Research on Fault Prediction Method of Elevator Door System Based on Transfer Learning
Abstract
:1. Introduction
2. Fault Characterization and Feature Extraction
2.1. Fault Characterization of an Elevator Door System
2.2. Feature Extraction
3. Model Construction
3.1. GNN-LSTM Feature Extraction Section
3.2. DANN Improved Based on Bhattacharyya Distance
3.3. Construction of Overall Model
4. Experimental Result and Discussion
4.1. Data Preparation
4.2. Model Performance Verification
5. Conclusions
- (1)
- Contrasted with conventional deep learning techniques, the Graph Neural Network (GNN) is capable of revealing intricate relationships between data points. When integrated with Long Short-Term Memory (LSTM), it effectively captures more profound time-series characteristics. The GNN-LSTM feature extraction module proposed in this study outperforms standalone GNN or LSTM methods in terms of feature extraction efficacy.
- (2)
- The BDANN transfer learning method proposed in this paper, as compared to the original DANN, addresses the issue of DANN’s inability to precisely represent distribution differences in different domains. It alleviates the problem of gradient vanishing during training, thereby enhancing model stability.
- (3)
- The experimental results substantiate that the GNN-LSTM-BDANN prediction model proposed in this study effectively harnesses historical data from other elevators to realize the prediction of the remaining service life for the target elevator, and it exhibits superior predictive performance compared to the two transfer learning methods, TCA and JDA. Consequently, this model provides a favorable foundation for the prediction of failures and the implementation of preventive maintenance in elevator door systems.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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MSE | RMSE | |
---|---|---|
GNN-LSTM-BDANN | 0.0036 | 0.0601 |
GNN-BDANN | 0.0237 | 0.1540 |
LSTM-BDANN | 0.0318 | 0.1783 |
MSE | RMSE | |
---|---|---|
GNN-LSTM-BDANN | 0.0036 | 0.0601 |
GNN-LSTM-DANN | 0.0044 | 0.0666 |
GNN-LSTM | 0.0626 | 0.2502 |
MSE | RMSE | |
---|---|---|
GNN-LSTM-BDANN | 0.0036 | 0.0601 |
TCA | 0.0183 | 0.0855 |
JDA | 0.0170 | 0.0825 |
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Pan, J.; Shao, C.; Dai, Y.; Wei, Y.; Chen, W.; Lin, Z. Research on Fault Prediction Method of Elevator Door System Based on Transfer Learning. Sensors 2024, 24, 2135. https://doi.org/10.3390/s24072135
Pan J, Shao C, Dai Y, Wei Y, Chen W, Lin Z. Research on Fault Prediction Method of Elevator Door System Based on Transfer Learning. Sensors. 2024; 24(7):2135. https://doi.org/10.3390/s24072135
Chicago/Turabian StylePan, Jun, Changxu Shao, Yuefang Dai, Yimin Wei, Wenhua Chen, and Zheng Lin. 2024. "Research on Fault Prediction Method of Elevator Door System Based on Transfer Learning" Sensors 24, no. 7: 2135. https://doi.org/10.3390/s24072135
APA StylePan, J., Shao, C., Dai, Y., Wei, Y., Chen, W., & Lin, Z. (2024). Research on Fault Prediction Method of Elevator Door System Based on Transfer Learning. Sensors, 24(7), 2135. https://doi.org/10.3390/s24072135