Francisco Gean D. da Silva Filho 
, Joaquim O. F. Moura Filho , Vandilberto P. Pinto & Márcio A. B. Amora
Abstract: The degradation of bearings is a critical issue in mechanical systems, leading to performance deterioration and potential failures. Classifying degradation stages in bearing systems is essential for effective maintenance and the prevention of unexpected downtime. Based on this, the study presents a methodology for detecting degradation stages in bearing systems by employing multiple techniques, such as Fourier transform, principal component analysis, and decision trees. The results demonstrate that this approach enables the detection of the current degradation stage using techniques that generally require lower computational effort compared to methods previously used in the literature. Additionally, the comparative analysis reveals superior performance in terms of bearing lifespan after fault detection in advanced stages, surpassing results from the literature. Moreover, interpreting the results through SHAP plots identifies value ranges within crucial features for fault classification, such as mean absolute deviation and linear predictive cepstral coefficients. The dependency analysis between these features provides valuable insights into failure patterns. The achieved classification accuracy exceeded 90%, demonstrating the effectiveness and efficiency of the proposed method in enhancing predictive maintenance for rolling bearings.
Keywords: Detection of degradation stage, bearing systems, predictive maintenance, principal component analysis, decision trees, shapley additive explanations, classification.
DOI code: 10.21528/lnlm-vol23-no1-art5
PDF file: vol23-no1-art5.pdf
BibTex file: vol23-no1-art5.bib