Bubble Detection in Lithium-ion Polymer Cell Sheet Using Extreme Learning Machine

Author(s): Liyong Ma*, Chengkuan Ma, Lidan Tang.

Journal Name: Recent Patents on Engineering

Volume 13 , Issue 1 , 2019

Become EABM
Become Reviewer

Graphical Abstract:


Abstract:

Background: As lithium-ion polymer battery has high energy density and it is easy to be manufactured into different shapes, it arouses more interests of both technology and application recently. The quality of the lithium-ion polymer battery is essential to all the applications, and the detection of bubble defect in cell sheets is critical to the quality control of batteries. Recent patents on flaw detection in cell sheet are reviewed.

Method: A novel application is developed to detect bubble defect in cell sheets of lithium-ion polymer battery by using extreme learning machine. The image processing methods and the selected features for bubble detection are detailed. Gaussian mixture model density estimation for extreme learning machine is developed to solve the problem of lack of enough flaw samples for classification learning.

Results: The comparison of classification correction rate of different methods showed that the classification accuracy of the proposed method was between 99% and 100%. The proposed method was able to keep the superior performance of accuracy with the different sample numbers, and it had most satisfactory performance with varies of sample number. Experimental results also showed that the number of nodes in the hidden layer had little influence on the classification accuracy in the proposed method.

Conclusion: All these experiments have shown that the proposed method has the best performance and the proposed bubble detection method is more efficient than other learning-based methods, and the proposed method has the potential to defect detection in other image processing applications.

Keywords: Bubble detection, defect detection, density estimation, extreme learning machine, gaussian mixture model, lithiumion polymer battery.

[1]
A.M. Stephan, and K.S. Nahm, "Review on composite polymer electrolytes for lithium batteries", Polymer., vol. 47, pp. 5952-5964, 2006.
[2]
H.G. Bu, J. Wang, and X.B. Huang, "Fabric defect detection based on multiple fractal features and support vector data description", Eng. Appl. Artif. Intell., vol. 22, pp. 224-235, 2009.
[3]
S. Nashat, A. Abdullah, and M.Z. Abdullah, "Machine vision for crack in spection of biscuits featuring pyramid detection scheme", J. Food Eng., vol. 120, pp. 233-247, 2014.
[4]
C.C. Wang, B.C. Jiang, J.Y. Lin, and C.C. Chu, "Machine vision-based defect detection in IC images using the partial information correlation coefficient", IEEE Trans. Semicond. Manuf., vol. 26, pp. 378-384, 2013.
[5]
M. Israil, S.A. Anwar, and M.Z. Abdullah, "Automatic detection of micro-crack in solar wafers and cells: A review", Trans. Inst. Meas. Contr., vol. 35, pp. 606-618, 2013.
[6]
D. Weimer, H. Thamer, and B. Scholz, "Reiter, “Learning defect classifiers for textured surfaces using neural networks and statistical feature representations", Procedia CIRP, vol. 7, pp. 347-352, 2013.
[7]
S. Nashat, A. Abdullah, S. Aramvith, and M.Z. Abdullah, "Support vector machine approach to real-time inspection of biscuits on moving conveyor belt", Comput. Electron. Agric., vol. 75, pp. 147-158, 2011.
[8]
R. Shanmugamani, M. Sadique, and B. Ramamoorthy, "Detection and classification of surface defects of gun barrels using computer vision and machine learning", Measurement, vol. 60, pp. 222-230, 2015.
[9]
W. Mu, J. Gao, H. Jiang, Z. Wang, F. Chen, and C. Dang, "Automatic classification approach to weld defects based on PCA and SVM", Insight Non. Destr. Test. Cond. Monit, vol. 55, pp. 535-539, 2013.
[10]
L. Ma, "Support Tucker machines based marine oil spill detection using SAR images", Indian J. Mar. Sci., vol. 45, pp. 1445-1449, 2016.
[11]
L. Ma, Y. Hu, and Y. Zhang, "Support Tucker machines based bubble defect detection of lithium-ion polymer cell sheets", Eng. Lett, vol. 25, pp. 46-51, 2017.
[12]
S. Scheller, T. Hundert, and M. Braun, Detection system for detecting a soldered joint. U.S. Patent 20,160,131,598, 2013.
[13]
X. Huang, Y. Zhou, S. Liu, W. Liu, and C. Liu, Detection method of solder joint void defects based on laser pulse excitation simulation. CN Patent 102,015,000,417,524, 2015.
[14]
K. Stepanovich, P. Evgenevich, K. Yurevich, B. Anatolevich, F. Vladimirovich, and K. Aleksandrovich, Method of laser-ultrasound quality control of soldered joints. RU Patent 0,002,545,348, 2013.
[15]
L. Zhou, D. Zhang, H. Shen, and R. Lu, System and method for detection soldering quality. CN Patent 2017/050924, 2017.
[16]
J. Lin, Solder joint type detection method and apparatus based on image identification. CN Patent 10,201,600,091,842, 2016.
[17]
G.B. Huang, Q.Y. Zhu, and C.K. Siew, "Extreme learning machine: theory and applications", Neurocomputing, vol. 70, pp. 489-501, 2016.
[18]
G. Huang, G.B. Huang, S. Song, and K. You, "Trends in extreme learning machines: A review", Neural Netw., vol. 61, pp. 32-48, 2015.
[19]
G.B. Huang, H. Zhou, X. Ding, and R. Zhang, "Extreme learning machine for regression and multiclass classification", IEEE Trans. Syst. Man Cybern. Part B Cybern., vol. 42, pp. 513-529, 2012.
[20]
S. Wang, C. Deng, W. Lin, and G.B. Huang, "NMF-based image quality assessment using extreme learning machine", IEEE Trans. Cybern ., vol. 47, pp. 232-243, . January 2017
[21]
T. Yksel, "Intelligent visual servoing with extreme learning machine and fuzzy logic", Expert Syst. Appl., vol. 72, pp. 344-356, 2017.
[22]
X. Liu, C. Deng, S. Wang, G.B. Huang, B. Zhao, and P. Lauren, "Fast and accurate spatiotemporal fusion based upon extreme learning machine", IEEE Geosci. Remote Sens. Lett., vol. 13, pp. 2039-2043, 2016.
[23]
L. Ma, C. Ma, W. Xie, and M. Sun, Detection method on defection of electronics component solder joint. CN Patent 2,017,113,389,990, 2017.
[24]
N. Dalal, and B. Triggs, "Histograms of oriented gradients for human detection", Proc. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA, IEEE, pp. 886-893, 2005.
[25]
W. Zong, G.B. Huang, and Y. Chen, "Weighted extreme learning machine for imbalance learning", Neurocomputing, vol. 101, pp. 229-242, 2013.
[26]
J. Yang, H. Yu, X. Yang, and X. Zuo, "Imbalanced extreme learning machine based on probability density estimation", Lect. Notes Comput. Sci., vol. 9426, pp. 160-167, 2015.
[27]
Y. He, R. Ashfaq, J.Z. Huang, and X. Wang, "Imbalanced ELM based on normal density estimation for binary-class classification", Lect. Notes Comput. Sci., vol. 9794, pp. 48-60, 2016.
[28]
R.A. Redner, and H.F. Walker, "Mixture densities, maximum likelihood and the EM algorithm", SIAM Rev., vol. 26, pp. 195-239, 1984.
[29]
G. McLachlan, and D. Peel, Finite Mixture Models New York., Springer-Verlag, 2000.


Rights & PermissionsPrintExport Cite as


Article Details

VOLUME: 13
ISSUE: 1
Year: 2019
Page: [75 - 82]
Pages: 8
DOI: 10.2174/1872212112666180522082329
Price: $58

Article Metrics

PDF: 11
HTML: 1