Open Access
Manufacturing Rev.
Volume 9, 2022
Article Number 21
Number of page(s) 10
Published online 02 August 2022
  1. T. Thamcharoen, P. Chommuangpuck, J. Deeying, J. Srisertpol, Fault detection and classification for slider attachment process using convolution neural network, Int. J. Neural Netw. Adv. Appl. 7 (2020) 60–65 [Google Scholar]
  2. P. Chommuangpuck, T. Wanglomklang, S. Tantrairatn, J. Srisertpol, Fault tolerant control based on an observer on PI servo design for a high-speed automation machine, Machines 8 (2020) 22 [CrossRef] [Google Scholar]
  3. P. Chommuangpuck, T. Wanglomklang, J. Srisertpol, Fault detection and diagnosis of linear bearing in auto core adhesion mounting machines based on condition monitoring, Syst. Sci. Control Eng. 9 (2021) 290–303 [CrossRef] [Google Scholar]
  4. J. Deeying, K. Asawarungsaengkul, P. Chutima, Multi-objective optimization on laser solder jet bonding process in head gimbal assembly using the response surface methodology, Opt. Laser Technol. 98 (2018) 158–168 [CrossRef] [Google Scholar]
  5. X. Xu et al., Application of neural network algorithm in fault diagnosis of mechanical intelligence, Mech. Syst. Signal Process. (2020) 106625 [CrossRef] [Google Scholar]
  6. S.-Y. Lin, S.-C. Horng, A classification-based fault detection and isolation scheme for the ion implanter, IEEE Trans. Semiconduct. Manufactur. 19 (2006) 411–424 [CrossRef] [Google Scholar]
  7. J.C.M. Oliveira et al., Fault detection and diagnosis in dynamic systems using weightless neural networks, Exp. Syst. Appl. 84 (2017) 200–219 [CrossRef] [Google Scholar]
  8. M. Krysander, E. Frisk, Leakage detection in a fuel evaporative system, Control Eng. Pract. 17 (2009) 1273–1279 [CrossRef] [Google Scholar]
  9. P. Manescu et al., Accurate and interpretable classification of microspectroscopy pixels using artificial neural networks, Med. Image Anal. 37 (2017) 37–45 [CrossRef] [Google Scholar]
  10. A. Jain et al., Intercircuit and cross-country fault detection and classification using artificial neural network, in 2010 Annual IEEE India Conference (INDICON). IEEE (2010) [Google Scholar]
  11. V. Veeriah, N. Zhuang, G.-J. Qi, Differential recurrent neural networks for action recognition, in Proceedings of the IEEE international conference on computer vision (2015) [Google Scholar]
  12. R. Chauhan, K. Kumar Ghanshala, R.C. Joshi, Convolutional Neural Network (CNN) for image detection and recognition, in 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC), IEEE (2018) [Google Scholar]
  13. D. Dey et al., A deep learning framework using convolution neural network for classification of impulse fault patterns in transformers with increased accuracy, IEEE Trans. Dielectr. Electr. Insulat. 24 (2017) 3894–3897 [CrossRef] [Google Scholar]
  14. S. Yokomichi et al., Development of diagnostic methods for vacuum leakage from vacuum interrupter by partial discharge detection, in 2016 27th International Symposium on Discharges and Electrical Insulation in Vacuum (ISDEIV). Vol. 2. IEEE (2016) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.