基于粒子群优化算法和非线性盲源信号分离测量两相流速度(英文)

• 作 者: 吴新杰 崔春阳 胡晟 李志宏 吴成东
• 机 构: College of Physics,Liaoning University,Key Laboratory of Condition Monitoring and Control for Power
• 出 处: Chinese Journal of Chemical Engineering
• 发表日期: 2012年02期
• 关键词: 信号分离 downstream mutual 粒子群优化 swarm pipeline blind upstream disturbance processed

In order to overcome the disturbance of noise,this paper presented a method to measure two-phase flow velocity using particle swarm optimization algorithm,nonlinear blind source separation and cross correlation method.Because of the nonlinear relationship between the output signals of capacitance sensors and fluid in pipeline,nonlinear blind source separation is applied.In nonlinear blind source separation,the odd polynomials of higher order are used to fit the nonlinear transformation function,and the mutual information of separation signals is used as the evaluation function.Then the parameters of polynomial and linear separation matrix can be estimated by mutual information of separation signals and particle swarm optimization algorithm,thus the source signals can be separated from the mixed signals.The two-phase flow signals with noise which are obtained from upstream and downstream sensors are respectively processed by nonlinear blind source separation method so that the noise can be effectively removed.Therefore,based on these noise-suppressed signals,the distinct curves of cross correlation function and the transit times are obtained,and then the velocities of two-phase flow can be accurately calculated.Finally,the simulation experimental results are given.The results have proved that this method can meet the measurement requirements of two-phase flow velocity.

In order to overcome the disturbance of noise, this paper presented a method to measure two-phase flow velocity using particle swarm optimization algorithm, nonlinear blind source separation and cross correlation method. Because of the nonlinear relationship between the output signals of capacitance sensors and fluid in pipeline, nonlinear blind source separation is applied. nonlinear blind source separation, the odd polynomials of higher order are used to fit the nonlinear transformation function, and the mutual information of separation signals is used as the evaluation function. The parameters of polynomial and linear separation matrix can be estimated by mutual information of separation signals and particle swarm optimization algorithm, thus the source signals can be separated from the mixed signals. two-phase flow signals with noise which are obtained from upstream and downstream sensors are separately processed by nonlinear blind source separation method so that the noise can the effective curves of cross correlation function and the transit times are obtained, and then the velocities of two-phase flow can be accurately calculated. Finally, the simulation experimental results are given The results have proved that this method can meet the measurement requirements of two-phase flow velocity.