Fault Diagnosis for 3D Printers Using Suboptimal Networked Deep Learning

被引：1
|
作者：
Li C. [1 ,2 ,3 ]
Zhang S. [2 ]
De Oliveira J.V. [3 ]
机构：
[1] National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing
[2] School of Mechanical & Automobile Engineering, South China University of Technology, Guangzhou
[3] CEOT, Universidade do Algarve, Faro
来源：
Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2019年 / 55卷 / 07期
关键词：
3D printing; Big data; Deep learning; Fault diagnosis; Suboptimal network;
D O I：
10.3901/JME.2019.07.073
中图分类号：
学科分类号：
摘要：
For reliable and cost-saving diagnosis of 3D printer faults, a customer-goods level attitude sensor is employed to collect healthy condition data of the printer. To make up for the inferior precision of the low-cost hardware, a suboptimal networked deep learning (SNDL) method is developed. In addition to conventional deep learning architecture composed of pre-training and fine-tuning, there are two new merits for SNDL. A pre-classifying strategy is first proposed to adaptively determine the suboptimal network structure. A fine-classifying approach is on the other hand adopted for further improving fault classification performance. In the experiments, the attitude sensor is installed on the end of the transmission chain (i.e., the printing head of the delta 3D printer). Motion signals collected from all channels of the sensor are used as the input vector of the suboptimal networks, which are developed by deep Boltzmann machines for the big data-driven fault diagnosis. The addressed SNDL method is compared with peer methods. The results show that the present SNDL is capable of effectively diagnosing transmission faults for the 3D printer. © 2019 Journal of Mechanical Engineering.
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页码：73 / 80
页数：7
相关论文
共 18 条
  • [1] Mohammed M.I., Gibson I., Design of three-dimensional, triply periodic unit cell scaffold structures for additive manufacturing, Journal of Mechanical Design, 140, 7, (2018)
  • [2] Chang D., Fang Y., Ye W., Design and analysis of a double output 3D printing decoupling parallel manipulator, Journal of Mechanical Engineering, 53, 7, pp. 39-46, (2017)
  • [3] Miller D.B., Glisson W.B., Yampolskiy M., Et al., Identifying 3D printer residual data via open-source documentation, Computers & Security, 75, pp. 10-23, (2018)
  • [4] Jia F., Lei Y.G., Lin J., Et al., Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data, Mechanical Systems and Signal Processing, 72-73, pp. 303-315, (2016)
  • [5] Li C., Zhu R., Yang S., Optimal frequency band demodulation for fault diagnosis of rolling element bearings based on fuzzy fusion of multiple criteria, Journal of Mechanical Engineering, 51, 7, pp. 107-114, (2015)
  • [6] Shao H., Jiang H., Zhao H., Et al., A novel deep autoencoder feature learning method for rotating machinery fault diagnosis, Mechanical Systems and Signal Processing, 95, pp. 187-204, (2017)
  • [7] Mao Y., Qin S., Qin Y., Demodulation based on harmonic wavelet and its application into rotary machinery fault diagnosis, Chinese Journal of Mechanical Engineering, 23, 3, pp. 419-425, (2009)
  • [8] Ding X., He Q., Energy-fluctuated multiscale feature learning with deep ConvNet for intelligent spindle bearing fault diagnosis, IEEE Transactions on Instrumentation and Measurement, 66, 8, pp. 1926-1935, (2017)
  • [9] Jing Y., Liu C., Bi F., Et al., Diesel engine valve clearance fault diagnosis based on features extraction techniques and FastICA-SVM, Chinese Journal of Mechanical Engineering, 30, 4, pp. 991-1007, (2017)
  • [10] Shen C.Q., Wang D., Kong F.R., Et al., Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier, Measurement, 46, pp. 1551-1564, (2013)
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