针对滚动轴承性能衰退状态监测中的故障信号微弱和故障模式多样性等问题，提出了一种基于自适应噪声完备集合经验模态分解(CEEMDAN)以及主成分分析(PCA)的滚动轴承性能衰退状态监测方法.针对经过预处理后的滚动轴承原始振动信号，分别采用CEEMDAN方法以及Hilbert-Huang变换提取本征模能量特征和故障特征；综合运用斯皮尔曼等级相关系数和PCA进行特征融合，以获得表征滚动轴承性能衰退状态的健康指数；通过对健康指数的单调性、稳健性和衰退性等进行分析，并经过加权平均来识别滚动轴承性能衰退状态.实例分析结果表明，所提出的方法能够较为准确地识别滚动轴承性能衰退状态.
A degradation condition monitoring method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and principal component analysis (PCA) for rolling bearings is proposed, which considers the problems of weak fault signals and diverse fault modes. In this paper, intrinsic energy features and failure frequency features are respectively extracted from the preprocessed original vibration signals of rolling bearings using CEEMDAN algorithm and Hilbert-Huang transform. Then, a health index of the rolling bearings is obtained through the feature fusion using spearman rank correlation coefficient and PCA. Finally, the degradation condition of the rolling bearings is identified by the analysis of the monotonicity, robustness and correlation of the health index. The conclusion is drawn by a case study that the proposed method can be applied to accurately and timely identifying the degradation condition of the rolling bearings.
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