Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks
In modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and da...
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| Format: | Article |
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MDPI AG
2017-01-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/17/2/273 |
| _version_ | 1798024548507975680 |
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| author | Rui Zhao Ruqiang Yan Jinjiang Wang Kezhi Mao |
| author_facet | Rui Zhao Ruqiang Yan Jinjiang Wang Kezhi Mao |
| author_sort | Rui Zhao |
| collection | DOAJ |
| description | In modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, considering the noise, varying length and irregular sampling behind sensory data, this kind of sequential data cannot be fed into classification and regression models directly. Therefore, previous work focuses on feature extraction/fusion methods requiring expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, which redefine representation learning from raw data, a deep neural network structure named Convolutional Bi-directional Long Short-Term Memory networks (CBLSTM) has been designed here to address raw sensory data. CBLSTM firstly uses CNN to extract local features that are robust and informative from the sequential input. Then, bi-directional LSTM is introduced to encode temporal information. Long Short-Term Memory networks(LSTMs) are able to capture long-term dependencies and model sequential data, and the bi-directional structure enables the capture of past and future contexts. Stacked, fully-connected layers and the linear regression layer are built on top of bi-directional LSTMs to predict the target value. Here, a real-life tool wear test is introduced, and our proposed CBLSTM is able to predict the actual tool wear based on raw sensory data. The experimental results have shown that our model is able to outperform several state-of-the-art baseline methods. |
| first_indexed | 2024-04-11T18:04:26Z |
| format | Article |
| id | doaj.art-a02aac83bdbd42cd91521208fa36a99a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T18:04:26Z |
| publishDate | 2017-01-01 |
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| spelling | doaj.art-a02aac83bdbd42cd91521208fa36a99a2022-12-22T04:10:22ZengMDPI AGSensors1424-82202017-01-0117227310.3390/s17020273s17020273Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM NetworksRui Zhao0Ruqiang Yan1Jinjiang Wang2Kezhi Mao3School of Instrument Science and Engineering, Southeast University, Nanjing 210009, ChinaSchool of Instrument Science and Engineering, Southeast University, Nanjing 210009, ChinaSchool of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, ChinaSchool of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, SingaporeIn modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, considering the noise, varying length and irregular sampling behind sensory data, this kind of sequential data cannot be fed into classification and regression models directly. Therefore, previous work focuses on feature extraction/fusion methods requiring expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, which redefine representation learning from raw data, a deep neural network structure named Convolutional Bi-directional Long Short-Term Memory networks (CBLSTM) has been designed here to address raw sensory data. CBLSTM firstly uses CNN to extract local features that are robust and informative from the sequential input. Then, bi-directional LSTM is introduced to encode temporal information. Long Short-Term Memory networks(LSTMs) are able to capture long-term dependencies and model sequential data, and the bi-directional structure enables the capture of past and future contexts. Stacked, fully-connected layers and the linear regression layer are built on top of bi-directional LSTMs to predict the target value. Here, a real-life tool wear test is introduced, and our proposed CBLSTM is able to predict the actual tool wear based on raw sensory data. The experimental results have shown that our model is able to outperform several state-of-the-art baseline methods.http://www.mdpi.com/1424-8220/17/2/273machine health monitoringtool wear predictionconvolutional neural networkrecurrent neural networkbi-directional long-short term memory network |
| spellingShingle | Rui Zhao Ruqiang Yan Jinjiang Wang Kezhi Mao Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks Sensors machine health monitoring tool wear prediction convolutional neural network recurrent neural network bi-directional long-short term memory network |
| title | Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks |
| title_full | Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks |
| title_fullStr | Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks |
| title_full_unstemmed | Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks |
| title_short | Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks |
| title_sort | learning to monitor machine health with convolutional bi directional lstm networks |
| topic | machine health monitoring tool wear prediction convolutional neural network recurrent neural network bi-directional long-short term memory network |
| url | http://www.mdpi.com/1424-8220/17/2/273 |
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