[1] Y Xia, Z R Zhang, B L Shang, et al. Fault diagnosis for ICE based on image processing and neural networks. Transactions of CSICE (Chinese Society for Internal Combustion Engines), 2001, 19(4): 356-360. (in Chinese).
[2] Z Li, X C Cheng, Z B Liu. Study of diagnosis methods for diesel's valve train faults based on picture processing and neural networks. Transactions of CSICE (Chinese Society for Internal Combustion Engines), 2001, 19(3): 241-244. (in Chinese).
[3] H B Zheng, Z Y Li, X Z Chen, et al. Engine knock signature analysis and fault diagnosis based on time-frequency distribution. Transactions of CSICE (Chinese Society for Internal Combustion Engines), 2002, 20(3): 267-272. (in Chinese).
[4] Z M Geng, J Chen, J B Hull. Analysis of engine vibration and design of an applicable diagnosing approach. International Journal of Mechanical Sciences, 2003, 45(8): 1391-1410.
[5] Z Geng, J Chen. Investigation into piston-slap-induced vibration for engine condition simulation and monitoring. Journal of Sound and Vibration, 2005, 282(3-5): 735-751.
[6] M Li, J H Yang, X J Wang. Fault feature extraction of rolling bearing based on an improved cyclical spectrum density method. Chinese Journal of Mechanical Engineering, 2015, 28(6): 1240-1247.
[7] K Zhang, Y Dong, A Ball. Feature selection by merging sequential bidirectional search into relevance vector machine in condition monitoring. Chinese Journal of Mechanical Engineering, 2015, 28(6):1248-1253.
[8] G H Chen, L F Qie, A J Zhang, et al. Improved CICA algorithm used foe single channel compound fault diagnosis of rolling bearings. Chinese Journal of Mechanical Engineering, 2016, 29(1): 204-210.
[9] H Z Gao, L Liang, X G Chen, et al. Feature extraction and recognition for rolling element bearing fault utilizing short-Time fourier transform and non-negative matrix factorization. Chinese Journal of Mechanical Engineering, 2015, 28(1): 96-104.
[10] S Klinchaeam, P Nivesrangsan. Condition monitoring of valve clearance fault on a small four strokes petrol engine using vibration signals. Songklanakarin Journal of Science & Technology, 2010, 32(6): 619-625.
[11] Y C Choi, Y H Kim. Fault detection in a ball bearing system using minimum variance cepstrum. Measurement Science & Technology, 2007, 18(5): 1433-1440.
[12] L L Cui, F Ding, L X Gao, et al. Research on the comprehensive demodulation of gear tooth crack early fault. Wuhan University of science and technology journal, 2006, 28(s2): 596-599.
[13] Y Qin, S R Qin, Y F Mao. Research on iterated Hilbert transform and its application in mechanical fault diagnosis. Mechanical Systems & Signal Processing, 2008, 22(8):1967-1980.
[14] J S Cheng, D J Yu, Y Yang. The application of energy operator demodulation approach based on EMD in machinery fault diagnosis. Mechanical Systems & Signal Processing, 2007, 21(2): 668-677.
[15] W Y Wang. Early detection of gear tooth cracking using the resonance demodulation technique. Mechanical Systems & Signal Processing, 2001, 15(5): 887-903.
[16] J S Smith. The local mean decomposition and its application to EEG perception data. Journal of the Royal Society Interface, 2005, 2(5): 443-454.
[17] Y X Wang, Z J He, Y Y Zi. A demodulation method based on improved local mean decomposition and its application in rubimpact fault diagnosis. Measurement Science and Technology, 2009, 20(2): 1-10.
[18] Y X Wang, Z J He, Y Y Zi. A comparative study on the local mean decomposition and empirical mode decomposition and their applications to rotating machinery health diagnosis. Journal of Vibration and Acoustics, 2010, 132(2): 613-624.
[19] Y F Dong, Y M Li, M K Xiao, et al. Analysis of earthquake ground motions using an improved Hilbert-Huang transform. Soil Dynamics & Earthquake Engineering, 2008, 28(1): 7-19.
[20] S G Song, J T Tang, J S He. Wavelets analysis and the recognition, separation and removal of the static shift in electromagnetic soundings. Chinese Journal of Geophysics, 1995, 38(1): 120-128. (in Chinese).
[21] P Chen, Q M Li. Design and analysis of mathematical morphology-based digital filters. Proceedings of the CSEE, 2005, 25(11): 60-65. (in Chinese).
[22] L Ling, Z Xu. Mathematical morphology based detection and classification of dynamic power quality disturbances. Power System Technology, 2006, 30(5): 62-66. (in Chinese).
[23] G Y Li, Y Luo, M Zhou, et al. Power quality disturbance detection and location based on mathematical morphology and grille fractal. Proceedings of the CSEE, 2006, 26(3): 25-30. (in Chinese).
[24] D B Logan, J Mathew. Using the correlation dimension for vibration fault diagnosis of rolling element bearing-Ⅱ. Selection of experimental parameters. Mechanical Systems and Signal Processing, 1996, 10(3): 251-264.
[25] J D Jiang, J Chen, L S Qu. The application of correlation dimension in gearbox condition monitoring. Journal of Sound and Vibration, 1999, 223(4): 529-542.
[26] W Wang, J Chen, Z Wu. The application of a correlation dimension in large rotating machinery fault diagnosis. Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science, 2000, 214(7): 921-930.
[27] I Daubechies. Ten lectures on wavelets[C]//Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, 1992.
[28] S Blanco, A Figliola, R Q Quiroga, et al. Time-frequency analysis of electroencephalogram series. Ⅲ. Wavelet packets and information cost function. Physical Review E Statistical Physics Plasmas Fluids & Related Interdisciplinary Topics, 1998, 57(1): 932-940.
[29] Z Y He, X Q Chen, G M Luo. Wavelet entropy measure definition and its application for transmission line fault detection and identification//In Proceedings of 2006 International Conference on Power Systems Technology, Chongqing, China, October 22-26, 2006: 634-639.
[30] Z Y He, S B Gao, X Q Chen, et al. Study of a new method for power system transients classification based on wavelet entropy and neural network. International Journal of Electrical Power & Energy Systems, 2011, 33(3): 402-410.
[31] O A Rosso, M T Martin, A Figliola, et al. Eeg analysis using wavelet-based information tools. Journal of Neuroscience Methods, 2006, 153(2): 163-182.
[32] W X Ren, Z S Sun. Structural damage identification by using wavelet entropy. Engineering Structures, 2008, 30(10): 2840-2849.
[33] B Yu, D D Liu, T H Zhang. Fault diagnosis for Micro-Gas turbine engine sensors via wavelet entropy. Sensors, 2011, 11(10): 9928-9941.
[34] Y Maki, K A Loparo. A neural network approach to fault detection and diagnosis in industrial process. IEEE Transactions on Control Systems Technology, 1997, 5(6): 529-541.
[35] B A Paya, I I Esat, M N M Badi. Artificial neural network based fault diagnostics of rotating machinery using wavelet transforms as a preprocessor. Mechanical Systems and Signal Processing, 1997, 11(5): 751-765.
[36] B Samant, K R Al-Balushi. Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mechanical Systems and Signal Processing, 2003, 17(2): 317-328.
[37] C C Hsu, M C Chen, L S Chen. Intelligent ICA-SVM fault detector for non-Gaussian multivariate process monitoring. Expert Systems with Applications, 2010, 37(4): 3264-3273.
[38] C Park, D Looney, M M V Hulle, et al. The complex local mean decomposition. Neurocomputing, 2011, 74(6): 867-875.
[39] P Grassberger, I Procaccia. Characterization of strange attractors. Physical Review Letters, 1983, 50(5): 346-349.
[40] S G Mallat. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. Patt. Anal. Mach. Intell., 1989, 11(7): 674-693.
[41] V N Vapnik. The nature of statistical learning theory. New York: Springer, 1995.
[42] B E Boser, I M Guyon, V N VAPNIK. A training algorithm for optimal margin classifiers//Proceeding of the 1992 Fifth Annual ACM Workshop, Pittsburgh, Pennsylvania, USA, July 27-29, 1992: 144-152.
[43] Y S Zhu. Support vector machine and its application in mechanical fault pattern recognition. Xi'an: Jiaotong University, 2003.
[44] J Y Yang, Y Y Zhang. Application research of support vector machines in condition trend prediction of mechanical equipment. Lecture Notes in Computer Science, 2005, 3498: 857-864.
[45] C W Hsu, C J Lin. A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks, 2002, 13(2): 415-425.
[46] L J Cao, K S Chua, W K Chong, et al. A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine. Neurocomputing, 2003, 55(1-2):321-336.
[47] D Kugiumtzis. State space reconstruction parameters in the analysis of chaotic time series-the role of the time window length. Physica D Nonlinear Phenomena, 1996, 95(1): 13-28.
[48] H S Kim, R Eykholt, J D SALAS. Nonlinear dynamics, delay times, and embedding windows. Physica D Nonlinear Phenomena, 1999, 127(1-2): 48-60.