针对引风机变工况运行导致轴承故障特征难以挖掘这一问题,本文提出了一种基于非线性模态分解(Nonlinear mode decomposition, NMD)的无转速计阶次跟踪方法,该方法首先利用NMD获取信号低频部分的非线性模态分量,然后利用Hilbert变换提取该分量的瞬时频率,之后将该瞬时频率作为轴承的转频对时域信号进行角域重采样；最后利用基于粒子群算法优化的平方包络盲反卷积算法(Square envelope blind deconvolution algorithm based on Particle Swarm Algorithm Optimization, PSO-SEBD)实现对轴承故障特征阶次的提取。实验结果表明,和常用的信号分解方法相比,该方法能够更准确地提取转速信息,并能在噪声环境下实现轴承故障特征阶次的可靠提取。

Aiming at the problem that it is difficult to excavate the fault characteristics of the bearing due to the variable operating conditions of the induced draft fan, this paper proposes a tachometer-less order tracking method based on Nonlinear mode decomposition (NMD), this paper proposes a tachometer-free order tracking method based on nonlinear mode decomposition (NMD), it firstly uses NMD to obtain the nonlinear modal component of the low-frequency part of the signal, and then extracts the instantaneous frequency of the component by using Hilbert transform, and then resamples the time-domain signal in the angular domain by using this instantaneous frequency as the rotational frequency of the bearing; and finally, it makes use of the square envelope blind deconvolution algorithm based on Particle Swarm Algorithm Optimization (PSO-SEBD) is used to realize the extraction of bearing fault characteristic order. The experimental results show that the method can extract the rotational speed information more accurat

国家能源集团科技项目(GJNY-23-68)