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Real-Time Monitoring for Hydraulic States Based on Convolutional Bidirectional LSTM with Attention Mechanism
| Content Provider | MDPI |
|---|---|
| Author | Kim, Kyutae Jeong, Jongpil |
| Copyright Year | 2020 |
| Description | By monitoring a hydraulic system using artificial intelligence, we can detect anomalous data in a manufacturing workshop. In addition, by analyzing the anomalous data, we can diagnose faults and prevent failures. However, artificial intelligence, especially deep learning, needs to learn much data, and it is often difficult to get enough data at the real manufacturing site. In this paper, we apply augmentation to increase the amount of data. In addition, we propose real-time monitoring based on a deep-learning model that uses convergence of a convolutional neural network (CNN), a bidirectional long short-term memory network (BiLSTM), and an attention mechanism. CNN extracts features from input data, and BiLSTM learns feature information. The learned information is then fed to the sigmoid classifier to find out if it is normal or abnormal. Experimental results show that the proposed model works better than other deep-learning models, such as CNN or long short-term memory (LSTM). |
| Starting Page | 7099 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s20247099 |
| Journal | Sensors |
| Issue Number | 24 |
| Volume Number | 20 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-12-11 |
| Access Restriction | Open |
| Subject Keyword | Sensors Industrial Engineering Hydraulic System Cnn Bidirectional Lstm Attention Mechanism Classification Data Augmentation |
| Content Type | Text |
| Resource Type | Article |
