The health status information of rolling bearings is often contained in vibration signals, but it is difficult to detect bearing defects directly through vibration signals. To effectively extract the key feature information hidden in the original signal, this paper proposes the refined composite multiscale weighted permutation entropy (RCMWPE) method to efficiently characterize the operating state of the bearing. The proposed method focuses on two aspects: the improved version reduces the dependence of entropy on the length of the original time series, and the error caused by considering the amplitude information is suppressed. The performance of the proposed method is evaluated by synthetic signals and real bearing data, and compared with other traditional methods. By analyzing bearing signals of different fault types and different degrees of damage, it is verified that the proposed method can obtain more stable and reliable results and achieve higher fault diagnosis accuracy.

滚动轴承的健康状态信息通常包含在振动信号中，但是很难直接通过振动信号检测轴承缺陷。为了有效地提取隐藏在原始信号中的关键特征信息，本文提出了一种改进的复合多尺度加权置换熵（RCMWPE）方法，以有效地表征轴承的工作状态。所提出的方法集中在两个方面：改进的版本减少了熵对原始时间序列的长度的依赖性，并且抑制了考虑幅度信息而引起的误差。该方法的性能通过合成信号和真实方位数据进行评估，并与其他传统方法进行比较。