Underdetermined Blind Separation for Bearings Faults Based on the Improved Morphological Filter

Abstract:

Aiming at the condition of the strong background noise, many interference sources and unknown the number of sources in the actual industrial field, a method based on multi-scale multi-structure close-open average combination morphological filtering(C-OACMF) and sparse component analysis (SCA) was proposed to deal with the blind source separation problem of rotation machines. First, the C-OACMF was used to filter out background noise signals and extract the characteristic signal of observation signals, then using the simulated annealing genetic algorithm of fuzzy C-average clustering algorithm estimates the mixing matrix, the linear programming is finally used to estimate the source signals. Through the actual environment composite rolling bearing fault vibration signal extraction experiments verify the effectiveness and accuracy of the proposed algorithm.

• Periodical: Applied Mechanics and Materials (Volumes 670-671)
• Pages: 1200-1204
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.670-671.1200
• Online since: October 2014
• Authors: Zhou Jun*, Xing Wu, Yi Lin Chi, Pan Nan
• Keywords: Composite Fault Diagnosis, Improved Morphological Filter, Sparse Component Analysis (SCA), Underdetermined Blind Extraction

References

[1] Zhengjia He, Jin Chen, Taiyong Wang, et al. Mechanical fault diagnosis theory and application [M]. Beijing: Higher education press. (2010).

[2] Nikolaou N G, Antoniadis I A. Application of morphological operators as envelope extractors for impulsive-type Periodic signals [J]. Mechanical Systems and Signal Processing, 2003, 17(6): 1147-1162.

DOI: 10.1006/mssp.2002.1576

[3] Lu Shen, Xiaojun Zhou, Wenbin Zhang, et al. Fault diagnosis of rolling element bearing based on morphological f ilter and grey incidence [J]. Journal of Vibration and Shock, 2009, 28(11): 17-20.

[4] Yuchuan Li, Xing Wu, Yilin Chi, et al. Blind separation for rolling bearing faults based on morphological filtering and sparse component analysis [J]. Journal of Vibration and Shock, 2011, 30(12): 170-174.

[5] Lu Shen. Study on Application of Morphology in machinery faults Diagnosis [D]. Zhejiang: Zhejiang university, (2010).

[6] Li Y, Cichocki A, Amari S. Analysis of sparse representation and blind sourse separation [J]. Neural computation, 2004, 16(6): 1193-1234.

DOI: 10.1162/089976604773717586

[7] Nan Pan, Xing Wu, Yilin Chi, et al. Underdetermined blind solution of convolution is used for composite bearing fault acoustic diagnosis [J]. Journal of Vibration, Measurement Diagnosis, 2013, 33(2): 284-289.