was developed were in vibration signals are used to create scalograms and spectrograms, which in turn are used to train generative and classification neural networks with the goal of diagnosing a partially or totally unknown dataset based on a fully labelled one. Results were compared to a simpler method in which a classification network is trained on the labelled dataset to diagnose the unknown dataset. As inputs the Case Western Reserve University Bearing Dataset (CWR) and the Society for Machine Failure Prevention Technology Bearing Dataset. Both datasets are used as labelled and unknown. For classification a Convolutional Neural Network (CNN) is designed. A Generative Adversarial Network (GAN) is used as generative model. The generative model is based of a previous paper called StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation. Results were favourable for the CNN network whilst generally negative for the GAN network. Result analysis suggests that the cost function is unsuitable for the proposed problem. Conclusions state that cycle based image-to-image translation does not work correctly on vibration signals for bearing diagnosis.

In this thesis the algorithms and strategies for active magnetic bearing should be analysed, implemented and simulated in Matlab as well as experimentally tested in the real-time computation system for a prototype of active magnetic bearing. Develop a general method and algorithm identi cation for active magnetic bearings prototype and get real system parameters that allow generate the equation of state of the system to control its further development. The specific objectives in this Thesis are: Develop a data acquisition system for the AMBs. Analyse the mathematical model of the system from the real system. Conduct experiments of a physical model for data collection. Develop an identification algorithm for the parameters of the real AMBs. Validate system developed by testing the prototype. / Tesis

In Peru, mining is one of the main drivers behind most developments in engineering, an area of high dynamic forces and high pollution. As an illustration, shovel excavators transport tons of sediment each day while cylinder screens sort the stones by size. Typically, ball or cylindrical bearings support these screens and companies, such as SKF, replace the bearings in regular intervals. Thus, the idea is to implement a system, which can dynamically adapt to loads and dynamical load changes to reduce maintenance intervals and vibrations. Ultimately, for a prototype this thesis presents two design proposals of a homopolar, hybrid active magnetic bearing. Moreover, the selection of an inductive type sensor should represent an adequate solution for determining the shaft position. / In Peru ist der Bergbau einer der Haupttreiber für die meisten Entwicklungen im Ingenieurwesen, einem Bereich mit hohen dynamischen Kräften und hoher Verschmutzung. Beispielsweise transportieren Schaufelbagger täglich Tonnen von Sedimenten, während Zylindersiebe die Steine nach Größe sortieren. Typischerweise werden diese Siebe von Kugel- oder Zylinderlager getragen, welche von Unternehmen wie SKF in regelmäßigen Abständen ausgetauscht werden. Die Idee ist also, ein System zu implementieren das sich dynamisch an Lasten und dynamische Laständerungen anpassen kann, um Wartungsintervalle und Vibrationen zu reduzieren. Letztendlich stellt diese Arbeit zwei Entwürfe für einen Prototyp eines homopolaren, hybriden aktiven Magnetlagers vor. Darüber hinaus soll die Auswahl eines induktiven Sensors eine geeignete Lösung zur Bestimmung der Wellenposition darstellen. / Tesis

In this thesis the algorithms and strategies for active magnetic bearing should be analysed, implemented and simulated in Matlab as well as experimentally tested in the real-time computation system for a prototype of active magnetic bearing. Develop a general method and algorithm identi cation for active magnetic bearings prototype and get real system parameters that allow generate the equation of state of the system to control its further development. The specific objectives in this Thesis are: Develop a data acquisition system for the AMBs. Analyse the mathematical model of the system from the real system. Conduct experiments of a physical model for data collection. Develop an identification algorithm for the parameters of the real AMBs. Validate system developed by testing the prototype. / Tesis