Machine Learning for Data-driven Prognostics : Methods and Applications

Abstract

Knowledge extraction from monitoring sensor data has gained a lot of attention from many fields of research during recent years. Artificial intelligence, machine learning, advanced statistics, the Internet of things and architectures and strategies for optimal big data management are good examples of such interest. This is mainly due to the increase in the amount of data available and in the storage and speed capabilities of actual computing systems. The main motivation of this research is on providing automatic behavior modeling and intelligent decision-making strategies to prevent critical events and damages, which can imply important loss of money and safety issues.The research activity performed also explores the difficulties derived from data modeling in several industrial sectors, figuring out specific needs and requirements. The reliability of complex assets and equipment is crucial to minimize faults and the related negative impact in terms of money and time loss, to mitigate potential risks and to successfully accomplish the task. Proactive maintenance is especially relevant to this concern. It is executed on the basis of corrective and predictive techniques that allow obtaining a diagnosis, and even anticipating potential failures and events of interest.This PhD dissertation is also focused on the development of the Diagnosis and Impact Model 4.0, which is motivated by the fourth industrial revolution from a data science perspective. The underlying idea is to apply Machine Learning paradigms for data-driven behavior modeling and prognostics in complex systems, from imagination and innovation to real impact. Several successful cases are shown in this dissertation, covering a wide variety of challenging scenarios and assets, and targeting important industrial sectors such as maritime, renewable energy, railway, agro-food, civil structures and machine-tool. ResumenEn losúltimos años la extracción y generación de nuevo conocimiento a partir de datos ha experimentado un creciente interés por parte de la comunidad científica. La inteligencia artificial, el aprendizaje automático, la estadística avanzada, el Internet de las cosas y la gestión inteligente de grandes volúmenes de información son ejemplos representativos de este creciente interés. Todo ello viene motivado por un incremento exponencial en la cantidad de datos disponibles y en las capacidades de almacenaje y velocidad de cómputo de los sistemas de procesamiento actuales. En este contexto la motivación principal para la elaboracin de esta tesis doctoral consiste en modelar comportamientos de interés a partir de datos de manera automática, y proveer estrategiasóptimas que permitan anticipar fallos y eventos críticos.La actividad investigadora realizada también explora las dificultades derivadas de la generación de modelos basados en datos en varios sectores industriales, teniendo en cuenta las necesidades y los requisitos específicos de cada sector. La fiabilidad de los activos y equipos es clave a la hora de minimizar la aparición de fallos...

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