Application of an Improved Multipoint Optimal Minimum Entropy Deconvolution Adjusted for Gearbox Composite Fault...

Area of Science:

• Mechanical Engineering
• Signal Processing
• Condition Monitoring

Background:

• Gearbox fault diagnosis is challenging under complex working conditions.
• Accurate extraction of fault features is crucial for preventing failures.

Purpose of the Study:

• To develop a hybrid fault diagnosis method for gearboxes.
• To enhance the extraction of fault features from complex signals.

Main Methods:

• Ensemble Local Mean Decomposition (ELMD) for noise reduction and component separation.
• Correlation coefficient method to remove false and residual components.
• Multi-point kurtosis spectrum analysis for fault period extraction.
• Combined Product Function (CPF) for signal recombination.
• Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) for noise reduction and feature enhancement.

Main Results:

• Successfully reduced noise and identified relevant signal components.
• Extracted fault feature periods by analyzing product functions.
• Recombined periodic components while preserving signal integrity.
• Applied MOMEDA to further refine fault feature extraction.

Conclusions:

• The proposed hybrid method effectively extracts gearbox fault features, even for composite faults.
• Demonstrated feasibility and effectiveness in complex operational scenarios.
• Offers a robust approach for gearbox condition monitoring and diagnostics.