Bearing Outer Race Fault Detection Using Wavelet Transform

This article explores the application of wavelet transform for detecting bearing outer race faults - a common mechanical failure in industrial production. Bearing failures significantly impact production efficiency and safety, with outer race defects being one of the most prevalent fault types. The wavelet transform technique enables effective detection of outer race faults through signal processing, allowing for timely maintenance interventions to ensure operational safety and productivity.

The wavelet transform methodology involves decomposing vibration signals into different frequency components using mother wavelets (such as Daubechies or Morlet wavelets) and reconstructing them to identify fault-induced patterns. This process typically includes: 1) Multi-level signal decomposition using discrete wavelet transform (DWT) to extract detail coefficients, 2) Threshold-based denoising to eliminate background noise, and 3) Feature extraction from reconstructed signals to identify fault characteristics. The technique serves as an effective tool for detecting outer race faults by analyzing vibration signatures that exhibit specific frequency modulations when defects are present. Furthermore, analysis of detection results provides insights into fault severity levels and helps prioritize maintenance actions, thereby offering robust support for production safety and efficiency optimization.

Key implementation steps in MATLAB would include using functions like wavedec for signal decomposition, wden for denoising, and waverec for signal reconstruction, followed by frequency domain analysis using FFT to identify fault-specific frequency components.