Compound Fault Diagnosis of Rolling Bearing Based on Singular Negentropy Difference Spectrum and Integrated Fast...

Area of Science:

• Mechanical Engineering
• Signal Processing
• Fault Diagnosis

Background:

• Compound faults in mechanical systems present significant diagnostic challenges due to their complexity and non-stationary nature.
• Existing methods struggle with the intricate and diverse characteristics of these faults.
• Accurate separation of individual fault signatures is crucial for reliable diagnosis.

Purpose of the Study:

• To propose a novel method for separating compound fault features in mechanical systems.
• To enhance the accuracy and effectiveness of fault diagnosis in complex machinery.
• To address the limitations of current techniques in handling non-stationary and diverse fault signals.

Main Methods:

• A new compound fault separation method integrating singular negentropy difference spectrum (SNDS) and integrated fast spectral correlation (IFSC).
• Signal de-noising using SNDS, which improves noise reduction by incorporating negative entropy.
• Fault feature separation using IFSC, employing fourth-order energy to determine resonance bands and isolate single fault signatures.

Main Results:

• The proposed SNDS and IFSC method demonstrates superior noise reduction compared to traditional singular difference spectrum.
• IFSC effectively identifies resonance bands and separates distinct fault features from compound signals.
• Validation on simulated and experimental data confirms excellent performance in separating rolling bearing composite faults.

Conclusions:

• The novel SNDS and IFSC method provides an effective solution for the challenging problem of compound fault diagnosis.
• This approach significantly improves the separation of individual fault features within complex mechanical vibrations.
• The method shows high potential for practical applications in machinery health monitoring and diagnostics.