摘要
磁声发射(MAE)是铁磁性材料磁化过程中产生的声发射信号,在构件应力检测和微观损伤检测中有着广泛的应用。针对MAE信号非稳态、复杂性、衰减性等特点,提出海鸥算法结合变分模态分解(SOA-VMD)的去噪方法,为克服海鸥算法求解过程中易陷入局部最优解问题,利用柯西变异算子产生随机迭代过程,使改进算法即柯西变异海欧算法(CVSOA)跳出早熟收敛。采用以幅值谱熵为适应度函数,优化VMD算法中分解模态个数K和二次惩戒因子α两个参数,将含噪声的MAE信号进行VMD分解重构。经仿真信号和实际检测信号分析表明,改进后的CVSOA-VMD算法全局寻优能力和去噪性能优于传统的SOA-VMD算法,降噪后的MAE信号特征值对于不同应力下均方根、偏斜度特征值的重复性更好,可靠性更高。
Magnetic acoustic emission(MAE)is an acoustic emission signal generated in the magnetization process of ferromagnetic materials,which has a wide range of applications in component stress detection and micro damage detection.Aiming at the characteristics of MAE signal instability,complexity,and attenuation,a denoising method based on seagull optimization algorithm combined with variational mode decomposition(SOA-VMD)is proposed.In order to overcome the problem of getting into the local optimal solution in the solving process of the seagull algorithm,we use the Cauchy variation operator to generate random iterations,making Cauchy variation seagull optimization algorithm(CVSOA)to jump out of premature convergence.The amplitude spectrum entropy is used as the fitness function,and the SOA is used to optimize the number of decomposed modes K and secondary penalty termαin the VMD algorithm.Then,the noisy signal is decomposed by VMD,and the MAE signal is reconstructed after removing the noise component.The analysis of the simulated signal and the actual detection signal shows that the improved CVSOA-VMD algorithm’s global optimization ability and denoising performance are better than the traditional SOA-VMD algorithm,the noise reduced MAE signal eigenvalues have better repeatability and higher reliability for root mean square and skewness eigenvalues under different stresses.
作者
傅伟成
吴伟
邱发生
李喆
FU Weicheng;WU Wei;QIU Fasheng;LI Zhe(Key Laboratory of Non-destructive Testing Technology,Nanchang Hangkong University,Nanchang 330063,China;AECC Shenyang Liming Aero-engine Co.,Ltd.,Shenyang 110043,China)
出处
《数据采集与处理》
CSCD
北大核心
2022年第2期359-370,共12页
Journal of Data Acquisition and Processing
基金
江西省教育厅科技青年项目(GJJ190511)
南昌航空大学研究生创新专项基金(YC2020S524)。
关键词
磁声发射
海鸥算法
柯西变异
变分模态分解
magnetic acoustic emission
seagull algorithm
Cauchy variation
variational mode decomposition
