改进的傅里叶分解算法及其在滚动轴承故障诊断的应用

针对滚动轴承早期微弱故障在噪声背景下难以提取的问题,提出一种改进的傅里叶分解(IFDM)与快速谱峭度相结合的新方法,用以准确、快速地识别故障特征成分。傅里叶分解法能将故障信号自适应地分解为一系列瞬时频率具有明确物理意义的固有频带函数(FIBFs),类似于经验模态分解产生的本征模态函数,但其缺点在于无法控制所生成的FIBFs数量,如信号本身调制成分太多或受噪声影响太大,则直接运用傅里叶分解算法(FDM)会产生大量无意义的FIBFs,造成大量计算时间浪费,增加数据处理难度。为此,提出一种改进的FDM方法,该方法使用快速谱峭度法对故障信号进行预处理,自适应地确定滤波器的最佳参数及故障所在频带,然后仅在该频带上使用改进的FDM分解,因此在准确提取出故障频率成分的同时极大地减少计算量。对仿真及轴承实际故障信号的分析结果表明,该方法能更准确识别故障特征。

Improved Fourier Decomposition and Its Application to Fault Diagnosis of Rolling Bearing

Aiming at the problem that it is difficult to extract the early weak fault of rolling bearing under the background of noise, a new method combining improved Fourier decomposition (IFDM) and fast spectral kurtosis was proposed to accurately and quickly identify the fault features components. Similar to the intrinsic mode functions generated by empirical mode decomposition, Fourier decomposition method can be used to adaptively decompose the fault signal into a series of intrinsic frequency band functions (FIBFs) which have the physical significance, but its disadvantage is that it cannot control the number of FIBFs. If the signal itself has too many modulation components or the influence of noise is too large, the directly using Fourier decomposition (FDM) will produce a large number of meaningless FIBFs, resulting in a large amount of calculation time waste and increasing the difficulty of data processing. Therefore, an improved FDM method was proposed, in which the fast kurtosis method was used to preprocess the fault signal, and the optimal parameters of the filter and the frequency band of the fault were determined adaptively. Then, only the frequency band with IFDM was decomposed, so the fault frequency components could be accurately extracted and the calculation amount was greatly reduced. The simulation and analysis results of the actual bearing fault signal demonstrate that this method can be used to extract the fault feature frequency more accurately and effectively.