Estudo comparativo de métodos de compressão de dados e detecção de danos em monitoramento de integridade estrutural - ELIASQUEVICI, Felipe (2019)
Structural Health Monitoring (SHM) is a process that aims to detect damage in structures through sensors attached on this structure that capture the data and quantify the damage in real time. A big number of sensors capturing information in short time intervals can generate a huge volume of data, that needs to be transmitted and stored. Data compression then becomes essential to these kinds of system. In the compression process, however, essential characteristics of the signals generated by the sensors can be lost, and the damage detection could be compromised. Considering that, this study propose to compare several combinations of data compression methods and damage detection techniques. To measure the efficiency of those combinations it is utilized a parametric metric, which quantifies how good a combination is in a single measure. After comparing the results it was observed that the algorithm Chebshevy Approximation generate the best results, specially when combined with Fuzzy-C-Means and K-Means.