Predictive long-term asset maintenance strategy: development of a fuzzy logic condition-based control system

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceTechnology has accelerated the growth of the Facility Management industry and its roles are broadening to encompass more responsibilities and skill sets. FM budgets and teams are becoming larger and more impactful as new technological trends are incorporated into data-driven strategies. This new scenario has motivated institutions such as the European Central Bank to initiate projects aimed at optimising the use of data to improve the monitoring, control and preservation of the assets that enable the continuity of the Bank's activities. Such projects make it possible to reduce costs, plan, manage and allocate resources, reinforce the control, and efficiency of safety and operational systems. To support the long-term maintenance strategy being developed by the Technical Facility Management section of the ECB, this thesis proposes a model to calculate the Left wear margin of the equipment. This is accomplished through the development of an algorithm based on a fuzzy logic system that uses Python language and presents the system's structure, its reliability, feasibility, potential, and limitations. For Facility Management, this project constitutes a cornerstone of the ongoing digital transformation program

In(c)(v)ite: the in-Between Project

The present paper presents an alternative urban design approach that explores the project as an in- between mechanism. By in-between, we assume that “the project is neither the beginning nor the ending it is just an in-between in places’ time, both past and indeterminate future.”[1] It is an in-between time process that crosses several scales, actors, and places. We found the in-Between Project by searching through the existing cracks [2] in the contemporary built environment – uncertain and abandoned places/buildings and wastelands – generated by four factors: (a) the increasing of a fragile global economy; (b) the recurrent urban transformation processes (such as the over construction of road infra- structures and the cyclic destruction/construction of the old/new housing planning); (c) the absence of activities/production; and (d) the consequent abandonment of buildings and urban plots. Therefore, it was acknowledged that these cracking processes are creating a catalytic effect in the built environment, causing uncertain cross-scaled consequences between time, space, and society such as: not knowing the future of these places, not expecting positive scenarios for these places, not conveying the relationships of these places, and not engaging socially with these places. When addressing this problematic, fundamental questions arise: how can we articulate the (dis)connections created by the existing cracks in the urban environment? How can we transform the waste inherent to these cracks into a life potential? How can we create a viable metabolism with this waste? How can we generate new activities? How can we attract new inhabitants? How can we transform cracks into magnets? In this research, we realised that these questions cannot be answered through the narrow design solutions formalized by neither the conventional object/programmatic approach, nor by the top-to-bottom/bottom-to-top urban strategies, detached from the indeterminate cross time-scale relationships of these cracked places. An alternative urban design approach was required. The in-Between Project is structured using three interconnected concepts: Cite, Recite. and Incite. Cite is to select the time traces found in the place to trigger and ground the design project. Recite is to transform the cited elements into a simple base structure that stimulates unforeseen appropriations and becomes adaptable to change. Incite is to critically imagine future possible scenarios for the created base structure. These ideas are presented, tested, and developed in one specific design research in Azenha do Mar, a remote fishing village on the southwest coast of Portugal. It is acknowledged that the in-Between Project is a simple practice of in(c)(v)itation: it incites the hidden potentials of cracked places and invites human beings to appropriate them in an imaginative and unforeseeable way.info:eu-repo/semantics/publishedVersio

Multilayer Quantile Graph for Multivariate Time Series Analysis and Dimensionality Reduction

In recent years, there has been a surge in the prevalence of high- and multi-dimensional temporal data across various scientific disciplines. These datasets are characterized by their vast size and challenging potential for analysis. Such data typically exhibit serial and cross-dependency and possess high dimensionality, thereby introducing additional complexities to conventional time series analysis methods. To address these challenges, a recent and complementary approach has emerged, known as network-based analysis methods for multivariate time series. In univariate settings, Quantile Graphs have been employed to capture temporal transition properties and reduce data dimensionality by mapping observations to a smaller set of sample quantiles. To confront the increasingly prominent issue of high dimensionality, we propose an extension of Quantile Graphs into a multivariate variant, which we term "Multilayer Quantile Graphs". In this innovative mapping, each time series is transformed into a Quantile Graph, and inter-layer connections are established to link contemporaneous quantiles of pairwise series. This enables the analysis of dynamic transitions across multiple dimensions. In this study, we demonstrate the effectiveness of this new mapping using a synthetic multivariate time series dataset. We delve into the resulting network's topological structures, extract network features, and employ these features for original dataset analysis. Furthermore, we compare our results with a recent method from the literature. The resulting multilayer network offers a significant reduction in the dimensionality of the original data while capturing serial and cross-dimensional transitions. This approach facilitates the characterization and analysis of large multivariate time series datasets through network analysis techniques