ACROA优化的自适应最稀疏窄带分解方法

发布时间：2018-12-09 17:54

【摘要】：提出了基于人工化学反应优化算法(artificial chemical reaction optimization algorithm,ACROA)的自适应最稀疏窄带分解(adaptive sparsest narrow-band decomposition,ASNBD)方法,将信号分解转化为对滤波器参数的优化问题,使用ACROA进行优化,以得到信号的最稀疏解为优化目标,在优化过程中将信号自适应地分解成若干个具有物理意义的局部窄带信号。对数值仿真和齿轮故障数据进行分析,结果表明该方法在抑制模态混淆、抗噪声性能、提高分量的正交性和准确性等方面要优于ASTFA方法、基于遗传算法(genetic algorithm,GA)的ASNBD方法及总体平均经验模态分解(ensemble empirical mode decomposition,EEMD)方法,并能有效识别出齿轮的典型故障。
[Abstract]:An adaptive narrow-band decomposition (adaptive sparsest narrow-band decomposition,ASNBD) method based on the artificial chemical reaction optimization algorithm (artificial chemical reaction optimization algorithm,ACROA) is proposed. The signal decomposition is transformed into the optimization of filter parameters and optimized by ACROA. In order to obtain the sparse solution of the signal as the optimization objective, the signal is decomposed adaptively into a number of local narrow band signals with physical significance during the optimization process. The numerical simulation and gear fault data are analyzed. The results show that the proposed method is superior to ASTFA method in suppressing modal confusion, anti-noise performance, improving the orthogonality and accuracy of components, and based on genetic algorithm (genetic algorithm,. The ASNBD method of GA and the method of total average empirical mode decomposition (ensemble empirical mode decomposition,EEMD) can effectively identify the typical faults of gears.
【作者单位】： 湖南大学汽车车身先进设计制造国家重点实验室;
【基金】：国家重点研发计划项目(2016YFF0203400) 国家自然科学基金资助项目(51375152,51575168) 智能型新能源汽车国家2011协同创新中心 湖南省绿色汽车2011协同创新中心资助项目
【分类号】：TH132.41
，

本文编号：2369795