Luís Guimarães

Abstract
Ensuring process quality in modern manufacturing is increasingly challenging due to the complexity of production processes and reliance on skilled operators, which can lead to suboptimal solutions and poor quality. To address these challenges, we introduce a novel, unsupervised, robust, nonparametric control chart for Phase II monitoring. This chart tracks the degradation of a quality characteristic using a condition index that captures mean and scale shifts without relying on assumptions, offering high flexibility and adaptability. Comparative studies with state-of-the-art nonparametric schemes demonstrate faster detection capabilities and competitive accuracy across various scenarios. We validate our approach through its application in the glass container production process, showcasing its effectiveness in monitoring multiple defective rates. Although tested on defective rates, the methodology is adaptable to any quantifiable quality characteristic.

Abstract
Hydropower currently generates more than all other renewable energies combined. Considering the challenges of climate change and the transition to green energy, it is expected to remain the world's largest source of renewable electricity generation. This paper proposes a tool for performance evaluation and benchmarking of hydropower generation to inform dispatching. Through them, strengths and weaknesses of asset operations can be set, identifying areas with the best performance, gathering insights from their strategies and best practices, and comprehending factors that lead to variations in performance levels. The results allow for optimising energy resource use by indicating the dispatching rules with maximum power production and minimum wearand-tear impact. This framework allows the formulation of practical guidelines for dispatching policies. The proposed methodology is applied to analyse two real-world case studies: the Vogelgr & uuml;n run of river hydropower plant (France) and the Frades 2 pump-storage powerplant (Portugal).

Abstract
Maintaining process quality is one of the biggest challenges manufacturing industries face, as production processes have become increasingly complex and difficult to monitor effectively in today's manufacturing contexts. Reliance on skilled operators can result in suboptimal solutions, impacting process quality. In doing so, the importance of quality monitoring and diagnosis methods cannot be undermined. Existing approaches have limitations, including assumptions, prior knowledge requirements, and unsuitability for certain data types. To address these challenges, we present a novel unsupervised monitoring and detection methodology to monitor and evaluate the evolution of a quality characteristic's degradation. To measure the degradation we created a condition index that effectively captures the quality characteristic's mean and scale shifts from the company's specification levels. No prior knowledge or data assumptions are required, making it highly flexible and adaptable. By transforming the unsupervised problem into a supervised one and utilising historical production data, we employ logistic regression to predict the quality characteristic's conditions and diagnose poor condition moments by taking advantage of the model's interpretability. We demonstrate the methodology's application in a glass container production process, specifically monitoring multiple defective rates. Nonetheless, our approach is versatile and can be applied to any quality characteristic. The ultimate goal is to provide decision-makers and operators with a comprehensive view of the production process, enabling better-informed decisions and overall product quality improvement.

Abstract

Abstract
Data clustering combined with multiobjective optimization has become attractive when the structure and the number of clusters in a dataset are unknown. Data clustering is the main task of exploratory data mining and a standard statistical data analysis technique used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. This project analyzes data to extract possible failure patterns in Solar Photovoltaic (PV) Panels. When managing PV Panels, preventive maintenance procedures focus on identifying and monitoring potential equipment problems. Failure patterns such as soiling, shadowing, and equipment damage can disturb the PV system from operating efficiently. We propose a multiobjective evolutionary algorithm that uses different distance functions to explore the conflicts between different perspectives of the problem. By the end, we obtain a non-dominated set, where each solution carries out information about a possible clustering structure. After that, we pursue a-posteriori analysis to exploit the knowledge of non-dominated solutions and enhance the fault detection process of PV panels.