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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2018 Vol.8(5): 416-422 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2018.8.5.722

Senvis-Net: Learning from Imbalanced Machinery Data by Transferring Visual Element Detectors

Qingwei Guo, Yoshinori Miyamae, Zhongjun Wang, Koji Taniuchi, Huazhong Yang, and Yongpan Liu
Abstract—With the development of sensor network technologies and the popularization of Industry 4.0, data-driven machine health monitoring has become increasingly important, not only to save maintenance costs of factory machinery, but also to guarantee the safety of factories. However, traditional data-driven algorithms are limited by two aspects. Firstly, the information fusion of multiple sensors heavily relies on domain knowledge. Secondly, imbalanced distribution of machinery data brings a challenge for the machine learning algorithm performance. In order to tackle these issues, we propose a general methodology to organize collected sensor data into image form and utilize visual element detectors learned by a pre-trained convnet to explore meaningful information hidden in data. We also design a Convolutional Neural Network (CNN) model, named Senvis-Net. Applied to an imbalance learning task of remaining useful life (RUL) prediction, our model outperforms the state-of-the-art CNN that learns directly from sensor data. Moreover, transferring visual element detectors can bring another 20.1% ~ 97% performance benefits depending on severity of imbalance.

Index Terms—Machine health monitoring, convolutional neural network, imbalanced data, transfer learning.

Qingwei Guo, Huazhong Yang, Yongpan Liu are with Tsinghua University, China (e-mail: gqw15@mails.tsinghua.edu.cn, yanghz@tsinghua.edu.cn, ypliu@tsinghua.edu.cn).
Yoshinori Miyamae, Zhongjun Wang, Koji Taniuchi are with ROHM Semiconductor, Japan (e-mail: Yoshinori.Miyamae@dsn.rohm.co.jp, zhongjun.wang@res.rohmchina.com.cn, koji.taniuchi@dsn.rohm.co.jp).

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Cite: Qingwei Guo, Yoshinori Miyamae, Zhongjun Wang, Koji Taniuchi, Huazhong Yang, and Yongpan Liu, "Senvis-Net: Learning from Imbalanced Machinery Data by Transferring Visual Element Detectors," International Journal of Machine Learning and Computing vol. 8, no. 5, pp. 416-422, 2018.

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