Integrated Improved Complete Ensemble Empirical Mode Decomposition and Continuous Wavelet Transform Approach for Enhanced Bearing Fault Diagnosis in Noisy Environments

Bearings are vital apparatuses in many industrial systems, and their failure can lead to severe damage, costly downtime, and safety risks. Therefore, early detection of bearing faults is critical to prevent catastrophic failures. However, diagnosing bearing faults in real-world conditions is challenging due to noise, which can obscure vibration signals and reduce the effectiveness of traditional diagnostic techniques. This paper portrays a unique method for bearing fault identification in high-noise environments by integrating Improved Complete Ensemble Empirical Mode Decomposition (ICEEMD) and Continuous Wavelet Transform (CWT). ICEEMD decomposes complex vibration signals into intrinsic mode functions, effectively filtering out noise and enhancing feature extraction. CWT is then applied to obtain a time–frequency representation of the cleaned signal, allowing for precise detection of transient events and frequency variations associated with faults. The proposed approach is evaluated using simulated signals, achieving a testing accuracy of 78% at −20 dB SNR, demonstrating its robustness in noisy environments. This study highlights the capability of combining ICEEMD and CWT for robust fault diagnosis in noisy industrial applications, paving the way for improved predictive maintenance strategies.