A uniform phase decoupled iterative filtering and multiscale sliding fractal box dimension method for rolling bearing fault diagnosis

Due to harsh working conditions and variable load, the vibration signal of the rolling bearing possess nonstationary and nonlinear properties, leading to low robustness and unreliability in fault diagnosis under noise interference. To this end, a uniform phase decoupled iterative filtering (IF) and multi-scale sliding fractal box dimension (UPDIF-MSFBD) is proposed, where UPDIF is employed to reduce the noise interference of vibration signal, and MSFBD is introduced to capture the multiscale fractal temporal features. First, a narrowband sinusoidal wave with a uniform phase distribution in IF is added into the original signal to equalize the distribution of extreme points of the intrinsic mode functions (IMF), where a new kind of decoupled IMFs is obtained based on the orthogonality between the IMFs to overcome the mode mixing problem. Second, the interquartile range normalization and the sliding coarse-grained method are introduced to explore abundant coarse-grained information and capture the fractal temporal features of fault vibration signals. Third, the extracted features are input into the classifiers for bearing fault diagnosis. The advantage of the UPDIF-MSFBD method is the robustness and effectiveness in the extraction and separation of multiscale fractal temporal features. The findings reveal that the proposed approach efficiently capture fault characteristics, outperforming current methods in terms of accuracy in fault diagnosis. The method is especially valuable for real-time monitoring and predictive maintenance, offering an advanced solution for fault diagnosis in complex machinery systems, which is crucial for minimizing downtime and extending the lifespan of critical components.