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Sensors 2017, 17(2), 273; doi:10.3390/s17020273

Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks

1
School of Instrument Science and Engineering, Southeast University, Nanjing 210009, China
2
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
3
School of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, China
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 24 November 2016 / Revised: 12 January 2017 / Accepted: 24 January 2017 / Published: 30 January 2017
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Abstract

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. View Full-Text
Keywords: machine health monitoring; tool wear prediction; convolutional neural network; recurrent neural network; bi-directional long-short term memory network machine health monitoring; tool wear prediction; convolutional neural network; recurrent neural network; bi-directional long-short term memory network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhao, R.; Yan, R.; Wang, J.; Mao, K. Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks. Sensors 2017, 17, 273.

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