Rolling Bearing Fault Diagnosis Based on an Improved HTT Transform

Area of Science:

• Mechanical Engineering
• Signal Processing
• Condition Monitoring

Background:

• Vibration signals from rolling bearings contain fault features, noise, and interference.
• Effective fault diagnosis requires suppressing noise while highlighting fault signals.

Purpose of the Study:

• To develop a novel method for rolling bearing fault diagnosis.
• To enhance the detection of impulsive fault features in vibration data.

Main Methods:

• A novel improved Hilbert time-time (IHTT) transform was developed.
• The method combines Hilbert time-time (HTT) transform with principal component analysis (PCA).
• PCA was used to de-noise the HTT transform matrix.

Main Results:

• The IHTT transform successfully enhanced impulsive fault feature signals.
• The method demonstrated improved robustness in de-noising vibration signals.
• Simulated and experimental data confirmed the method's effectiveness.

Conclusions:

• The proposed IHTT transform is superior for detecting rolling bearing failures.
• This technique offers a robust approach for condition monitoring and fault diagnosis.