Abstract Details
168 / 2025-06-10 19:01:29
Adaptive Reconstruction of Sensor Fault Data Based on CNN-LSTM Neural Network
data reconstruction,sensor fault,LSTM,CNN
Final Paper
Accurate condition monitoring is essential for ensuring efficient and reliable operation of integrated transmission systems, where sensor data reliability plays a critical role. To address this challenge, we propose a novel deep learning-based fault reconstruction model that exploits structural and temporal correlations among sensors. When sensor failures occur, our model automatically reconstructs faulty measurements while maintaining data accuracy, thereby enhancing system reliability and operational safety. The proposed framework also serves as a robust reference for subsequent fault diagnosis research.Methodologically, we first employ Exponentially Weighted Moving Average (EWMA) for data smoothing, followed by correlation analysis to identify highly interdependent sensor features. We then develop a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture to achieve precise sensor data reconstruction. Extensive case studies on integrated transmission systems with multiple sensor faults validate the model's reliability, accuracy, and effectiveness. Results demonstrate that our approach provides a technically sound and efficient solution for enhancing sensor reliability in complex transmission systems.
Important Date
-
Conference Date
Aug 01
2025
toAug 04
2025
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Aug 20 2025
Draft paper submission deadline
Sponsored By
中国机械工程学会设备智能运维分会
Organized By
新疆大学
Contact Information
- Mr. Yanruqiang
- ya******@xjtu.edu.cn
Previous Conferences
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2025-08-01 China wulumuqi
2025 International Conference on Equipment Intelligent Operation and Maintenance -
2023-09-21 China Hefei
2023 International Conference on Equipment Intelligent Operation and Maintenance
