Selected article for: "accurately predict and long short"
Author: Kim, Kyutae; Jeong, Jongpil
Title: A Hydraulic Condition Monitoring System Based on Convolutional BiLSTM Model
  • Cord-id: 3dnlmizz
  • Document date: 2020_8_19
    • ID: 3dnlmizz
    Snippet: In this paper, to monitor the conditions of hydraulic system, a real-time monitoring method based on convergence of convolutional neural networks (CNN) and a bidirectional long short-term memory networks (BiLSTM) is proposed. This method uses CNN and BiLSTM. In the CNN, the feature is extracted from the time-series data entered as an input, and in the BiLSTM, information from the feature is learned. Then, the learned information is sent to the Sigmoid classifier and it classified whether the sys
    Document: In this paper, to monitor the conditions of hydraulic system, a real-time monitoring method based on convergence of convolutional neural networks (CNN) and a bidirectional long short-term memory networks (BiLSTM) is proposed. This method uses CNN and BiLSTM. In the CNN, the feature is extracted from the time-series data entered as an input, and in the BiLSTM, information from the feature is learned. Then, the learned information is sent to the Sigmoid classifier and it classified whether the system is stable or unstable. The experimental results show that compared to other deep learning models, this model can more accurately predict the conditions of the hydraulic system with the data collected by the sensors.

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