Navegando por Assunto "Harmonic analysis"

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Desconhecido
Estudo de técnicas de análise modal operacional em sistemas sujeitos a excitações aleatórias com a presença de componente harmônico
(Universidade Federal do Pará, 2006-02-17) CRUZ, Sérgio Luiz Matos da; MESQUITA, Alexandre Luiz Amarante; http://lattes.cnpq.br/3605920981600245
Traditional modal parameter identification usually require measurements of both the input force and the resulting response in laboratory conditions. However, when modal properties are to be identified from large structures in operation, usually the possibilities to control and measure the loading on the structure is rather limited. In this case, the modal testing is usually performed using response data only. Operational Modal Analysis (OMA) or Operational Modal Testing is a method where no artificial excitation needs to be applied to the structure or force signals to be measured. In this case, the modal parameters estimation is based upon the response signals, thereby minimizing the work of preparation for the test. However, standard OMA techniques, such as NExT, are limited to the case when excitation to the system is a white stationary noise. The NexT assumes that the correlation functions are similar to the impulse response functions, and then, traditional time domain identification methods can be applied. However, if harmonic components are present in addition to the white noise, these components can be misunderstood as natural modes in the plot of response spectrum. In this work, it is shown that it is possible identify if a peak in the response spectrum correspond to a natural mode or an operational mode. It is achieved through the application of the probability density function. It is also presented a modification in the LSCE algorithm in such manner that it can support harmonics in the operational excitation. In order to validate the methods presented in this work, it is shown numerical and experimental cases. In the former, results for a mass-spring-damper of five degree of freedom are presented, and in the latter a beam supporting an unbalanced motor is analyzed.
Desconhecido
Identificação de sistemas multiforças a partir de dados de vibração e técnicas de aprendizado de máquinas
(Universidade Federal do Pará, 2024-11-07) PINHEIRO, Giovanni de Souza; NUNES, Marcus Vinícius Alves; http://lattes.cnpq.br/9533143193581447
The emergence of defects in dynamic components tends to produce changes in the forces generated, which can be detected through alterations in the vibration response spectrum of the equipment. Understanding the forces acting on a structure is extremely important, especially in cases where measurement points are limited or inaccessible, as it allows for assessing, among other things, whether the component's lifespan is compromised by the current condition of the machine. In such cases, an inverse problem needs to be solved. Machine Learning techniques have been standing out as a powerful tool for prediction among the solutions developed for this type of problem, being increasingly applied to engineering problems. Therefore, this work aims to evaluate different machine learning models for the identification of a system, composed of a suspended plate with one or more applied forces, based on measured vibration data. In this regard, a computational model was generated and calibrated using vibration responses measured in the laboratory. A robust database was created using Response Surface Methodology together with the Design of Experiment (DOE) and then used to assess the ability of machine learning models to predict the location, excitation frequency, magnitude, and number of forces acting on the structure. Among the six machine learning models evaluated, k-NN was able to predict with an error of 0.013%, and random forests showed a maximum error of 0.2%. Finally, a database, containing a line of experimental data, was used to evaluate the k-NN and Random Forest models, obtaining a score of 0.96 and 0.93, respectively. The innovation of the study lies in the application of the proposed method for parameter identification in multiforce systems.