Identification of Multiple Faults in Gearbox Based on Multipoint Optional Minimum Entropy Deconvolution Adjusted and...

Area of Science:

• Mechanical Engineering
• Signal Processing
• Fault Diagnosis

Background:

• Extracting weak compound fault features from gearboxes is challenging due to complex and inter-modulated signal components.
• Existing methods struggle with the subtlety of weak fault signatures in noisy gearbox environments.

Purpose of the Study:

• To propose a novel approach for extracting weak compound fault features in gearboxes.
• To enhance the accuracy and reliability of fault diagnosis in transmissions.

Main Methods:

• Developed a multipoint reciprocal permutation entropy (MRPE) method to track impact fault sources.
• Utilized multipoint optimal minimum entropy deconvolution adjustment (MOMEDA) for signal denoising and optimal filter coefficient determination.
• Combined MRPE and MOMEDA into the MRPE-MOMEDA approach.

Main Results:

• MRPE effectively tracks impact fault sources and indicates the fault period.
• MOMEDA demonstrates outstanding performance in suppressing noise in gearbox signals with periodic impacts.
• The proposed MRPE-MOMEDA method successfully identified multiple simultaneous faults on a driving gear.

Conclusions:

• The MRPE-MOMEDA approach is effective for extracting weak fault features in gearboxes.
• This method offers a reliable solution for diagnosing compound faults in transmissions.
• The study highlights the potential for improved gearbox condition monitoring.