Cumulative spectrum distribution entropy for rotating machinery fault diagnosis

Entropy-based methods have shown promise in detecting dynamic changes in non-linear signals and have been widely applied in fault diagnosis for rotating machinery. However, these methods have limitations when it comes to capturing frequency-domain information of fault features, as they are primarily based on time-domain signals. To address this issue, this paper proposes a new entropy measure called cumulative spectrum distribution entropy (CSDEn), which is based on the cumulative distribution of the spectrum and considers both frequency probability and frequency values in the spectrum domain. The proposed method is evaluated using synthetic signals and experimental data from different bearing and gear working states. The results show that CSDEn outperforms other widely used entropy measures in detecting dynamic changes and measuring signal complexity with low noise sensitivity and high computing efficiency. Nonparametric Mann–Whitney U tests reveal significant differences between different working states for proposed CSDEn method, and compared with other entropy methods, CSDEn achieves the highest recognition rates in diagnosing different bearing and gear working states. Moreover, proposed CSDEn method demonstrates its effectiveness in addressing the challenges of small sample datasets and strong noise interference, making it highly competitive in real industrial applications.