Song Shuo, Wang Wenbo
Hubei Province Key Laboratory of System Science in Metallurgical Process, Wuhan University of Science and Technology, Wuhan 430081, China.
Entropy (Basel). 2023 Oct 16;25(10):1452. doi: 10.3390/e25101452.
Due to the early formation of rolling bearing fault characteristics in an environment with strong background noise, the single use of the time-varying filtering empirical mode decomposition (TVFEMD) method is not effective for the extraction of fault characteristics. To solve this problem, a new method for early fault detection of rolling bearings is proposed, which combines multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) with parameter optimization and TVFEMD. Firstly, a new weighted envelope spectrum kurtosis index is constructed using the correlation coefficient and envelope spectrum kurtosis, which is used to identify the effective component and noise component of the bearing fault signal decomposed by TVFEMD, and the intrinsic mode function (IMF) containing rich fault information is selected for reconstruction. Then, a new synthetic impact index (SII) is constructed by combining the maximum value of the autocorrelation function and the kurtosis of the envelope spectrum. The SII index is used as the fitness function of the gray wolf optimization algorithm to optimize the fault period, T, and the filter length, L, of MOMDEA. The signal reconstructed by TVF-EMD undergoes adaptive filtering using the MOMEDA method after parameter optimization. Finally, an envelope spectrum analysis is performed on the signal filtered by the adaptive MOMEDA method to extract fault feature information. The experimental results of the simulated and measured signals indicate that this method can effectively extract early fault features of rolling bearings and has good reliability. Compared to the classical FSK, MCKD, and TVFEMD-MOMEDA methods, the first-order correlated kurtosis (FCK) and fault feature coefficient (FFC) of the filtered signal obtained using the proposed method are the largest, while the sample entropy (SE) and envelope spectrum entropy (ESE) are the smallest.
由于滚动轴承故障特征在强背景噪声环境中形成较早，单纯使用时变滤波经验模态分解（TVFEMD）方法对故障特征提取效果不佳。为解决这一问题，提出一种滚动轴承早期故障检测新方法，该方法将多点最优最小熵反卷积调整（MOMEDA）与参数优化及TVFEMD相结合。首先，利用相关系数和包络谱峭度构建一种新的加权包络谱峭度指标，用于识别TVFEMD分解后的轴承故障信号有效分量和噪声分量，选取包含丰富故障信息的本征模态函数（IMF）进行重构。然后，结合自相关函数最大值和包络谱峭度构建一种新的综合冲击指标（SII）。将SII指标作为灰狼优化算法的适应度函数，对MOMDEA的故障周期T和滤波器长度L进行优化。经参数优化后，TVF-EMD重构信号采用MOMEDA方法进行自适应滤波。最后，对自适应MOMEDA方法滤波后的信号进行包络谱分析，提取故障特征信息。模拟信号和实测信号的实验结果表明，该方法能有效提取滚动轴承早期故障特征，具有良好的可靠性。与经典的FSK、MCKD和TVFEMD-MOMEDA方法相比，该方法得到的滤波信号的一阶相关峭度（FCK）和故障特征系数（FFC）最大，而样本熵（SE）和包络谱熵（ESE）最小。
