WEAR FAILURE MECHANISM AND MULTI-IMPACT PROPERTY OF AUTOMOTIVE ENGINE CHAIN

The multi-impact characteristics and failure mechanism of two kinds of automotive engine chain made in China are studied through engine assembly and road-drive tests. The worn surface morphologies of rubbing area between pin, bush and roller are also analyzed based on scanning electron microscope. The results show that the main wear mechanism of automotive engine chain is fatigue wear, and its failure mechanism is the forming, extending and flaking of cracks on top layer of pin and bush. In addition, the material, hot-treatment method and shaping technique for roller have a great influence upon the resistance to multi-impact. Ensuring sufficient strength and plasticity of roller, as well as adopting suitable shaping technique are the effective method to increase its resistance to multi-impact.

Ignition Pattern Analysis for Automotive Engine Trouble Diagnosis Using Wavelet Packet Transform and Support Vector Machines

Engine spark ignition is an important source for diagnosis of engine faults.Based on the waveform of the ignition pattern,a mechanic can guess what may be the potential malfunctioning parts of an engine with his/her experience and handbooks.However,this manual diagnostic method is imprecise because many spark ignition patterns are very similar.Therefore,a diagnosis needs many trials to identify the malfunctioning parts.Meanwhile the mechanic needs to disassemble and assemble the engine parts for verification.To tackle this problem,an intelligent diagnosis system was established based on ignition patterns.First,the captured patterns were normalized and compressed.Then wavelet packet transform（WPT） was employed to extract the representative features of the ignition patterns.Finally,a classification system was constructed by using multi-class support vector machines（SVM） and the extracted features.The classification system can intelligently classify the most likely engine fault so as to reduce the number of diagnosis trials.Experimental results show that SVM produces higher diagnosis accuracy than the traditional multilayer feedforward neural network.This is the first trial on the combination of WPT and SVM to analyze ignition patterns and diagnose automotive engines.

EXPERIMENTAL STUDY ON HIGH-SPEED CHARACTERISTICS OF AUTOMOTIVE ENGINE OIL-PUMP CHAIN

The high-speed multi-cycle impact and speed, load fluctuant characteristics of a kind of narrow-width automotive engine oil-pump chain 06BN-1 are studied through engine assembly and road-drive tests to satisfy the light-weight demand of engine. The worn surface morphologies of rubbing area between pin, bush and roller are also analyzed based on scanning electron microscope. The results show that the main wear mechanism of automotive engine oil-pump chain is fatigue wear, and it＇s failure mechanism is the forming, extending and flaking of cracks on top layer of pin and bush. Pin and bush both occurred cycle-soften phenomenon, and roller occurred cycle-harden. Fretting wear is one of the most important ＂fall to pieces＂ failure causes of automotive chain. Ensuring sufficient strength and plasticity of roller, as well as adopting suitable shaping technology are the effective methods to increase its resistance to multi-cycle impact.

Identification of Axial Vibration Excitation Source in Vehicle Engine Crankshafts Using an Auto-regressive and Moving Average Model

Violent axial vibration of a vehicle engine crankshaft might lead to multiple defects to the engine.Much research on mechanism and control measures has been done on engines,such as using the dynamic stiffness matrix method,rayleigh differential method,and system matrix method.But the source of axial vibration has not been identified clearly because there are many excitation factors for the axial vibration of a crankshaft,such as coupled torsional-axial vibration and coupled bending-axial vibration,etc.In order to improve the calculation reliability and identify the excitation source of axial vibration of in vehicle engine crankshafts,this paper presents a method to identify the axial vibration excitation source of crankshafts for high speed diesel engines based on an auto-regressive and moving average（ARMA） model.Through determining initial moving average variables and measuring axial /bending/torsional vibrations of a crankshaft at the free-end of a 4-cylinder diesel engine,autoregressive spectrum analysis is applied to the measured vibration signal.The results show that the axial vibration of the crankshaft is mainly excited by coupled bending vibration at high speed.But at low speed,the axial vibration in some frequencies is excited primarily by torsional excitation.Through investigation of axial vibration source of engine crankshafts,calculation accuracy of vibration can be improved significantly.

Robustness Assessment and Adaptive FDI for Car Engine

A new on-line fault detection and isolation （FDI） scheme proposed for engines using an adaptive neural network classifier is evaluated for a wide range of operational modes to check the robustness of the scheme in this paper. The neural classifier is adaptive to cope with the significant parameter uncertainty, disturbances, and environment changes. The developed scheme is capable of diagnosing faults in on-line mode and the FDI for the closed-loop system with can be directly implemented in an on-board crankshaft speed feedback is investigated by diagnosis system （hardware）. The robustness of testing it for a wide range of operational modes including robustness against fixed and sinusoidal throttle angle inputs, change in load, change in an engine parameter, and all these changes occurring at the same time. The evaluations are performed using a mean value engine model （MVEM）, which is a widely used benchmark model for engine control system and FDI system design. The simulation results confirm the robustness of the proposed method for various uncertainties and disturbances.

Reverse metallurgical engineering towards sustainable manufacturing of vehicles using Nb and Mo alloyed high performance steels

Steel is the dominant construction material for most industrial goods such as equipments, structures, buildings or vehicles. Although there have been great advances in steel technology over the last 4 decades, the industry currently faces serious sustainability challenges with regard to energy conservation, reduction of CO2 emission and a generally more efficient use of resources. The principal connotation in this respect is increasing steel strength allowing to reduce component weight. However, it is also necessary considering in how far the modified steel properties interfere with typical manufacturing techniques established in the processing chain. A feasible method in this sense is a reverse approach starting from detailed knowledge of the manufacturing process and translating it back into the most suitable metallurgical and microstruc- tural design of steel. Modifying steels towards better manufacturing performance typically involves innovative alloying and metallurgical processing concepts. Niobium and molybdenum are two of the most powerful alloying elements in helping to adapt microstructures and properties with regard to downstream manufacturing processes. This paper will highlight several examples how a reverse met- allurgical engineering approach can be successfully applied to optimize the efficiency of subsequent manufacturing processes with a focus on the automotive industry.

Calibration of catalyst temperature in automotive engines over coldstart operation in the presence of different random noises and uncertainty： Implementation of generalized Gaussian process regression machine

The main scope of the current study is to develop a systematic stochastic model to capture the undesired uncertainty and random noises on the key parameters affecting the catalyst temperature over the coldstart operation of automotive engine systems. In the recent years, a number of articles have been published which aim at the modeling and analysis of automotive engines＇ behavior during coldstart operations by using regression modeling methods. Regarding highly nonlinear and uncertain nature of the coldstart operation, calibration of the engine system＇s variables, for instance the catalyst temperature, is deemed to be an intricate task, and it is unlikely to develop an exact physics-based nonlinear model. This encourages automotive engineers to take advantage of knowledge-based modeling tools and regression approaches. However, there exist rare reports which propose an efficient tool for coping with the uncertainty associated with the collected database. Here, the authors introduce a random noise to experimentally derived data and simulate an uncertain database as a representative of the engine system＇s behavior over coldstart operations. Then, by using a Gaussian process regression machine （GPRM）, a reliable model is used for the sake of analysis of the engine＇s behavior. The simulation results attest the efficacy of GPRM for the considered case study. The research outcomes confirm that it is possible to develop a practical calibration tool which can be reliably used for modeling the catalyst temperature.

AUTOMOTIVE DIESEL ENGINES IN CHINA

In China, only trucks with payload of 8 tons and over were equipped with diesel engines at the early stage. Since 1970s, diesel engines for medium trucks in China have been developed at an ever increasing speed, and in 1980s, light trucks also began to use diesel

Advanced ECU Software Development Method for Fuel Cell Systems

The electronic control unit （ECU） in electrical powered hybrid and fuel cell vehicles is exceedingly complex. Rapid prototyping control is used to reduce development time and eliminate errors during software development. This paper describes a high-efficiency development method and a flexible tool chain suitable for various applications in automotive engineering. The control algorithm can be deployed directly from a Matlab/Simulink/Stateflow environment into the ECU hardware together with an OSEK real-time operating system （RTOS）. The system has been successfully used to develop a 20-kW fuel cell system ECU based on a Motorola PowerPC 555 （MPC555） microcontroller. The total software development time is greatly reduced and the code quality and reliability are greatly enhanced.

Comparison of generalized engine control and MPC based on maximum principle

Automotive engine control has been continuously improved due to the strong demands from the society and the market since introducing electronic controls but not always following control theories. Therefore, it is not easy for researchers from academia and even engineers from the automotive industry to grasp the whole aspect of engine control. To encounter the issue, important features of engine control are extracted and generalized from the standpoint of control engineering. Comparisons of the control and model predictive control （MPC） showed an outstanding performance of the control generalized from engine controls and how to apply MPC in the framework.