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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sg-ntu-dr.10356-872462020-03-07T13:57:27Z Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks Zhao, Rui Yan, Ruqiang Wang, Jinjiang Mao, Kezhi School of Electrical and Electronic Engineering Machine Health Monitoring Tool Wear Prediction 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. Published version 2018-01-24T03:59:03Z 2019-12-06T16:38:04Z 2018-01-24T03:59:03Z 2019-12-06T16:38:04Z 2017 Journal Article Zhao, R., Yan, R., Wang, J., & Mao, K. (2017). Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks. Sensors, 17(2), 273-. https://hdl.handle.net/10356/87246 http://hdl.handle.net/10220/44339 10.3390/s17020273 en Sensors © 2017 The Author(s); licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/). 18 p. application/pdf |
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Machine Health Monitoring Tool Wear Prediction Zhao, Rui Yan, Ruqiang Wang, Jinjiang Mao, Kezhi Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks |
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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. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Zhao, Rui Yan, Ruqiang Wang, Jinjiang Mao, Kezhi |
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Article |
author |
Zhao, Rui Yan, Ruqiang Wang, Jinjiang Mao, Kezhi |
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Zhao, Rui |
title |
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_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 |
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Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks |
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learning to monitor machine health with convolutional bi-directional lstm networks |
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2018 |
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https://hdl.handle.net/10356/87246 http://hdl.handle.net/10220/44339 |
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