去趋势波动分析
数学
系列(地层学)
赫斯特指数
小波
统计物理学
信号(编程语言)
缩放比例
作者
Lin Jinshan,Chunhong Dou,Yingjie Liu
标识
DOI:10.1007/s11071-021-06223-7
摘要
Multifractal detrended fluctuation analysis (MFDFA) is a powerful tool to uncover nature of complex signals. However, MFDFA runs into difficulties in determining the type of the fitting polynomial trend and making the fitting polynomial trend continuous. To solve these problems, MFDFA based on empirical mode decomposition (MFDFAemd) is developed. Unfortunately, MFDFAemd suffers from negative frequency and difficulties in selecting fractal components. To overcome deficiencies of these traditional methods, this paper proposes a novel version of MFDFA based on optimized empirical mode decomposition (MFDFAoemd). In MFDFAoemd, instantaneous frequency of a signal component is estimated using normalized Hilbert transform and Teager energy operator and a criterion for distinguishing a fractal component from a truly noisy component is established. Moreover, the effectiveness of MFDFAoemd is compared with MFDFA and MFDFAemd by probing a multifractal signal generated by a multifractal cascade model. The comparison displays superiority of MFDFAoemd over MFDFA and MFDFAemd. Next, the performance of MFDFAoemd is further benchmarked against MFDFA and MFDFAemd by analyzing gearbox vibration signals containing different types of single-point fault and those containing different types of compound gear fault. The results show that MFDFAoemd can remedy the shortages of MFDFA and MFDFAemd and has an advantage in diagnosing both single-point gear faults and compound gear faults.
科研通智能强力驱动
Strongly Powered by AbleSci AI