Step robust mode decomposition method and its application in rolling bearing fault diagnosis

Area of Science:

• Mechanical Engineering
• Signal Processing
• Condition Monitoring

Background:

• Rolling bearings are critical components in machinery.
• External impacts and uneven loads introduce step disturbances.
• These disturbances corrupt fault features and reduce diagnostic accuracy.

Purpose of the Study:

• To develop a robust method for decomposing signals containing step disturbances.
• To enhance the accuracy of fault diagnosis in rolling bearings.
• To preserve the integrity of fault features during signal decomposition.

Main Methods:

• Proposed a Step Robust Mode Decomposition (SRMD) method.
• Employed a physically constrained collaborative optimization with derivative penalty terms and sparse modeling.
• Utilized an adaptive iterative mechanism for autonomous modal component determination.

Main Results:

• SRMD effectively separates non-periodic transient step disturbances.
• The method preserves the integrity of fault features.
• Experimental results show superior robustness and accuracy across various noise levels.

Conclusions:

• SRMD offers a robust solution for fault diagnosis in rolling bearings under challenging conditions.
• The method enhances diagnostic accuracy by effectively handling step disturbances.
• SRMD demonstrates significant improvements in fault feature extraction and identification.