Photonic Low Cost Micro-Sensor for in-Line Wear Particle Detection in Flowing Lube Oils

The presence of microscopic particles in suspension in industrial fluids is often an early warning of latent or imminent failures in the equipment or processes where they are being used. This manuscript describes work undertaken to integrate different photonic principles with a micro- mechanical fluidic structure and an embedded processor to develop a fully autonomous wear debris sensor for in-line monitoring of industrial fluids. Lens-less microscopy, stroboscopic illumination, a CMOS imager and embedded machine vision technologies have been merged to develop a sensor solution that is able to detect and quantify the number and size of micrometric particles suspended in a continuous flow of a fluid. A laboratory test-bench has been arranged for setting up the configuration of the optical components targeting a static oil sample and then a sensor prototype has been developed for migrating the measurement principles to real conditions in terms of operating pressure and flow rate of the oil. Imaging performance is quantified using micro calibrated samples, as well as by measuring real used lubricated oils. Sampling a large fluid volume with a decent 2D spatial resolution, this photonic micro sensor offers a powerful tool at very low cost and compacted size for in-line wear debris monitoring.This work has been funded in part by the Fondo Europeo de Desarrollo Regional (FEDER); by the Ministerio de Economia y Competitividad under project TEC2015-638263-C03-1-R; by the Gobierno Vasco/Eusko Jaurlaritza under projects IT933-16 and ELKARTEK (KK-2016/0030 and KK-2016/0059

On the use of context information for an improved application of data-based algorithms in condition monitoring

xi, 124 p.En el campo de la monitorización de la condición, los algoritmos basados en datos cuentan con un amplio recorrido. Desde el uso de los gráficos de control de calidad que se llevan empleando durante casi un siglo a técnicas de mayor complejidad como las redes neuronales o máquinas de soporte vectorial que se emplean para detección, diagnóstico y estimación de vida remanente de los equipos. Sin embargo, la puesta en producción de los algoritmos de monitorización requiere de un estudio exhaustivo de un factor que es a menudo obviado por otros trabajos de la literatura: el contexto. El contexto, que en este trabajo es considerado como el conjunto de factores que influencian la monitorización de un bien, tiene un gran impacto en la algoritmia de monitorización y su aplicación final. Por este motivo, es el objeto de estudio de esta tesis en la que se han analizado tres casos de uso. Se ha profundizado en sus respectivos contextos, tratando de generalizar a la problemática habitual en la monitorización de maquinaria industrial, y se ha abordado dicha problemática de monitorización de forma que solucionen el contexto en lugar de cada caso de uso. Así, el conocimiento adquirido durante el desarrollo de las soluciones puede ser transferido a otros casos de uso que cuenten con contextos similares

Photonic low-cost sensors for in-line fluid monitoring. Design methodology

779 p.The paradigm of process monitoring has evolved in the last years, driven by a clear need for improving efficiency, quality and safety of processes and products. Sectors as manufacturing, energy, food and beverages, etc. are fostering the adoption of innovative methods for controlling their processes and products, in a non-destructive, in-place, reliable, fast, accurate and cost-efficient manner. Furthermore, the parameters requested by the industry for the quality assessment are evolving from basic magnitudes as pressures, temperatures, humidity, etc. to complete chemical and physical fingerprints of these products and processes. In this situation, techniques based on the UV/VIS/NIR light-matter interaction appear to be optimum candidates to face the request of the industry. Moreover, at this moment, when we are witnessing a technological revolution in the field of optoelectronic components, which are required for setting up these light-based analyzers.However, being able to integrate these optoelectronic components with the rest of subsystems (electronics, optics, mechanics, hydraulics, data processing, etc.) is not straightforward. The development of these multi-domain and heterogeneous sensor products meeting not just technological but also market objectives poses a considerable technical and organizational challenge for any company.In this context, a methodological hybrid and agile integration of photonic components within the rest of subsystems towards a sensor product development is presented as the main outcome of the thesis. The methodology has been validated in several industrial scenarios, being three of them included in this thesis, which covers from hydraulic fluid quality control to real-time monitoring of alcoholic beverage fermentation process

Maintenance Management of Wind Turbines

“Maintenance Management of Wind Turbines” considers the main concepts and the state-of-the-art, as well as advances and case studies on this topic. Maintenance is a critical variable in industry in order to reach competitiveness. It is the most important variable, together with operations, in the wind energy industry. Therefore, the correct management of corrective, predictive and preventive politics in any wind turbine is required. The content also considers original research works that focus on content that is complementary to other sub-disciplines, such as economics, finance, marketing, decision and risk analysis, engineering, etc., in the maintenance management of wind turbines. This book focuses on real case studies. These case studies concern topics such as failure detection and diagnosis, fault trees and subdisciplines (e.g., FMECA, FMEA, etc.) Most of them link these topics with financial, schedule, resources, downtimes, etc., in order to increase productivity, profitability, maintainability, reliability, safety, availability, and reduce costs and downtime, etc., in a wind turbine. Advances in mathematics, models, computational techniques, dynamic analysis, etc., are employed in analytics in maintenance management in this book. Finally, the book considers computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques that are expertly blended to support the analysis of multi-criteria decision-making problems with defined constraints and requirements