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Sensors 2018, 18(9), 2917; https://doi.org/10.3390/s18092917

Electronic Systems Diagnosis Fault in Gasoline Engines Based on Multi-Information Fusion

1
Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
2
Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan 430070, China
3
Shanghai E-propulsion Auto Technology Co. Ltd., Shanghai 210800, China
4
Cummins East Asia Research & Development Co. Ltd., Wuhan 430070, China
*
Author to whom correspondence should be addressed.
Received: 24 July 2018 / Revised: 29 August 2018 / Accepted: 30 August 2018 / Published: 3 September 2018
(This article belongs to the Section Intelligent Sensors)
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Abstract

The rapid development of electronic techniques in automobile has led to an increase of potential safety hazards, thus, a strong on-board diagnostic (OBD) system is desperately needed. To solve the problem of OBD insensitivity to manufacture errors or aging faults, the paper proposes a novel multi information fusion method. The diagnostic model is composed of a data fusion layer, feature fusion layer, and decision fusion layer. They are based on the back propagation (BP) neural network, support vector machine (SVM), and evidence theory, respectively. Algorithms are mainly focused on the reliability allocation of diagnostic results, which come from the data fusion layer and feature fusion layer. A fault simulator system was developed to simulate bias and drift faults of the intake pressure sensor. The real vehicle experiment was carried out to acquire data that are used to verify the availability of the method. Diagnostic results show that the multi-information fusion method improves diagnostic accuracy and reliability effectively. The study will be a promising approach for the diagnosis bias and drift fault of sensors in electronic control systems. View Full-Text
Keywords: gasoline engine; electronic control system; fault diagnosis; multi-information fusion gasoline engine; electronic control system; fault diagnosis; multi-information fusion
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Hu, J.; Huang, T.; Zhou, J.; Zeng, J. Electronic Systems Diagnosis Fault in Gasoline Engines Based on Multi-Information Fusion. Sensors 2018, 18, 2917.

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