Post-nonlinear blind source separation with kurtosis constraints using augmented Lagrangian particle swarm optimization and its application to mechanical systems. (August 2019)

• Record Type: Journal Article
• Authors: Lu, Jiantao; Cheng, Wei; Chu, Yapeng; Zi, Yanyang
• Is Part Of: Journal of vibration and control. Volume 25:Number 16(2019)
• Page Start: 2246
• Page End: 2260
• Publication Date: 2019-08
• Subjects: Post-nonlinear blind source separation -- augmented Lagrangian particle swarm optimization -- kurtosis constraint -- source identification -- vibration and acoustic monitoring and control; Vibration -- Periodicals; Damping (Mechanics) -- Periodicals; 620.3
• DOI: 10.1177/1077546319852483
• Languages: English
• ISSNs: 1077-5463

Abstract

To accurately estimate source signals from their post-nonlinear mixtures, a post-nonlinear blind source separation (PNLBSS) method with kurtosis constraints is proposed based on augmented Lagrangian particle swarm optimization (PSO). First, an improved contrast function is presented by combining mutual information of the separated signals and kurtosis ranges of source signals. Second, an augmented Lagrangian multiplier method is used to convert PNLBSS into an unconstrained pseudo-objective optimization problem. Then, improved PSO is applied to update the parameters in complex nonlinear spaces. Finally, numerical case studies and experimental case studies are provided to evaluate the performance of the proposed method. By adding the kurtosis ranges constraints, the estimation accuracy of source signals could be improved, which would benefit vibration and acoustic monitoring and control.