Cheng Zhengyang, Yang Yu, Duan Chengcheng, Kang Xin, Cui Jianxin
College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
Sensors (Basel). 2024 Dec 24;25(1):23. doi: 10.3390/s25010023.
All time-scale decomposition (ATD) is a non-parametric adaptive signal decomposition method, which relies on zero-crossing points and extreme points to jointly construct the baseline, achieving the suppression of modal mixing caused by the proximity of component frequencies. However, ATD is unable to solve mode mixing induced by noise. To improve this defect, a new noise-assisted signal decomposition method named ensemble all time-scale decomposition (EATD) is proposed in this paper. EATD introduces the noise-assisted technique of complementary ensemble empirical mode decomposition based on ATD, adding complementary noises to mask the noise interference in the signal. EATD not only overcomes mode mixing caused by noise but also preserves the capability of ATD to suppress mode mixing caused by the proximity of component frequencies. Simulation signals and bevel gear fault signals are utilized to validate EATD, and the results indicate that EATD can successfully overcome mode mixing induced by noise and can be effectively applied for gear fault diagnosis.
全时间尺度分解（ATD）是一种非参数自适应信号分解方法，它依靠过零点和极值点共同构建基线，实现对由分量频率接近引起的模态混叠的抑制。然而，ATD无法解决由噪声引起的模态混叠问题。为改善这一缺陷，本文提出了一种新的噪声辅助信号分解方法，即总体全时间尺度分解（EATD）。EATD在ATD的基础上引入了互补总体经验模态分解的噪声辅助技术，添加互补噪声以掩盖信号中的噪声干扰。EATD不仅克服了由噪声引起的模态混叠，还保留了ATD抑制由分量频率接近引起的模态混叠的能力。利用仿真信号和锥齿轮故障信号对EATD进行验证，结果表明EATD能够成功克服由噪声引起的模态混叠，并且能够有效地应用于齿轮故障诊断。
