[1] XIA J Z, LIU Y H, LENG Y G, et al. Analysis of methods of weak signal detection [J]. Noise and Vibration Control, 2011, 31(3): 156-161 (in Chinese).
[2] L¨U Y, LI Y R,WANG Z G, et al. Research on a extraction method for weak fault signal and its application[J]. Journal of Vibration Engineering, 2007, 20(1): 24-28 (in Chinese).
[3] YE Q H, HUANG H N, ZHANG C H. Design of stochastic resonance systems in weak signal detection [J]. Acta Electronica Sinica, 2009, 37(1): 216-220 (in Chinese).
[4] LIN J, QU L S. Feature detection and fault diagnosis based on continuous wavelet transform [J]. Chinese Journal of Mechanical Engineering, 2000, 36(12): 95-100 (in Chinese).
[5] WANG T Y, HE H L, WANG G F, et al. Rollingbearings fault diagnosis based-on empirical mode decomposition and least square support vector machine[J]. Chinese Journal of Mechanical Engineering, 2007,43(4): 88-92 (in Chinese).
[6] LI C Y, XU M Q, GUO S. Fault diagnosis of rolling element bearing based on principal component analysis of acoustic signal [J]. Technical Acoustics, 2008, 27(2):271-274 (in Chinese).
[7] CHENG J S, ZHANG K, YANG Y. Local mean decomposition method and its application to roller bearing fault diagnosis [J]. China Mechanical Engineering,2009, 20(22): 2711-2717 (in Chinese).
[8] BENZI R, SUTERA A, VULPIANI A. The mechanism of stochastic resonance [J]. Journal of Physics A:Mathematical and General, 1981, 14(11): L453-L457.
[9] HU N Q, CHEN M, WEN X S. Application of stochastic resonance theory for early detecting rub-impact fault of rotor system [J]. Chinese Journal of Mechanical Engineering, 2001, 37(9): 88-91 (in Chinese).
[10] LENG Y G, WANG T Y. Numerical research of twice sampling stochastic resonance for the detection of a weak signal submerged in a heavy noise [J]. Acta Physica Sinica, 2003, 52(10): 2432-2437 (in Chinese).
[11] CHEN M, HU N Q, QIN G J, et al. Application of parameter-tuning stochastic resonance for detecting early mechanical faults [J]. Journal of Mechanical Engineering, 2009, 45(4): 131-135 (in Chinese).
[12] TAN J Y, CHEN X F, LEI Y G, et al. Adaptive frequency-shifted and re-scaling stochastic resonance with applications to fault diagnosis [J]. Journal of Xi’an Jiaotong University, 2009, 43(7): 69-73 (in Chinese).
[13] LEI Y G, HAN D, LIN J, et al. New adaptive stochastic resonance method and its application to fault diagnosis[J]. Journal of Mechanical Engineering, 2012, 48(7):62-67 (in Chinese).
[14] ZHOU Y F, WANG H J, ZUO Y B, et al. Bearing fault diagnosis based on scale transformation stochastic resonance [J]. Journal of Beijing Information Science & Technology University, 2015, 30(6): 68-72 (in Chinese).
[15] QIAO Z J, LEI Y G, LIN J, et al. An adaptive unsaturated bistable stochastic resonance method and its application in mechanical fault diagnosis [J]. Mechanical Systems and Signal Processing, 2017, 84: 731-746.
[16] KENNEDY J, EBERHART R. Particle swarm optimization [C]//International Conference on Neural Networks. Perth, Australia: IEEE, 1995: 1942-1948.
[17] LI J M, CHEN X F, HE Z J. Adaptive monostable stochastic resonance based on PSO with application in impact signal detection [J]. Journal of Mechanical Engineering, 2011, 47 (21): 58-63 (in Chinese).
[18] LI Y B, ZHANG B L, LIU Z X, et al. Adaptive stochastic resonance method based on quantum particle swarm optimization [J]. Acta Physica Sinica, 2014, 63 (16): 40-47 (in Chinese).
[19] WANG Z X, GUO L. Research on weak signal detection method based based on adaptive stochastic resonance[J]. Computer Measurement & Control, 2018,26(1): 42-46 (in Chinese).
[20] XIAO L, XIA T B, PAN E S, et al. A novel weak bearing fault detection method based on vibrational resonance [C]//2018 Prognostics and System Health Management Conference (PHM-Chongqing). Chongqing,China: IEEE, 2018: 100-104.
[21] BECHHOEFER E. Condition based maintenance fault database for testing of diagnostic and prognostics algorithms [EB/OL]. [2019-08-28].https://www.mfpt.org/fault-data-sets/.