Due to the complex service conditions of rolling bearings, vibration signals arising therefrom exhibit non-linear characteristics, which means that single-scale feature extraction techniques cannot extract fault features. Multiscale symbolic dynamic entropy (MSDE) is a new technique that has recently emerged and been applied to fault diagnosis in machinery. However, MSDE has limitations such as its poor stability, large errors, and even loss of information. To this end, a novel sensible multiscale symbol dynamic entropy (SMSDE) method was proposed. For SMSDE, the signal was first decomposed using empirical mode decomposition, and then the useful intrinsic mode functions were selected for reconstruction to decrease noise. Secondly, the slippage-averaging multiscale approach was designed to coarse-grain the signal, which considers the connection of data before and after the breakpoint, thus reducing the error. The method can not only decrease noise, but also avoid the loss of key information, thereby extracting sensitive feature information. The results with multiple synthesized signals show that the proposed method is more robust than the other eight entropy methods. Furthermore, the real bearing signals of the three cases indicate that compared with other advanced entropy methods, SMSDE can better distinguish the various states of the bearing.

由于滚动轴承复杂的使用条件，其产生的振动信号呈现非线性特征，这意味着单尺度特征提取技术无法提取故障特征。多尺度符号动态熵（MSDE）是最近出现的一种新技术，并应用于机械故障诊断。但MSDE存在稳定性差、误差大、甚至信息丢失等局限性。为此，提出了一种新颖的明智的多尺度符号动态熵（SMSDE）方法。对于 SMSDE，首先使用经验模态分解对信号进行分解，然后选择有用的本征模态函数进行重构以降低噪声。其次，滑动平均多尺度方法被设计为粗粒度信号，它考虑了断点前后数据的连接，从而减少了错误。该方法既可以降低噪声，又可以避免关键信息的丢失，从而提取敏感特征信息。多个合成信号的结果表明，所提出的方法比其他八种熵方法更稳健。此外，三种情况的真实轴承信号表明，与其他先进的熵方法相比，SMSDE 可以更好地区分轴承的各种状态。多个合成信号的结果表明，所提出的方法比其他八种熵方法更稳健。此外，三种情况的真实轴承信号表明，与其他先进的熵方法相比，SMSDE 可以更好地区分轴承的各种状态。多个合成信号的结果表明，所提出的方法比其他八种熵方法更稳健。此外，三种情况的真实轴承信号表明，与其他先进的熵方法相比，SMSDE 可以更好地区分轴承的各种状态。