A New Morphological Filter for Fault Feature Extraction of Vibration Signals

Abstract

Early faults in rolling bearings tend to result in periodic impulse components in the collected vibration signals. However, these fault features are always distorted by noise interferences. Feature extraction from vibration signals is an effective means to detect early defects in rolling bearings. A new morphological filter (MF), called enhanced difference morphological filter (EDMF) is proposed for vibration signal processing and then implementing bearing fault diagnosis. EDMF is capable of depressing noise and preserving effective impulsive components, where four new basic morphological operators are integrated effectively. The experimental results on simulation and bearing vibration signals verify that EDMF is effective for defect detection of rolling bearings. The comparison results show that the new MF can extract fault features more effectively from vibration signals with much noise than other typical MFs.