[1] Jiang G Q, He H B, Yan J et al. Multiscale convolutional neural networks for fault diagnosis of wind turbine gearbox[J]. IEEE Transactions on Industrial Electronics, 66, 3196-3207(2019).

[2] Yang Q, Hu C Z, Zheng N G. Data-driven diagnosis of nonlinearly mixed mechanical faults in wind turbine gearbox[J]. IEEE Internet of Things Journal, 5, 466-467(2018).

[3] Zhou W, Liang Q. Real-time spectrum analysis algorithm for non-stationary signal and it's implementation on FPGA platforms[J]. Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition), 30, 633-641(2018).

[4] Wang J, Cheng F Z, Qiao W et al. Multiscale filtering reconstruction for wind turbine gearbox fault diagnosis under varying-speed and noisy conditions[J]. IEEE Transactions on Industrial Electronics, 65, 4268-4278(2018).

[5] Zhang M, Wang K S, Wei D D et al. Amplitudes of characteristic frequencies for fault diagnosis of planetary gearbox[J]. Journal of Sound and Vibration, 432, 119-132(2018).

[6] Chen Y, Wu C T, Liu H L. EMD self-adaptive selecting relevant modes algorithm for FBG spectrum signal[J]. Optical Fiber Technology, 36, 63-67(2017).

[7] Amarnath M. Praveen Krishna I R. Local fault detection in helical gears via vibration and acoustic signals using EMD based statistical parameter analysis[J]. Measurement, 58, 154-164(2014).

[8] Pan P, Xi L X, Zhang X G et al. Experimental research on polarization mode dispersion measurement based on empirical mode decomposition[J]. Chinese Journal of Lasers, 45, 0106002(2018).

[9] Chen J Y, Zhou D, Liu C et al. An integrated method based on CEEMD-SampEn and the correlation analysis algorithm for the fault diagnosis of a gearbox under different working conditions[J]. Mechanical Systems and Signal Processing, 113, 102-111(2018).

[10] Zhao C R, Jin X F, Ni D C et al. Optical fiber rotational speed sensor based on plastic optical fiber and optical prism[J]. Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition), 28, 383-388(2016).

[11] Chen Y, Liu B L, Liu H L et al. Load location and measurement system based on fiber Bragg grating sensor[J]. Chinese Journal of Mechanical Engineering, 52, 8-15(2016).

[12] Chen Y, Zhou L X, Liu H L. A fiber Bragg grating sensor perimeter intrusion localization method optimized by improved particle swarm optimization algorithm[J]. IEEE Sensors Journal, 18, 1243-1249(2018).

[13] Liu H L, Wang C J, Chen Y. An improved genetic algorithm for increasing the addressing accuracy of encoding fiber Bragg grating sensor network[J]. Optical Fiber Technology, 40, 28-35(2018).

[14] He C B, Cai S K, Sun D K et al. Fault diagnosis of wind turbine planetary gearbox based on order analysis and divergence index[J]. The Journal of Engineering, 2017, 1394-1398(2017).

[15] Huang N E, Shen Z, Long S R et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London Series A: Mathematical, Physical and Engineering Sciences, 454, 903-995(1998).

[16] Lian J J, Rong Q B, Dong X F et al. Structural model parameter identification method based on an improved HHT for suppressing mode mixing[J]. Journal of Vibration and Shock, 37, 1-8(2018).

[17] Zhang X, Cui W, Liu Y L. Recovery of structured signals with prior information via maximizing correlation[J]. IEEE Transactions on Signal Processing, 66, 3296-3310(2018).

[18] Zhang L, Mao Z D, Yang S X et al. An improved Kurtogram based on band-pass envelope spectral Kurtosis with its application in bearing fault diagnosis[J]. Journal of Vibration and Shock, 37, 171-179(2018).

[19] Pan X L, Yang Z J, Xu Y T et al. Safe screening rules for accelerating twin support vector machine classification[J]. IEEE Transactions on Neural Networks and Learning Systems, 29, 1876-1887(2018).

[20] Chen Y, Liu Z Q, Liu H L. A method of fiber Bragg grating sensing signal de-noise based on compressive sensing[J]. IEEE Access, 6, 28318-28327(2018).

[21] Geng R, Wang X J, Ye N et al. A fault prediction algorithm based on rough sets and back propagation neural network for vehicular networks[J]. IEEE Access, 6, 74984-74992(2018).