Feature Extraction for Low-Speed Bearing Fault Diagnosis Based on Spectral Amplitude Modulation and Wavelet Threshold...

Area of Science:

• Mechanical Engineering
• Signal Processing
• Condition Monitoring

Background:

• Extracting bearing fault features in low-speed conditions is challenging due to weak signals and significant environmental noise.
• Traditional spectral amplitude modulation methods are highly susceptible to noise, limiting their effectiveness.

Purpose of the Study:

• To propose a robust low-speed bearing fault diagnosis method.
• To overcome the limitations of traditional methods in noisy environments.
• To enhance the extraction of weak fault features.

Main Methods:

• Wavelet threshold denoising applied to raw signals to reduce background noise.
• Spectral amplitude modulation on the denoised signal to enhance bearing fault impulses.
• Normalization of the envelope spectrum for clear visualization of fault frequencies.

Main Results:

• The proposed method effectively reduces noise interference in low-speed signals.
• Weak bearing fault features are successfully extracted even in the presence of strong noise.
• Simulated and experimental signal analyses confirm the method's efficacy.

Conclusions:

• The developed method provides a reliable approach for low-speed bearing fault diagnosis.
• It significantly improves the ability to detect faults by enhancing signal clarity.
• This technique offers a practical solution for condition monitoring in challenging low-speed applications.