Balanceamento vetorial de máquinas rotativas com um ou dois planos de correção e análise espectral com o uso de microcontrolador

Abstract

This work develops a low-cost solution for predictive maintenance and balancing of rotating machinery, using microcontrollers and affordable sensors. It implements the Least Squares Method (LSM) and the Influence Coefficients (IC) method to obtain vibration phasors and calculate imbalance correction in rigid rotors, with signal processing in ESP32-S3-WROOM-1 and ADXL311 sensors. It also employs spectral analysis with digital integration via RC and FFT filters to identify other vibrations and detect faults, such as bearing wear. In an experiment, vibrations were reduced by 95.72%, with an average error of 5.28% in mass and 3.18% in angle due to manual positioning limitations, reaching values below the limits imposed by the ISO21940-11 (2016) standard. This approach stands out for its accessibility, low cost, and integration of linear algebra, statistics, programming, electronics, and mechanical vibrations, offering a viable alternative for industrial monitoring. Future improvements include spectral analysis automation, greater correction accuracy, IoT integration, and comparison with commercial Schenck solutions.