THE FAULT DIAGNOSIS METHOD BASED ON JOINT APPROXIMATE DIAGONALIZATION OF EIGEN-MATRICES AND STACKED AUTO-ENCODER

Yuanfang Xin,∗ Yuanyuan Jiang,∗ and Yanbin Liu∗∗

References

  1. [1] C. Fenglin, L. Yongbin, F. Jian et al., Machine status feature extraction method based on EMD and JADE, Computer Engineering, 41(7), 2015, 305–309.
  2. [2] L.L.C. Kasun, Y. Yang, G.B. Huang et al., Dimension reduction with extreme learning machine, IEEE Transactions on Image Processing, 25(8), 2016, 3906–3918.
  3. [3] L. Wang, X. Zhou, Y. Xing et al., Clustering ECG heartbeat using improved semi-supervised affinity propagation, IET Software, 11(5), 2017, 207–213.
  4. [4] Q. Gong and P.-F. Tang, Uncorrelated locality preserving projections analysis based on maximum margin criterion, Acta Automatica Sinca, 39(9), 2013, 1575–1580.
  5. [5] Y. Liu, B. He, F. Liu et al., Feature fusion using kernel joint approximate diagonalization of eigen-matrices for rolling bearing fault identification, Journal of Sound and Vibration, 385, 2016, 389–401.
  6. [6] J. Cui, J. Tang, C. Gong, and Z. Zhang, A fault feature extraction method of aerospace generator rotating rectifier based on improved stacked auto-encoder, Proceedings of the CSEE, 37(19), 2017, 5696–5706.
  7. [7] Q. Lü, Y. Dou, X. Niu, J. Xu, and F. Xia, Remote sensing image classification based on DBN model, Journal of Computer Research and Development, 51(9), 2014, 1911–1918. 243
  8. [8] Y. Qian, X. Ding, T. Liu et al., Identification method of user’s travel consumption intention in chatting robot, Social Science Information, 47, 2017, 997–1007.
  9. [9] Y.-J. Duan, Y.-S. Lv, J. Zhang, X.-L. Zhao, and F.-Y. Wang. Deep learning for control: The state of the art and prospects, Acta Automatica Sinica, 42(5), 2016, 643–654.
  10. [10] L. Wen, X. Li, L. Gao et al., A new convolutional neural network-based data-driven fault diagnosis method, IEEE Transactions on Industrial Electronics, 65(7), 2018, 5990–5998.
  11. [11] Z. Zhou, G. Huang, J. Gao, and X. Man, Radar emitter identificational algorithm based on deep learning, Journal of Xidian University, 44(3), 2017, 77–82.
  12. [12] J.W. Shang, C.K. Wang, X. Xin, and X. Ying, Community detection algorithm based on deep sparse autoencoder, Ruan Jian Xue Bao/Journal of Software, 28(3), 2017, 648–662. http://www.jos.org.cn/1000-9825/5165.htm.
  13. [13] L. Qiu, T. Liu, N. Lin, and Z. Huang, Data aggregation in wireless sensor network based on deep learning model, Chinese Journal of Sensors and Actuators, 27(12), 2014, 1704–1709.
  14. [14] N. Li, Y. Li, X. Zhu, H. Lei, and J. Yu, Fault diagnosis for new inverter circuits based on mixed logic dynamic model and incident identification vector, Power System Technology, 37(10), 2013, 2808–2813.
  15. [15] Y. Xia, J. Roy, and R. Ayyanar, A capacitance-minimized, doubly grounded transformer less photovoltaic inverter with inherent active-power decoupling, IEEE Transactions on Power Electronics, 32(7), 2017, 5188–5201.
  16. [16] Y. Jiang, Y. Wang, Y. Wu, Q. Sun, and H. Luo, Online multiple fault diagnosis for PV inverter based on wavelet packet energy spectrum and extreme learning machine, Chinese Journal of Scientific Instrument, 36(9), 2015, 2145–2152.

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