Work

E-booster transmission fault diagnosis via feature fusion

Year: 2025

Type: article

Abstract: Considering the characteristics of an E-Booster, such as its short working time and sealed internal components, a multi-domain feature fusion method was proposed to diagnose the fault of E-Booster by analyzing the vibration signal. The root mean square(RMS), crest factor and kurtosis of the vibration signal were extracted as time domain features. Using short-time Fourier transform (STFT), the vibration signal was transformed into frequency-domain. The maximum energy in the spectrogram and the extremum of energy ridge were extracted as frequency-domain features. The vibration signal was resampled with equal angles, and the resampled signal was transformed by Fourier transform to obtain the order spectrum, and the order information was extracted by segmenting and taking the extreme values, serving as angular-domain features; then, kernel principal component analysis (KPCA) was used to fuse multi-domain features; finally, the support vector machine (SVM) and eXtreme Gradient Boosting(XGBoost) were used to classify the multi-domain feature fusion information of the samples. Experimental verification shows that the fault diagnosis accuracy rate under multi-domain feature fusion information is 99.04%. Results shows that multi-domain feature fusion information is better than that of single-domain feature information and multi-domain feature information on the fault diagnosis accuracy rate, and can effectively detect the E-Booster transmission faults online.

Source: Measurement and Control

DOI: 10.1177/00202940251382422

Authors: Xixiang Wu, Weijun Fan, Bin Guo

Institution: China Jiliang 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: $2,000