摘要
针对传统的机械故障非线性盲分离方法的不足,即将非线性盲源分离中分离矩阵和非线性去混合函数的参数分开来优化,这样容易顾此失彼,学习效率低。将量子遗传引入到机械故障非线性盲分离中,提出一种基于量子遗传的机械故障非线性盲源分离方法(简称QGA-NBSS方法),该方法能同时对分离矩阵和非线性去混合函数的参数进行优化,获得全局最优解并加快了算法的全局收敛性,克服了传统的机械故障源的非线性盲源分离方法的不足。仿真和实验结果验证了提出的方法的有效性。
Based on the deficiency in the traditional nonlinear blind separation method of mechanical fault sources, i.e. the separation matrix parameter and nonlinear mixing parameter in the nonlinear blind source separation are usually optimized separately, which easily lead to have one without another and low learning efficiency. Quantum genetic algorithm is introduced into the nonlinear blind source separation of mechanical fault, a nonlinear blind separation method of mechanical fault sources based on the quantum genetic algorithm, which is named as QGA-NBSS method, is proposed. The proposed method can simultaneously optimize all parameters in the nonlinear blind separation, i.e. the separation matrix and nonlinear mixing function, obtain global optimal solution, and greatly improves the global convergence of the algorithm. The simulation and experimental results show that the proposed algorithm is effective.
出处
《机械强度》
CAS
CSCD
北大核心
2015年第3期393-397,共5页
Journal of Mechanical Strength
基金
国家自然科学基金(51075372
51265039
50775208)
江西省教育厅科技计划项目(GJJ12405)
湖南科技大学机械设备健康维护湖南省重点实验室开放基金(201204)资助~~
关键词
量子遗传
非线性盲源分离
故障诊断
Quantum genetic algorithm （QGA）, Nonlinear blind source separation, Fault diagnosis
