Journal Published Online: 22 August 2022
Volume 51, Issue 3

An Intelligent Fault Diagnosis Algorithm for Vehicle Internal Combustion Engines Based on Instantaneous Speed for a Smart City

CODEN: JTEVAB

Abstract

Affected by interference factors such as Gaussian noise, the traditional methods have the problems of inaccurate diagnosis results of unsteady vibration signals, high uncertainty of fault diagnosis, and low overall fault diagnosis accuracy. In this paper, a fault diagnosis algorithm of vehicle internal combustion engine based on instantaneous speed and machine learning is proposed. The instantaneous speed is measured by the hardware method. According to the processing results of instantaneous speed, the unsteady vibration signal of the vehicle internal combustion engine is analyzed, and the principal components of unsteady vibration are separated to suppress the interference of Gaussian strong noise. The running state of the vehicle internal combustion engine is identified by the wavelet transform method. According to the identification results, the fault diagnosis of the vehicle internal combustion engine is realized by the twin support vector machine classification algorithm in machine learning. The experimental results show that the minimum uncertainty coefficient of fault diagnosis in this algorithm is 0.08, the accuracy of the unsteady vibration signal diagnosis is higher, and the overall accuracy of fault diagnosis is lower.

Author Information

Ma, Baoqiu
Department of Mechanical and Electrical Engineering, Shijiazhuang University of Applied Technology, Shijiazhuang, China
Li, Jingli
Railway Locomotive & Car Department, Hebei Vocational College of Rail Transportation, Shijiazhuang, China
Liang, Jianwei
Department of Mechanical and Electrical Engineering, Shijiazhuang University of Applied Technology, Shijiazhuang, China
Liu, Peiyue
Department of Mechanical and Electrical Engineering, Shijiazhuang University of Applied Technology, Shijiazhuang, China
Hou, Lifeng
Department of Mechanical and Electrical Engineering, Shijiazhuang University of Applied Technology, Shijiazhuang, China
Zhao, Lei
Department of Mechanical and Electrical Engineering, Shijiazhuang University of Applied Technology, Shijiazhuang, China
Liu, Jiangran
Department of Mechanical and Electrical Engineering, Shijiazhuang University of Applied Technology, Shijiazhuang, China
Pages: 12
Price: $25.00
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Details
Stock #: JTE20220099
ISSN: 0090-3973
DOI: 10.1520/JTE20220099