In allusion to the issue of rolling bearing degradation feature extraction and degradation condition clustering, a logistic chaotic map is introduced to analyze the advantages of C₀ complexity and a technique based on a multidimensional degradation feature and Gath–Geva fuzzy clustering algorithmic is proposed. The multidimensional degradation feature includes C₀ complexity, root mean square, and curved time parameter which is more in line with the performance degradation process. Gath–Geva fuzzy clustering is introduced to divide different conditions during the degradation process. A rolling bearing lifetime vibration signal from intelligent maintenance system bearing test center was introduced for instance analysis. The results show that C₀ complexity is able to describe the degradation process and has advantages in sensitivity and calculation speed. The introduced degradation indicator curved time parameter can reflect the agglomeration character of the degradation condition at time dimension, which is more in line with the performance degradation pattern of mechanical equipment. The Gath–Geva fuzzy clustering algorithmic is able to cluster degradation condition of mechanical equipment such as bearings accurately.

针对滚动轴承退化特征提取和退化条件聚类的问题，引入逻辑混沌映射图分析C ₀复杂度的优点，提出了基于多维退化特征和Gath-Geva模糊聚类算法的技术。多维降级特征包括C ₀复杂度，均方根和弯曲时间参数，这些参数与性能降级过程更加一致。引入了Gath-Geva模糊聚类，以在退化过程中划分不同的条件。引入了智能维护系统轴承测试中心的滚动轴承寿命振动信号进行实例分析。结果表明C₀复杂度能够描述退化过程，并且在灵敏度和计算速度上具有优势。引入的退化指标弯曲时间参数可以反映退化条件在时间维度上的结块特征，这更符合机械设备的性能退化模式。Gath-Geva模糊聚类算法能够准确地对机械设备（例如轴承）的退化条件进行聚类。