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Real-Time Monitoring for Hydraulic States Based on Convolutional Bidirectional LSTM with Attention Mechanism

Kyutae Kim1, Jongpil Jeong1

  • 1Department of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Korea.

Sensors (Basel, Switzerland)
|December 16, 2020
PubMed
Summary

Artificial intelligence (AI) can detect hydraulic system anomalies in manufacturing. This study introduces a deep learning model using data augmentation to improve fault detection and prevent failures, outperforming existing methods.

Keywords:
CNNattention mechanismbidirectional LSTMclassificationdata augmentationhydraulic system

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