Application of FCEEMD-TSMFDE and adaptive CatBoost in fault diagnosis of complex variable condition bearings

Abstract

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.