Automobile Fault Diagnosis Research Articles

ICA-Feature-Extraction-Based Fault Identification of Vehicular Starter Motor

The starter motor is responsible for providing the mechanical rotating energy for automotive operation through electromechanical conversion via battery. In case of a fault on the starter motor, the vehicle will fail to operate posing problems for the driver, especially for an emergency service vehicle like an ambulance. The starter motor and its corresponding components are prone to mechanical as well as electrical faults due to high starting torque and friction with the flywheel and gear teeth of the starter. In this paper, the focus has been given to the electrical fault of a starter motor and its effect on the operational characteristics of an automobile. The response characteristics thus obtained have been evaluated by independent component analysis (ICA) based feature extraction analysis to obtain a continuous data set for automobile fault diagnosis. The feature analysis of the response characteristics helps in easier calculation while being effective in designing a system that can help with vehicle diagnosis and quality control.

Fault Diagnosis of Automobile Engine Based on Improved BP Neutral Network

With the continuous enrichment of automobile functions and the complexity of automobile structure, the difficulty of automobile fault diagnosis is constantly increasing. The study of fault diagnosis methods with high real-time performance, accuracy, and predictability is of great significance to improve automobile safety performance and ensure driving safety. Since the convergence speed of the traditional BP neural network algorithm is slow and the accuracy is insufficient in the process of automobile engine fault diagnosis, this paper improves convergence speed of the algorithm by introducing the momentum term, and the weights and thresholds of the neural network are optimized by using GA selection, crossover, and genetic characteristics, to propose a genetic algorithm (GA) optimization BP neural network fault diagnosis method. The average absolute error of the traditional BP neural network algorithm is 0.5976, while the average absolute error of the improved BP neural network algorithm in this paper is 0.1027. The comparative simulation results show that the proposed improved algorithm is better than the traditional BP neural network algorithm in diagnosis precision, accuracy, and other key indicators.

Compiling method of automobile fault diagnosis information

Automobile fault diagnosis is the most technical content in automobile maintenance. The speed, accuracy and convenience of diagnosis directly affect the maintenance efficiency. Automobile fault diagnosis information is an important reference for finding and solving automobile faults. High quality information is the key to improve technical personnel's judgment and solving faults. In this paper, the methods and examples of fault diagnosis information compilation are given to guide the technical data writers to compile effective fault diagnosis information for automobiles.

Application Analysis of Artificial Intelligent Neural Network Based on Intelligent Diagnosis

In recent years, the continuous development of computer science and AI technology makes the application prospect of artificial intelligence in fault diagnosis emerge. As a simulation technology of human thinking pattern, intelligent diagnosis technology can check and manage the monitoring target in real time to ensure the accuracy of data information. This paper introduces the basic principles of key artificial intelligence technologies in the field of sports, such as convolutional neural network, object detection, object tracking and action recognition. Then it analyzes the application status of intelligent diagnosis technology and artificial intelligence network under intelligent diagnosis, and puts forward the application of artificial intelligence neural network in automobile fault diagnosis based on examples. In the construction of the neural network system, the real-time collection of vehicle operation data can be analyzed, once the fault is found, the driver can be notified in time to avoid safety accidents. The author summarizes the existing research results on the application of artificial intelligence algorithm in intelligent diagnosis, in order to provide help for the subsequent research.

Taking Comprehensive Experiments as the Starting Point to Improve the Quality of Training Applied Undergraduate Talents—— Taking the Automobile Detection and Fault Diagnosis Course as an Example

Taking Comprehensive Experiments as the Starting Point to Improve the Quality of Training Applied Undergraduate Talents—— Taking the Automobile Detection and Fault Diagnosis Course as an Example

Design and Development of Automobile Fault Diagnosis System Using AI Machine Learning

Design and Development of Automobile Fault Diagnosis System Using AI Machine Learning

The Research of Improved BP Neural Network in Automobile Sensor Fault Diagnosis

Fault control diagnosis object of automobile sensor has the problems of big space dimension of fault sample data and poor real-time capability of fault diagnosis, etc, in order to solve these problems, this text introduces static fuzzy control method to diagnose automobile fault based on BP neural network, first finding fault node by static fuzzy self-learning method and classifying and diagnosing intelligently by BP network. Experiments show that method in this paper improves the accuracy of automobile sensor fault diagnosis and reduces diagnosis error, thus improves the recognition and decision-making ability of diagnosis.

Automotive Remote Fault Diagnosis Using the Technology of Artificial Intelligent Neural Network

With the rapid development of automobile electronic technology, automotive interior structure and mutual communication relations are becoming increasingly complex; meanwhile,users’ requirements for timeliness and accuracy in automobile fault diagnosis are also getting higher, thus increasing the difficulty and uncertainty of automobile fault diagnosis process. The automobile fault clustering fusion system with artificial intelligent neural network as the core is established for fault diagnosis, which not only meets the requirement for timeliness, but also makes after-sale diagnosis service intelligent and user-friendly. This system will have a promising development in future.

Automotive Wire Control Steering Sensor Fault Detection Algorithm Research

This article to cancel after the mechanical connections between steering wheel and steering, wire control steering system security and reliability problems, put forward on the basis of the analytical redundancy software sensor method of wire control steering system. In order to solve the compared with the traditional steering system in terms of reliability and safety of the problems of structural changes, the wire control steering system of the main sensor fault diagnosis methods are studied. In wire control steering system associated with the vehicle dynamics model is established under the premise of hypothesis testing to double adaptive fading Kalman filtering technology as a platform, combined with according to the working state of each sensors to determine fault feature vector, to build the main sensor wire control steering automobile fault diagnosis method of residual error threshold. For fault diagnosis of automobile EPS sensor, the BP neural network is put forward to EPS sensor for auto are introduced in the fault diagnosis. For large-scale wireless sensor networks (WSN), reduce the fault detection accuracy, and larger load of communication problems, according to the spatial and temporal correlation characteristics of sensor nodes, proposes a distributed sensor fault detection algorithm based on cluster. These algorithms for sensor fault detection is of great significance.

Design Car Fault Diagnosis System

Based on the domestic and foreign automobile fault diagnosis status and trends were compared in detail on Syria to study a low-power , high-precision , easy to carry and apply a wide degree of automotive fault diagnostic . This paper aims to study the C8051F020 fault diagnostic , fault depth diagnostic hardware and software design , understanding automotive fault diagnostic process .

Automotive Fault Diagnosis Based on the Fuzzy Mathematics

Automobile fault diagnosis is one of the hot issues in detection and diagnosis technology of automobile. The automobile fault has the characteristics of uncertainty and complexity problems. To solve the problems of qualitative description from drivers into quantitative, fuzzy mathematics is introduced. In addition, the fuzzy relationship of automobile fault and fault causes is expressed by setting the membership functions and fuzzy relation matrix. Finally, the qualitative analysis of real cases is present. The fuzzy theory and expert system for automobile fault diagnosis method is proved accurately through the algorithm analysis and real cases.

Development of Algorithms for Efficient Energy Management in the Industrial Common Rail Engine

In order to meet the recently enhanced emission standards at home and abroad, it is necessary to develop the CRDI ECU control algorithm that users can adjust fuel injection timing and amount in response to their needs. Therefore, this study developed the simulator for knocking analysis that enables knocking discrimination and engine balance correction applicable to the ECU exclusive to the industrial CRDI engine. The purpose of this study is to provide the driver-oriented diagnostic services that enable drivers to diagnose vehicles directly by developing diagnostic devices for vehicles with the use of the results of the developed simulator for knocking analysis according to the OBD II standards. For this purpose, this study aims to improve the fuel efficiency of vehicles by proposing the S/W design method of the OBD-II diagnosis device that can provide real-time communications with the use of wired system and bluetooth module as a wireless system to send and receive automobile fault diagnosis signal and sensor output signal, and to suggest an improvement for engine efficiency by minimizing the generation of harmful exhaust gas

커먼레일 엔진에서 노킹 진단 알고리즘 구현 및 OBD-II 진단기 S/W 설계 방안

국내외의 배출가스 규제 강화에 부합하기위하여, 사용자 의도에 따른 연료 분사시기와 분사량 조절이 가능한 커먼레일 ECU를 제어할 수 있는 알고리즘 개발의 필요에 따라서 본 논문에서는 커먼레일 엔진 전용 ECU에 적용할 수 있는 노킹 판별 및 엔진 밸런스 보정이 가능한 노킹 진단 알고리즘을 구현하여 시뮬레이터로 개발하였다. 또한 운전자가 직접 차량을 진단하는 운전자 중심의 진단 서비스를 제공하고자 시뮬레이터의 결과를 OBD-II 표준에 의거하는 차량 위주의 진단기로 개발하고자 한다. 이를 위해 자동차 고장진단 신호 및 센서 출력 신호를 송수신하는 유선 시스템과 무선 시스템인 블루투스 모듈을 이용하여 실시간 통신이 제공될 수 있는 OBD-II 진단기 S/W 설계방안을 제안함으로써 차량의 연비향상 및 유해가스 저감을 통한 엔진의 효율성 향상을 실현하도록 한다. In order to meet the recently enhanced emission standards at home and abroad, it is necessary to develop the CRDI ECU control algorithm that users can adjust fuel injection timing and amount in response to their needs. Therefore, this study developed the simulator for knocking analysis that enables knocking discrimination and engine balance correction applicable to the ECU exclusive to the industrial CRDI engine. The purpose of this study is to provide the driver-oriented diagnostic service that enable drivers to diagnose vehicles directly by developing diagnostic devices for vehicles with ths use of the results of the developed simulator for knocing analysis according to the OBD-II standards. For this purpose, this study aims to improve the fuel efficiency of vehicles by proposing the S/W design method of the OBD-II diagnosis device that can provide real-time communcations with the use of wired system and bluetooth module as a wireless system to send and recevice automobile fault diagnosis signal and sensor output signal, and to suggest an improvement for engine efficiency by minimizing the generation of harmful exhaust gas.

Research on Scheme Evaluation of the Automobile Fault Diagnosis System

Based on the characteristics of the automobile diagnosis system scheme, this paper has established the parameter system of diagnosis system scheme evaluation. Aiming at automobile diagnosis system scheme is difficult to evaluate its condition because of its structure is complex and its monitoring parameters are complicated and various, a second order fuzzy comprehensive evaluation model is proposed. The case study results show that the proposed model is feasible and effective. Hence, the evaluated results can aid to automobile diagnosis system design.

Key Technologies Study for Automobile Fault Diagnosis Expert System

Expert system for automobile fault diagnosis is an intelligent system, and its key technologies are knowledge acquisition, knowledge representation and inference strategy. Based on large collection of papers which on abroad or on home, some resolved methods have been presented in this paper, including improving of traditional method such as automotive acquisition of fault rules, combined knowledge representation and inference method diversification, applying of new theory or new technology such as case-base expert system, fuzzy-base expert system, neural network-base expert system and action-base expert system and so on. And then put forward to that intellectualization, cyberization and integration are the future development direction of automobile fault diagnosis expert system.

Key Technologies Study for Automobile Fault Diagnosis Expert System

Expert system for automobile fault diagnosis is an intelligent system, and its key technologies are knowledge acquisition, knowledge representation and inference strategy. Based on large collection of papers which on abroad or on home, some resolved methods have been presented in this paper, including improving of traditional method such as automotive acquisition of fault rules, combined knowledge representation and inference method diversification, applying of new theory or new technology such as case-base expert system, fuzzy-base expert system, neural network-base expert system and action-base expert system and so on. And then put forward to that intellectualization, cyberization and integration are the future development direction of automobile fault diagnosis expert system.

Research of Automobile Quality Fault Diagnosis Method Based on CBR

CBR (Case-based reasoning) theory is applied to the automobile quality fault diagnosis field. Case description, case retrieve and case reuse are the main factors of CBR. It is realized by the methods of fault tree construction, discrete cases information entropy weight value calculation, discrete and multi-level semantic matching case retrieval and knowledge difference driving case reuse. The process of solving actual cases verify the validity and efficiency of CBR method in the quality fault diagnosis system.

Mining abnormal patterns from heterogeneous time‐series with irrelevant features for fault event detection

AbstractWe address the issue of detecting fault events in multivariate time series. We suppose the following realistic and challenging situations: (A) the features to which multivariate time series correspond are heterogeneous; (B) only a small number of examples of fault events are available in advance relative to a large number of normal examples; and (C) many features irrelevant to fault events are included. To resolve these situations, detecting faults specifically in machine systems such as automobile, train, etc. is usually required. We propose an algorithm to resolve the issue. Key ideas in it include: (1) transforming the time‐series for each feature into a sequence of anomaly scores, in order to map heterogeneous features to homogeneous ones (an anomaly score indicates the degree of anomalousness relative to an ordinal sequence) and then representing the pattern of a fault event in terms of fault anomaly score vectors; (2) selecting features specifying a fault event by means of iterative optimization using both normal and fault anomaly score vectors. We then monitor the degree of anomalousness with respect to target anomaly score vectors by comparing them with the abnormal patterns. We demonstrate the effectiveness of our proposed algorithm through an application to an actual automobile fault diagnosis data set as well as an artificial dataset. © 2009 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 2: 1‐17, 2009

Model based Fault Detection for an Active Vehicle Suspension

An automobile fault detection and diagnosis method is described for an active vehicle suspension system. The method is based on mathematical models of the suspension system. It is shown how the unknown parameters of these mathematical models can be obtained experimentally by parameter estimation. Furthermore, the semi-physical approach of the local linear neuronal network LOLIMOT is applied for the generation of parity equations. Both, parameter estimation and parity equations are then used for model based fault detection and identification. Various faults are simulated with test rig data and it is shown, how these faults are detected.

Intelligent Diagnosis Systems

This paper examines and compares several different approaches to design of intelligent systems for diagnosis applications. These include expert systems (or knowledge based systems), truth (or reason) maintenance systems, case based reasoning systems, and inductive approaches like decision trees, neural networks (or connectionist systems), and statistical pattern classification systems. Each of these approaches is demonstrated through the design of a system for a simple automobile fault diagnosis task. The paper also discusses the domain characteristics that influence the choice of a specific technique (or combination of techniques) for a given application.