Vibration-based detection of blower bearing defects using FFT and envelope analysis to improve machine reability - Yusuf Efendi; Sally Cahyati; Soeharsono Soeharsono (Jurnal Polimesin, 2026)
Rolling bearing failure in blower machines can disrupt plant operations, increase maintenance costs, and trigger unplanned shutdowns. This study aims to detect early-stage bearing damage in a blower unit at Plant Sabiz using vibration signal analysis. Vibration data were acquired using an SKF Microlog Analyzer CMDT 391 and evaluated using Fast Fourier Transform (FFT) and envelope analysis to identify defect-related frequency components. The results revealed an increase in velocity vibration up to 4.87 mm/s and envelope vibration up to 34.4 gE. The FFT spectrum showed dominant harmonics from 1xRPM to 3xRPM, indicating dynamic imbalance and potential damage to rotating components. Furthermore, envelope analysis identified bearing characteristic frequencies, particularly the Fundamental Train Frequency (FTF) and its harmonics, specifically pointing to cage degradation. This pattern was reinforced by the non-dominance of Ball Pass Frequency Outer Race (BPFO) and Ball Pass Frequency Inner Race (BPFI) frequencies, which ruled out damage to the outer and inner races. Visual inspection confirmed this interpretation, revealing cracks and breaks in the bearing cage. These findings confirm that combining FFT and envelope analysis is effective not only for early detection of bearing defects but also for identifying the specific type of damage based on frequency and harmonic patterns.