Weak fault feature extraction of rolling bearings based on improved ensemble noise-reconstructed EMD and adaptive threshold denoising

Extracting weak fault features under noise interference is crucial for the fault diagnosis of rolling bearings at an early stage. In this paper, a new method based on improved ensemble noise-reconstructed empirical mode decomposition (IENEMD) and adaptive threshold denoising (ATD) is proposed for the weak fault feature extraction of rolling bearings. Firstly, to tackle the drawbacks of EEMD which utilizes the additional Gaussian white noise resulting in submersion of the weak fault features, the inherent noise hidden in the raw signals is automatically extracted and leveraged in the IENEMD to decompose the raw signals into intrinsic mode functions (IMFs). The IENEMD not only avoids the mode mixing issues of the original EMD but also reduces the interference of inherent noise. Then, the ATD consisting of informative IMF selection and threshold denoising is executed on the decomposed IMFs. Taking the health signals of rolling bearings as the benchmarks, the meaningful IMFs rich in fault information are efficiently selected, which are further denoised by a newly constructed self-adaptive threshold. Finally, weak fault features are extracted from the reconstructed denoised signals employing the envelope analysis approach. A simulation case and two actual cases of rolling bearings in the early fault stage are utilized to verify the robustness and feasibility of the proposed IENEMD-ATD. The results indicate that the proposed approach exceeds other state-of-the-art techniques in extracting weak fault features of rolling bearings.