Mao Min, Xu Bingwei, Sun Yuhuan, Tan Kairong, Wang Yuran, Zhou Chao, Zhou Chengjiang, Yang Jingzong
Faculty of Information Engineering, Quzhou College of Technology, Quzhou, 324000, China.
Shimge Pump Industry (Zhejiang) Co. Ltd, Hangzhou, 310000, China.
Sci Rep. 2024 Dec 16;14(1):30448. doi: 10.1038/s41598-024-78845-x.
The mode mixing problem and inherent mode function selection bias in Fast Ensemble Empirical Mode Decomposition (FEEMD) result in ineffective extraction of fault components during the denoising stage, the loss of coarse-grained information in Multiscale Fuzzy Dispersion Entropy (MFDE) reduces the stability of fault features, and the lack of adaptability of CatBoost hyperparameters leads to reduced diagnostic accuracy. Therefore, a complex variable operating condition fault diagnosis method based on Fast Complementary Ensemble Empirical Mode Decomposition (FCEEMD) - Time-shift Multiscale Fuzzy Dispersion Entropy (TSMFDE) and adaptive Optuna-CatBoost is proposed. We introduce paired white noise with opposite signs in the construction of FCEEMD, effectively suppressing mode aliasing by neutralizing the residual noise generated during decomposition. Then, the Maximum Information Coefficient / Gini Index was introduced to construct a composite screening strategy, retaining the Intrinsic Mode Function (IMF) components that are strongly correlated with the original signal and have a fault impact to reconstruct the denoised signal. Secondly, time-shift multiscale is introduced into the coarse-grained process, and the constructed TSMFDE effectively extracts complete and stable fault features. Finally, with the introduction of the Optuna hyperparameter optimization framework, the adaptive Optuna-CatBoost can accurately diagnose bearing faults. The average fault diagnosis accuracy of the proposed method reached 99.76% and 99.33%, indicating that FCEEMD based on white noise can quickly and accurately decompose non-aliasing vibration modes, and the composite screening strategy can further filter out irrelevant noise modes and improve signal quality; The proposed TSMFDE can extract stable fault features, and its combination with Optuna-CatBoost can further improve the accuracy of fault diagnosis. This model is expected to be applied in more fields of feature extraction and pattern recognition.
快速集成经验模态分解（FEEMD）中的模态混叠问题和固有模态函数选择偏差导致在去噪阶段无法有效提取故障分量，多尺度模糊分散熵（MFDE）中粗粒度信息的丢失降低了故障特征的稳定性，而CatBoost超参数缺乏适应性导致诊断准确率降低。因此，提出了一种基于快速互补集成经验模态分解（FCEEMD）-时移多尺度模糊分散熵（TSMFDE）和自适应Optuna-CatBoost的复杂变工况故障诊断方法。在FCEEMD的构建中引入具有相反符号的成对白噪声，通过抵消分解过程中产生的残余噪声有效抑制模态混叠。然后，引入最大信息系数/基尼指数构建复合筛选策略，保留与原始信号强相关且对故障有影响的固有模态函数（IMF）分量来重构去噪信号。其次，将时移多尺度引入粗粒度过程，构建的TSMFDE有效提取完整稳定的故障特征。最后，通过引入Optuna超参数优化框架，自适应Optuna-CatBoost能够准确诊断轴承故障。所提方法的平均故障诊断准确率分别达到99.76%和99.33%，表明基于白噪声的FCEEMD能够快速准确地分解无混叠的振动模态，复合筛选策略能够进一步滤除无关噪声模态并提高信号质量；所提TSMFDE能够提取稳定的故障特征，其与Optuna-CatBoost相结合能够进一步提高故障诊断的准确率。该模型有望应用于更多的特征提取和模式识别领域。
