迭代辛几何模态分解的高速列车轴承故障诊断

High speed train bearings fault diagnosis of iteration symplectic geometry mode decomposition

针对传统的SGMD方法存在的端点效应抑制和分解终止约束问题,提出了一种新的信号分解算法迭代辛几何模态分解(Iteration Symplectic Geometry Mode Decomposition,ISGMD)。ISGMD在SGMD的基础上,将迭代的方法引入分解过程中,确保每个分量所提取的重构轨迹信号为独立分量,并提出了新的约束条件。ISGMD可以有效地分解时间序列信号并在没有任何定义参数的情况下消除噪声,抑制模态混叠与端点效应。数值仿真信号分析结果表明,所提出方法进行时间序列分解能够准确有效地分解分析信号。应用所提方法对高速列车轴承复合故障进行诊断,并与同类方法进行比较,结果表明所提方法可以更好地对轴承复合故障进行诊断。

Aiming at the problem of endpoint effect and decomposition termination constraint in traditional SGMD method,a new signal decomposition algorithm,iteration symplectic geometry mode decomposition(ISGMD),is proposed in this paper.On the basis of SGMD,ISGMD introduces iteration into the decomposition process to ensure that the reconstructed trajectory signal extracted by each component is an independent component,and puts forward new constraints.ISGMD can effectively decompose time series signals and eliminate noise without any defined parameters,thus suppressing modal aliasing and endpoint effect.The numerical simulation results show that the proposed method can decompose the analysis signals accurately and effectively.The proposed method is applied to the diagnosis of composite bearing faults in high-speed trains and compared with similar methods.The results show that the proposed method can better diagnose composite bearing faults.