Fuel System Fault Research Articles

Research on the impact of new OBD diagnostic items on the emission of China 6 light-duty vehicles

The new diagnostic items such as fuel system, VVT and cold start emission reduction strategy are tested for several vehicles meeting the requirements of the China 6 emission standards of light-duty vehicles. The test results show that the fuel system fault will increase the emissions of NMHC, NOx, CO and other pollutants by 0.7-7.5 times, and the emissions of lean mixture, rich mixture and uneven cylinder to cylinder faults will increase to different degrees; VVT fault will increase NMHC and CO emissions by 1.2-2.9 times and NOx by 1.6-6.1 times; The failure of cold start emission reduction strategy will increase NMHC, NOx and CO emissions by 4.2-9.0 times and NOx by 2.1-2.2 times; It is necessary to strengthen the monitoring of these diagnostic items and tighten the requirements in the future emission standards.

Experimental study on the emission influence of the fuel system faults for light-duty vehicle

The fuel system is a very important system in light-duty vehicles. The faults of the system will cause serious deterioration of vehicle emissions and other performance. In order to study the influence of different types of faults on emissions, this paper studies the faults of several OBDII vehicles. The results show that: the fault of rich mixture in fuel system will increase the emissions of CO and NMHC by 3-5 times and the emissions of NOx by 1-2 times; the fault of lean mixture in fuel system will increase the emissions of NOx by 4-5 times and the emissions of CO and NMHC by 0.7-2.5 times; there are two kinds of imbalance faults, one is imbalance lean and the other is imbalance rich. Imbalance lean mainly causes NOx deterioration, which is increased by 7.5 times. Imbalance rich mainly causes CO and NMHC emissions deterioration, which is increased by about 3 times. There are many kinds of faults in the fuel system which cause serious deterioration of emissions. It is necessary to focus on monitoring the faults of the fuel system.

Deep quantum inspired neural network with application to aircraft fuel system fault diagnosis

Fault diagnosis for aircraft fuel system can not only improve flight security, but also reduce the huge cost due to regular maintenance. It remains a problem because of the complicated system and the heterogeneous failure modes, especially the different failure modes that have similar impacts on the system. This paper uses the deep quantum inspired neural network (DQINN) which is an improved deep quantum network (DQN) to solve such problem. This method is the combination of classical deep belief network (DBN) and quantum inspired neural network (QINN). For the purpose of inheriting the advantages of DBN and QINN, the structure of DQINN is built in a new fashion. From a system perspective, the DQINN is constructed by the linear superposition of multiple DBNs with quantum intervals in the last hidden layer. Experiments conducted on standard datasets show that DQINN outperforms other three classical algorithms. Finally, a normal model of aircraft fuel system is built and four kinds of common failure modes of the core components are injected into this model, respectively. And the DQINN is applied to the aircraft fuel system fault diagnosis.

Fault diagnosis for large diesel fuel engine based on chaotic fractal method

In contrast with the large diesel engine fuel system operational condition complexity, the chaotic fractal characteristics in the combustion process of the diesel engine fuel system are systematically analyzed. Then, based on chaotic fractal method, an effective fault diagnosis method is further proposed. And under the two kinds of typical failure modes, the two important characteristic parameters variation trend for chaotic system, namely, correlation dimensions and the calculation methods of the maximum Lyapunov exponent are discussed. On the process of chaotic parameters calculation the wavelet noise reduction method is used to handle the original signal to conquer the linear scaling region properly. The results show that the maximum Lyapunov exponent method is a more effective method on the diesel engine fuel system fault diagnosis than the correlation dimension method, which can be further used in health condition assessment for this type fuel engine.

Electronic Returnless Fuel System Fault Diagnosis and Isolation: A Data-Driven Approach


 
 
 The Electronic Return-less Fuel System (ERFS) manages the delivery of fuel from the fuel tank to the engine. The pressure in the fuel line is electronically controlled by the fuel system control module by speeding up or slowing down the fuel pump. This allows the system to efficiently control the amount of fuel provided to the engine when compared to vehicles equipped with a standard fuel system wherein the fuel pump continuously runs at full speed. A failure in the fuel system that impacts the ability to deliver fuel to the engine will have an immediate effect on system performance. Consequently, improved reliability and availability, and reduction in the number of walk-home situations require efficient fault detection, isolation and prognosis of the ERFS system. This paper develops and implements data-driven fault detection, isolation and severity estimation algorithms for the ERFS. The HIL Fuel System Rig and a Chevrolet Silverado truck were used to collect and analyze the fuel system behavior under different fault conditions. Several data-driven classifiers, such as support vector machines, K- nearest Neighbor, Discriminant analysis, Bayes classifier, Partial- least squares, Quadratic and Linear classifiers, were implemented on a limited set of data for both training and testing. Regression techniques, such as Partial least squares regression and Principle component regression, are used to estimate the severity of faults. The resulting solution approach has the potential to be applicable to a wide variety of systems, ranging from automobiles to aerospace systems.
 
 

Use of COTS Functional Analysis Software as an IVHM Design Tool for Detection and Isolation of UAV Fuel System Faults

This paper presents a new approach to the development of health management solutions which can be applied to both new and legacy platforms during the conceptual design phase. The approach involves the qualitative functional modelling of a system in order to perform an Integrated Vehicle Health Management (IVHM) design – the placement of sensors and the diagnostic rules to be used in interrogating their output. The qualitative functional analysis was chosen as a route for early assessment of failures in complex systems. Functional models of system components are required for capturing the available system knowledge used during various stages of system and IVHM design. MADeTM (Maintenance Aware Design environment), a COTS software tool developed by PHM Technology, was used for the health management design. A model has been built incorporating the failure diagrams of five failure modes for five different components of a UAV fuel system. Thus an inherent health management solution for the system and the optimised sensor set solution have been defined. The automatically generated sensor set solution also contains a diagnostic rule set, which was validated on the fuel rig for different operation modes taking into account the predicted fault detection/isolation and ambiguity group coefficients. It was concluded that when using functional modelling, the IVHM design and the actual system design cannot be done in isolation. The functional approach requires permanent input from the system designer and reliability engineers in order to construct a functional model that will qualitatively represent the real system. In other words, the physical insight should not be isolated from the failure phenomena and the diagnostic analysis tools should be able to adequately capture the experience bases. This approach has been verified on a laboratory bench top test rig which can simulate a range of possible fuel system faults. The rig is fully instrumented in order to allow benchmarking of various sensing solution for fault detection/isolation that were identified using functional analysis.

Fault Diagnosis of PT Fuel System Based on Particle Swarm Optimization-Support Vector Machine

In order to overcome the difficulty in selecting parameters of support vector machine (SVM) when modeling the PT fuel system fault diagnosis, SVM optimized by particle swarm optimization (PSO) algorithm was proposed. The PSO-SVM model was established and the fault multi-classifiers of the SVM were got. The pressure signal of the PT fuel inlet and outlet at different rotational speed and conditions was collected. The algorithm of PSO-SVM was used to train and recognize the pressure signal. The result of experiment confirms the validity of this method through comparison of the BP-NN, SVM and the PSO-SVM.

A fuzzy diagnosis and advice system for optimization of emissions and fuel consumption

In this study, a fuzzy expert system has been developed, which is used for defining possible fuel system faults, ignition system faults, intake valve and exhaust valve faults and refers solution advice for these faults, which uses measurements of CO, HC, CO2, O2 and air ratio (@l). System gives decisions and advice by using rule base, which is prepared theoretic knowledge and experimental results as fuzzy. These decisions and advice are similar to expert views in a very high rate.