Identificação de sistemas multiforças a partir de dados de vibração e técnicas de aprendizado de máquinas

Abstract

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.