Simultaneous-fault detection based on qualitative symptom descriptions for automotive engine diagnosis

Vong, Chi‐Man; Wong, Pak Kin

doi:10.1016/j.asoc.2014.05.014

Cited by 27 publications

(9 citation statements)

References 27 publications

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“…In this research, fuel injection faults have been considered. Vong et al [6] have detected some automotive engine faults using a framework called fuzzy and probabilistic simultaneous-fault diagnosis (FPSD). Wang et al [7] have proposed an intelligent method for engine fault diagnosis using HilbertHuang transform (HHT) and support vector machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

Spark plug fault recognition based on sensor fusion and classifier combination using Dempster–Shafer evidence theory

et al. 2015

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Section: Introductionmentioning

confidence: 99%

Spark plug fault recognition based on sensor fusion and classifier combination using Dempster–Shafer evidence theory

et al. 2015

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“…The degree of electrification of vehicle engine is increasing and its structure is also becoming more and more complicated [2]. In addition, the requirements of the technology and assembling of engine are very high.…”

Section: Introductionmentioning

confidence: 99%

Control change cause analysis for engine fault diagnosis

Yang

Song

et al. 2016

2016 IEEE 13th International Conference on Networking, Sensing, and Control (ICNSC)

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Engine is the power source and the heart of the vehicle and it can ensure the safety and reliability of the whole vehicle dynamic system. Engine structure is becoming more and more complex, and it is difficult to make fault diagnosis after fault happened. In order to complete engine fault diagnosis quickly and accurately, in this paper, a new method of cause analysis has been proposed to make fault diagnosis, and this method is control change cause analysis (3CA). 3CA is a method which can be used to the cause analysis of different types of accidents and faults. 3CA is based on the process of control analysis and change analysis. Applying control change analysis to analyze engine fault can get the barrier and control which can prevent the fault happening again, the change of the engine performance and the importance ranking of the fault phenomenon, which provides a basis for the cause analysis of the fault.

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“…With the working performance of the engine of the vehicle to improve the degree of automation, continuous improvement, its structure is becoming more and more complex, and the working environment is bad, the fault of the vehicle engine has diversity, frequency, uncertainty and destructive characteristics of the fault recognition is more and more difficult. It is of great significance to explore the reliable and accurate method of engine fault identification for the safety of human and vehicle, environmental pollution and so on [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Vehicle Engine Based on Analytic Hierarchy Process and Neural Network

Du¹,

Wei²

2018

Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)

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Due to the nonlinear relationship between vehicle engine fault characteristics, traditional methods can not accurately describe the correlation characteristics between different features, it is very difficult to achieve accurate diagnosis of vehicle engine fault. In order to improve the accuracy of vehicle engine fault diagnosis, a new fault diagnosis method for vehicle engine AHP and neural network based on extracted characteristics of the vehicle engine fault diagnosis, then use AHP to determine the weight value of the vehicle engine fault diagnosis, the fault diagnosis of vehicle engine features as the input of neural network, the establishment of classification engine fault diagnosis of the vehicle, the vehicle engine fault diagnosis simulation experiment, the results show that this method improves the accuracy of engine fault diagnosis, fault diagnosis and accelerate the speed of the vehicle engine, has important practical significance.

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Simultaneous-fault detection based on qualitative symptom descriptions for automotive engine diagnosis

Cited by 27 publications

References 27 publications

Spark plug fault recognition based on sensor fusion and classifier combination using Dempster–Shafer evidence theory

Spark plug fault recognition based on sensor fusion and classifier combination using Dempster–Shafer evidence theory

Control change cause analysis for engine fault diagnosis

Fault Diagnosis of Vehicle Engine Based on Analytic Hierarchy Process and Neural Network

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