Affiliations: [a] State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi'an Jiaotong University, Xi'an, Shaanxi, China | [b] College of Computer Science and Technology, HuaQiao University, Xiamen, Fujian, China
Correspondence:
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Corresponding author: Cheng Wang, State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi'an Jiaotong University, Xi'an 710049, Shanxi, China. E-mail:[email protected]
Abstract: In order to separate multiple random fault sources only from mixed
vibration measurement response signals of mechanical system, Negentropy and
Gradient iteration based fast independent component analysis (FastICA) is
applied for blind signal separation (BSS). After finding the association
between independent components (ICs) matrix and multiple random fault
sources, multiple random fault sources identification problem is turned into
ICA of the stationary random vibration response signals of mechanical
system. This method uses negative entropy maximization as criterion of
independence, Gradient iteration as optimization method to extract random
fault sources one by one. Simulation experiment results verified that this
method could identify and separate multiple random fault sources only from
mixed vibration measurement response signals of mechanical system correctly
and effectively.
Keywords: Blind signal separation, fast independent component analysis, multiple random fault sources, blind identification and separation, vibration measurement response signals, negentropy, gradient iteration
