辛几何模态分解和广义形态分形维数的液压泵故障诊断

Hydraulic pump fault diagnosis method of symplectic geometry mode decomposition and generalized morphological fractal dimensions

针对液压泵故障诊断问题,本文提出了一种基于辛几何模态分解和广义形态分形维数相结合的方法。对实测液压泵多模态故障振动信号进行分解;基于所提出的能量选取法,重构含有丰富运行特征信息的模态分量,并将其作为数据源;基于数据源提取,实现对液压泵不同故障的诊断。通过对比分析仿真和实测液压泵故障振动信号的试验结果,验证了该方法可以有效地诊断液压泵不同故障。

Aiming at the fault diagnosis of hydraulic pumps,we propose a new fusion method based on symplectic geometry mode decomposition(SGMD)and general morphological fractal dimensions(GMFDs).First,SGMD is applied to decompose the multi-mode vibration fault signals of the hydraulic pump.Second,the modes with rich running feature information can be selected by the proposed energy method,and they are restructured as data sources.Lastly,GMFDs are extracted from the data source,and hydraulic pump faults can be diagnosed.The simulation signals and actually measured fault signals of the vibrating hydraulic pump were compared,and the ability of the proposed method to effectively diagnose hydraulic pump faults was verified.