BDTD - Biblioteca Digital de Teses e Dissertações
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Doctoral Thesis Enhancing fault and failure prediction in redundancy models: a novel approach using generalized stochastic petri networks and spiking neural networks(Universidade Federal do Rio Grande do Norte, 2024-02-29) Abreu, Rute Souza de; Silva, Ivanovitch Medeiros Dantas da; https://orcid.org/0000-0002-0116-6489; http://lattes.cnpq.br/3608440944832201; http://lattes.cnpq.br/7179002501337812; Costa, Daniel Gouveia; Villanueva, Juan Moisés Mauricio; Oliveira, Luiz Affonso Henderson Guedes de; Moioli, Renan CiprianoFailure prediction plays an important role across several sectors such as industry, technology, medical sector, among others. This task can help in the reducing of equipment maintenance costs, prevention of accidents and disasters, and improvement of system dependability since it can increase availability by reducing system downtime. This work presents a methodology for fault prediction in redundancy models designed using the formality of Generalized Stochastic Petri Networks. The approach comprehends the steps of modeling and simulation of systems with active and passive redundancy under different fault scenarios, such as non-perfect switches, standby failures, and common cause failures, as well as fault datasets generation and the implementation of a machine learning model for performing the fault prediction. For forecasting, this research utilizes Spiking Neural Networks (SNNs), which have been recognized as the third generation of Artificial Neural Networks. Just like typical artificial neural networks, SNNs draw inspiration from the biological dynamics of the brain, incorporating the interconnected topology of neurons into their architecture. However, while conventional neural networks rely on error minimization by weight adjustment, SNNs aim to replicate the learning process by simulating neuron behavior by taking into account elements of the biological process such as synapse, energy accumulation, electric impulse firing, and refractory periods between emissions. Due to the ability to capture temporal aspects from data, SNNs are vastly used in problems with time dynamics. Additionally, literature has shown these networks to be task and energy-efficient serving as a low-cost alternative compared to conventional ANNs.Master Thesis A methodology for detection of causal relationships between discrete time series on systems(2019-01-25) Abreu, Rute Souza de; Oliveira, Luiz Affonso Henderson Guedes de; ; ; Silva, Ivanovitch Medeiros Dantas da; ; Martins, Rodrigo Siqueira;The need for detecting causality relations of process, events or variables is present in many areas of knowledge, e.g., distributed computing, the stock market, industry and medical sector. This occurs because the knowledge of these relations can often be helpful in solving a variety of problems. For example, maintaining the consistency of replicated databases when writing distributed algorithms or optimizing the purchase and sale of stocks in the stock market. In this context, this dissertation proposes a new methodology for detecting causality relations in systems by using information criteria and Bayesian networks to generate the most probable structure of connections between discrete time series. Modeling the system as a directed graph, in which the nodes are the discrete time series and the edges represent the relations, the main idea of this work is to detect causality relations between the nodes. This detection is made using the method of transfer entropy, which is a method to quantify the information transferred between two variables, and the K2 algorithm: a heuristic method whose objective is to find the most probable belief-network structure, given a data set. Because K2 depends on the premise of having a previous structure that defines the hierarchy among the network nodes, it is proposed in the methodology the creation of the previous ordering on the nodes considering direct and indirect relations, and the modeling of these relations according to the lag between cause and effect. In addition, knowing that the K2 algorithm considers that each case of the data set occurs simultaneously, the proposed methodology modifies the original algorithm by inserting the dynamics of these lags into it. This modification provides a mechanism for comparing direct and indirect causality relations regarding its contribution to the structure. As the result, it is obtained a graph of causality relations between the series, with the relation’s lags being explicit.