Work

EEMD Energy Spectrum Decoupling: An Efficient Hilbert–Huang Fusion Approach for Intelligent Bearing Fault Diagnosis

Year: 2025

Type: article

Abstract: As a critical component of rotating machinery, the operational status of rolling bearings is considered to directly determine the reliability of rail traffic systems. To address the complex modulation effects existing between multiple bearing components and the non-linear, non-stationary characteristics exhibited by vibration acceleration signals, an intelligent fault diagnosis method for bearings based on Hilbert envelope demodulation and Ensemble Empirical Mode Decomposition energy distribution features is proposed. First, the original vibration signal is subjected to envelope demodulation processing by the Hilbert transform, thereby effectively separating the envelope signal containing fault characteristics. Subsequently, the demodulated envelope signal is decomposed by EEMD to extract Intrinsic Mode Functions (IMFs), where each IMF component is calculated layer by layer using a normalization method based on the EEMD decomposition sequence. Finally, the proposed algorithm is validated by the standard bearing fault dataset from Case Western Reserve University. Experimental results show that the proposed method achieves 100% accuracy in fault identification, and its superiority is proven to exceed conventional diagnostic approaches significantly.

Source: Applied Sciences

DOI: 10.3390/app15126458

Authors: Lianyou Lai, Weijian Xu, Zhongzhe Song

Institution: Jimei University

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896