基于SSA-VMD和ITFPF的喷油器信号去噪方法

Injector Signal Denoising Method Combined with SSA-VMD and ITFPF

由于柴油机工作环境复杂多变,喷油器信号中包含较多噪声,导致诊断率低。为降低噪声的影响,提高故障诊断率,提出一种麻雀算法优化变分模态分解(SSA-VMD)和改进时频峰值滤波(ITFPF)结合的信号去噪方法。针对VMD受分解参数制约的问题,以能量分解因子为目标函数,通过SSA自适应地将信号分解成一系列IMF,以解决参数设置不当导致的模态混叠问题。TFPF的窗长选择不当会影响其滤波效果,由于排列熵能够度量非平稳信号的复杂度,以排列熵为适应度函数寻到最优窗长,使ITFPF兼顾信号保真和噪声压制。仿真和试验结果表明:SSA-VMD和ITFPF结合的方法降噪效果优于其他方法,并使故障识别率相比未优化VMD提高了10.13%。

Due to the complex and changeable working environment of the diesel engine,the signal of the injector contains more noise,which leads to a low diagnosis rate.In order to reduce the impact of noise and improve the fault diagnosis rate,a signal denoising method combining sparrow algorithm optimized variational mode decomposition(SSA-VMD) and improved time-frequency peak filtering(ITFPF) was proposed.Aiming at the problem of VMD restricted by decomposition parameters,the energy decomposition factor was used as the objective function,and the signal was adaptively decomposed into a series of IMF through SSA,which could solve the mode aliasing problem caused by improper parameter setting.Improper selection of TFPF window length would affect its filtering effect.Since permutation entropy could measure the complexity of non-stationary signals,it was used as the fitness function to find the optimal window length,so that ITFPF could take into account both signal fidelity and noise suppression.The simulation and experiment results show that the noise reduction effect of the method combined with SSA-VMD and ITFPF is better than other methods,and the fault recognition rate is increased by 10.13% comparing with the original VMD.