Condition monitoring of wind turbine bearing based on CEEMDAN and improved TFR demodulation analysis

In recent years, as the share of wind energy in global renewable energy has grown rapidly, it has also occupied a central position in the global renewable energy landscape. In wind turbines, rolling element bearings (REBs) are indispensable and critical components. Once a failure occurs, the efficiency of the wind turbine will drop sharply or even be damaged. Especially when REB faults have just occurred, the ability to accurately extract fault features is a key factor in determining the effectiveness of fault diagnosis. To extract fault characteristic information, vibration analysis is one of the most powerful tools. However, traditional spectral analysis, based on vibration signals, requires highly detailed bearing structure parameters and lacks sufficient information, making it incapable to process non-stationary and nonlinear signals. This paper presents an innovative fault diagnosis method for rolling bearings, which combines Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and time-frequency representation (TFR) demodulation analysis to enhance fault detection accuracy and provide a holistic approach to fault diagnosis in rolling element bearings. The effectiveness of the proposed method is confirmed through the analysis of experimental datasets from the Center for Intelligent Maintenance Systems at the University of Cincinnati and the CWRU Bearing Data Center.