An Enhanced TK Technology for Bearing Fault Detection Using Vibration Measurement

Area of Science:

• Mechanical Engineering
• Signal Processing
• Condition Monitoring

Background:

• Rolling element bearings are crucial components in rotating machinery.
• Effective bearing fault detection is essential for preventing performance degradation and reducing maintenance costs.
• Existing fault detection methods face challenges in reliability.

Purpose of the Study:

• To propose an enhanced Teager-Kaiser (eTK) technique for improved bearing fault detection and diagnosis.
• To enhance the accuracy and reliability of bearing condition monitoring.

Main Methods:

• Utilized vibration signals for bearing fault analysis.
• Employed empirical mode decomposition (EMD) to identify representative intrinsic mode functions (IMFs).
• Developed an eTK denoising filter to enhance signal-to-noise ratio of selected IMF features.
• Applied analytical signal spectrum analysis for feature identification.

Main Results:

• The proposed eTK technique demonstrated effectiveness in bearing fault detection and diagnosis.
• The combination of EMD and eTK denoising improved the identification of fault-related features from vibration signals.
• Experimental validation confirmed the technique's capability across various bearing conditions.

Conclusions:

• The enhanced Teager-Kaiser (eTK) technique offers a novel and effective approach for bearing fault detection.
• The method successfully addresses challenges in reliable bearing condition monitoring.
• The proposed technique holds potential for practical applications in industrial machinery maintenance.