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BLIND SOURCE SEPARATION OF MECHANICAL FAULT BASED ON BAT ALGORITHM
Based on the advantages of Bat Algorithm（ BA） that is easily to be realized,fast convergence speed,high efficiency and high universality,combining BA and blind source separation,a new method of mechanical failure blind source separation based on BA is proposed（ BA-BSS）. The BA-BSS method,as the target function of the sum of the absolute value of the kurtosis,seeks the maximum of the target function by BA,and then determines the optimal separation matrix. Simulation results show that the BA-BSS method is significantly superior to the traditional mechanical failure blind source separation based on Genetic Algorithm（ GA-BSS） in separation performance,algorithm convergence and operation speed. Finally,the BA-BSS method is successfully applied to the actual rolling bearing inner and outer mixing fault blind source separation,and the separation effect is very effective
BLIND SOURCE SEPARATION OF MECHANICAL FAULT BASED ON BAT ALGORITHM
Based on the advantages of Bat Algorithm（ BA） that is easily to be realized,fast convergence speed,high efficiency and high universality,combining BA and blind source separation,a new method of mechanical failure blind source separation based on BA is proposed（ BA-BSS）. The BA-BSS method,as the target function of the sum of the absolute value of the kurtosis,seeks the maximum of the target function by BA,and then determines the optimal separation matrix. Simulation results show that the BA-BSS method is significantly superior to the traditional mechanical failure blind source separation based on Genetic Algorithm（ GA-BSS） in separation performance,algorithm convergence and operation speed. Finally,the BA-BSS method is successfully applied to the actual rolling bearing inner and outer mixing fault blind source separation,and the separation effect is very effective
BLIND SOURCE SEPARATION OF MECHANICAL FAULT BASED ON BAT ALGORITHM
SUN YiJie (author) / LI FuSheng (author) / LIANG MingLiang (author) / DONG LiSheng (author)
2018
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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