Rolling bearings are fundamental elements that play a crucial role in the functioning of rotating machines; thus, fault diagnosis of rolling bearings is of great significance to reduce catastrophic failures and heavy economic loss. However, the vibration signals of rolling bearings are often nonlinear and nonstationary, resulting in difficulty for feature extraction and fault recognition. In this paper, a hybrid method for multiple fault diagnosis of rolling bearings is presented. The bearing vibration signals are decomposed with the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to denoise and extract nonlinear entropy features. The nonlinear entropy features are further processed to select the more discriminative fault features and to reduce feature dimension. Then a multi-class intelligent recognition model based on ensemble support vector machine (ESVM) is constructed to diagnose different bearing fault modes as well as fault severities. The effectiveness of the proposed method is assessed via experimental case studies of rolling bearings under multiple operational conditions (i.e., speeds and loads). The results show that our method gives better diagnosis results as compared to some existing approaches.

中文翻译：

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基于改进的具有自适应噪声，非线性熵和集成支持向量机的完整集成经验模式分解的滚动轴承故障诊断

滚动轴承是基本要素，在旋转机械的功能中起着至关重要的作用。因此，滚动轴承的故障诊断对减少灾难性故障和重大的经济损失具有重要意义。然而，滚动轴承的振动信号通常是非线性且不稳定的，从而导致特征提取和故障识别困难。本文提出了一种用于滚动轴承多故障诊断的混合方法。轴承振动信号通过使用自适应噪声（ICEEMDAN）进行改进的完整整体经验模式分解（ICEEMDAN）进行分解，以去噪并提取非线性熵特征。进一步对非线性熵特征进行处理，以选择更具判别性的断层特征并减小特征尺寸。然后，基于集成支持向量机（ESVM），建立了一个多类智能识别模型，用于诊断不同的轴承故障模式以及故障严重程度。通过在多个运行条件（即速度和负载）下滚动轴承的实验案例研究，评估了所提出方法的有效性。结果表明，与某些现有方法相比，我们的方法可提供更好的诊断结果。