Fault Diagnosis in Bearing Housings Using Wavelet Transform

Resumo

The early and accurate diagnosis of bearing housing defects in industrial equipment is essential to ensure the reliability and operational efficiency of rotating components, preventing unscheduled downtimes. This study presents the wavelet transform as an effective technique for analyzing vibration signals from operational bearing housings, enabling the identification and characterization of faults at incipient stages. Due to its capability for multiresolution analysis, offering joint time-frequency representation, the wavelet transform is well-suited for detecting transients and irregularities associated with mechanical failures. Various wavelet families were evaluated for signal decomposition, facilitating the extraction of discriminative features that aid in distinguishing between normal and faulty conditions. The orthogonal decomposition achieved through the transform during signal processing highlighted early fault indicators while suppressing system noise. The results demonstrate that this approach significantly enhances predictive monitoring systems, improving diagnostic accuracy in industrial machinery. Thus, the application of the wavelet transform proves to be a robust and promising alternative for detecting transients and characteristic fault frequencies in the initial stages of bearing housing failures, supporting real-time decision-making and improving operational reliability.