Rolling Bearing Life Prediction Based on Improved Transformer Encoding Layer and Multi-Scale Convolution
To accurately and reliably characterize the degradation trend of rolling bearings and predict their life cycle, this paper proposes a bearing life prediction model based on an improved transformer encoder layer and multi-scale convolution. First, time-domain, frequency-domain, and time-frequency dom...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/13/6/491 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | To accurately and reliably characterize the degradation trend of rolling bearings and predict their life cycle, this paper proposes a bearing life prediction model based on an improved transformer encoder layer and multi-scale convolution. First, time-domain, frequency-domain, and time-frequency domain features are extracted from the vibration data covering the entire lifespan of the rolling bearings and passed through the transformer encoder layer. A novel dual-layer self-attention mechanism network structure is proposed to capture global information on the lifecycle progression of rolling bearings. Next, to further extract local temporal features within the bearing’s life cycle, a multi-scale convolution module is proposed to reinforce the local information across the entire lifespan. This method fully exploits both the long-term trends and short-term dynamic variations in the health status of rolling bearings, effectively enhancing the accuracy of life predictions. Experimental results show that, even under conditions with interference features, the TransCN model outperforms mainstream advantage model in terms of prediction accuracy and generalizability. This approach offers a new solution for managing the fault risk of rotating machinery and reducing maintenance costs. |
|---|---|
| ISSN: | 2075-1702 |