Coupling Singular Spectral and Envelope Analysis for Localised Bearing Defect Detection

• Abstract:

  In many industrial situations, bearing failure can lead to serious consequence on the overall process. A bearing’s fault progressive character raises the question of finding the right moment to perform replacement at the cost of stopping the machine. The study done in this paper deals with mathe- matical modeling of the bearing’s rolling element with a local defect on its fixed outer ring, based on a mass-spring-damper archetype system. A simulation of the vibratory behavior is performed, and its impact on ball-defect coincidences during shaft rotation under different working conditions is analyzed. The paper suggests applying advanced pre-processing tech- niques such as Singular Spectrum Analysis (SSA) and Envelope Analysis (EA) before extracting statistical indicators. Some well- known time-domain indicators such as the Root Mean Square (RMS), kurtosis, and Energy around Ball Pass Frequency Outer- ring (EBPFO) are used on the raw and processed signals to highlight the defect evolution. The results carried out show that, applying the EA with SSA on the raw time-series signal at the pre-processing level, before statistical analysis can significantly improve the detection, thus an excellent diagnosis to incipient defects in bearings.
• Keywords:

  failure; modelling; vibration analysis; indicator; detection; diagnosis
• Download:

  IMEKO-TC10-2022-020.pdf
• DOI:

  tc10-2022.020

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