Aero Engine Corporation of China Harbin Bearing Co., LTD, Harbin 150500, China.
School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China.
Sensors (Basel). 2019 Sep 19;19(18):4047. doi: 10.3390/s19184047.
In this paper, a novel method is proposed to enhance the accuracy of fault diagnosis for rolling bearings. First, an enhanced complementary empirical mode decomposition with adaptive noise (ECEEMDAN) method is proposed by determining two critical parameters, namely the amplitude of added white noise (AAWN) and the ensemble trails (ET). By introducing the concept of decomposition level, the optimal AAWN can be determined by judging the mutation of mutual information (MI) between adjacent intrinsic mode functions (IMFs). Furthermore, the ET is fixed at two to reduce the computational cost. This method can avoid disturbance of the spurious mode in the signal decomposition and increase computational speed. Enhanced CEEMDAN demonstrates a more significant improvement than that of the traditional CEEMDAN. Vibration signals can be decomposed into a set of IMFs using enhanced CEEMDAN. Some IMFs, which are named intrinsic information modes (IIMs), effectively reflect the vibration characteristic. The evaluated comprehensive factor (CF), which combines the shape, crest and impulse factors, as well as the kurtosis, skewness, and latitude factor, is developed to identify the IIM. CF can retain the advantage of a single factor and make up corresponding drawbacks. Experiment results, especially for the extraction of bearing fault under variable speed, illustrate the superiority of the proposed method for the fault diagnosis of rolling bearings over other methods.
本文提出了一种新的方法来提高滚动轴承故障诊断的准确性。首先，通过确定两个关键参数，即添加白噪声的幅度（AAWN）和集合轨迹（ET），提出了一种增强的自适应噪声互补经验模态分解（ECEEMDAN）方法。通过引入分解水平的概念，可以通过判断相邻固有模态函数（IMF）之间的互信息（MI）的突变来确定最佳的 AAWN。此外，将 ET 固定为 2 以减少计算成本。该方法可以避免信号分解中伪模态的干扰，提高计算速度。增强型 CEEMDAN 比传统 CEEMDAN 有了更显著的改进。振动信号可以使用增强型 CEEMDAN 分解为一组 IMF。一些 IMF，即固有信息模式（IIM），有效地反映了振动特性。评估综合因子（CF），它结合了形状、峰值和脉冲因子，以及峰度、偏度和纬度因子，用于识别 IIM。CF 可以保留单一因素的优势，并弥补相应的缺点。实验结果，特别是在变速条件下提取轴承故障的实验结果，表明与其他方法相比，该方法在滚动轴承故障诊断方面具有优越性。
