Acoustic-Based Rolling Bearing Fault Diagnosis Using a Co-Prime Circular Microphone Array

Area of Science:

• Mechanical Engineering
• Acoustics
• Signal Processing

Background:

• Bearing faults generate complex acoustic signals that are difficult to isolate due to component proximity.
• Traditional Direction-of-Arrival (DOA) estimation for noise suppression requires numerous microphones, increasing complexity.
• Accurate bearing fault diagnosis is crucial for industrial machinery maintenance and preventing failures.

Purpose of the Study:

• To develop a high-efficiency bearing fault diagnosis method using a novel microphone array configuration.
• To improve the accuracy of Direction-of-Arrival (DOA) estimation for acoustic fault signals.
• To enable precise identification and localization of bearing faults based on sound source characteristics.

Main Methods:

• Utilizing a co-prime circular microphone array (CPCMA) to enhance array degrees of freedom and reduce microphone dependency.
• Applying the Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) for rapid DOA estimation.
• Developing a sound source motion-tracking diagnosis method based on acoustic signal movement patterns.

Main Results:

• The CPCMA significantly reduces the number of microphones and computational load for accurate DOA estimation.
• The proposed method effectively separates and enhances acoustic signals from different bearing fault types.
• Precise frequency spectra were obtained, aiding in the determination of fault types and locations.

Conclusions:

• The CPCMA-based method offers a high-efficiency and accurate solution for bearing fault diagnosis.
• The sound source motion-tracking approach, combined with precise spectral analysis, reliably identifies bearing faults.
• This technique minimizes the dependence on microphone count and computational complexity in acoustic diagnosis.