Deep Transfer Network with Multi-Space Dynamic Distribution Adaptation for Bearing Fault Diagnosis

Area of Science:

• Mechanical Engineering
• Artificial Intelligence

Background:

• Domain adaptation methods are crucial for bearing fault diagnosis.
• Existing methods struggle with feature representation in diverse scenarios.
• Current adaptive methods lack consideration for variable cross-domain diagnostic cues.

Purpose of the Study:

• To propose a novel Deep Convolutional Multi-Space Dynamic Distribution Adaptation (DCMSDA) model.
• To enhance feature extraction for improved bearing fault diagnosis.
• To address limitations in current domain adaptation techniques for complex cross-domain scenarios.

Main Methods:

• A DCMSDA model with two feature extraction modules and a dynamic distribution adaptation module.
• Multi-space structure for comprehensive marginal and conditional distribution feature extraction.
• Dynamic distribution adaptation using varied metrics and an adaptive coefficient for alignment.

Main Results:

• The proposed DCMSDA model demonstrates excellent diagnosis performance.
• The method exhibits superior generalization capabilities across different domains.
• Experimental results validate the effectiveness of individual transfer modules within the DCMSDA model.

Conclusions:

• The DCMSDA model offers a significant advancement in bearing fault diagnosis.
• The proposed approach effectively handles complex cross-domain diagnostic challenges.
• The study highlights the importance of dynamic adaptation for robust fault diagnosis.