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Rolling Bearing Fault Diagnosis Based on an Improved HTT Transform.

Bin Pang¹, Guiji Tang², Tian Tian³

¹School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding 071000, China. 1162102031@ncepu.edu.cn.

Sensors (Basel, Switzerland)

|April 18, 2018

Summary

This study introduces an improved Hilbert time-time (IHTT) transform for rolling bearing fault diagnosis. The method effectively suppresses noise and enhances fault features in vibration signals for accurate failure detection.

Keywords:

Hilbert TT transform fault diagnosis improved Hilbert TT transform principal component analysis rolling bearing

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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.