@Article{cmes.2023.027896,
AUTHOR = {Wangpeng He, Yue Zhou, Xiaoya Guo, Deshun Hu, Junjie Ye},
TITLE = {Sparsity-Enhanced Model-Based Method for Intelligent Fault Detection of Mechanical Transmission Chain in Electrical Vehicle},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {137},
YEAR = {2023},
NUMBER = {3},
PAGES = {2495--2511},
URL = {http://www.techscience.com/CMES/v137n3/53742},
ISSN = {1526-1506},
ABSTRACT = {In today’s world, smart electric vehicles are deeply integrated with smart energy, smart transportation and smart
cities. In electric vehicles (EVs), owing to the harsh working conditions, mechanical parts are prone to fatigue
damages, which endanger the driving safety of EVs. The practice has proved that the identification of periodic
impact characteristics (PICs) can effectively indicate mechanical faults. This paper proposes a novel model-based
approach for intelligent fault diagnosis of mechanical transmission train in EVs. The essential idea of this approach
lies in the fusion of statistical information and model information from a dynamic process. In the algorithm, a novel
fractal wavelet decomposition (FWD) is used to investigate the time-frequency representation of the input signal.
Based on the sparsity of the PIC model in the Hilbert envelope spectrum, a method for evaluating PIC energy ratio
(PICER) is defined based on an over-complete Fourier dictionary. A compound indicator considering kurtosis and
PICER of dynamic signal is designed. Using this index, evaluations of the impulsiveness of the cycle-stationary
process can be enabled, thus avoiding serious interference from the sporadic impact during measurements. The
robustness of the proposed approach to noise is demonstrated via numerical simulations, and an engineering
application is employed to validate its effectiveness.},
DOI = {10.32604/cmes.2023.027896}
}