Current load spectrum editing methods based on the Hilbert-Huang Transform (HHT) are limited by modal aliasing and boundary effects, and editing precision is therefore reduced. To overcome these limitations, the use of the Empirical Wavelet Transform (EWT) theory for editing automotive component load spectra is investigated in this study. The proposed methodology comprises three primary steps. First, the load spectrum is decomposed into Intrinsic Mode Functions (IMFs) with distinct frequency characteristics by EWT. Second, the instantaneous amplitude and frequency of load components are extracted from these IMFs by applying the Hilbert Transform, and the instantaneous energy spectrum is constructed. Third, the optimal threshold for the instantaneous energy spectrum is determined by a genetic algorithm, thereby allowing the identification and removal of time segments making minimal contribution to component damage. The remaining segments are then spliced, and a streamlined load spectrum is generated. The load spectrum of an automotive lower control arm bushing is used as a case study, and the editing results of the HHT and EWT methods are compared. It is demonstrated that greater time compression is achieved by the EWT-based approach in the reduced load spectrum, while close alignment with the original is maintained in terms of statistical parameters, power spectral density, cross-level counting, fatigue life, and damage distribution. The potential of the EWT-based method to improve the efficiency of durability bench tests for automotive components is highlighted, and a promising direction for load-spectrum editing in automotive engineering is introduced.
Dai et al. (2026) studied this question.