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Expanding various future mobilities such as purpose built vehicle (PBV), urban air mobility (UAM), and robo-taxi, the application of autonomous driving system (ADS) technology is also spreading. The main point of ADS is to ensure safety by monitoring vehicle anomalies to prevent functional failure or accident. In this study, a model-based diagnosis and prognosis process was established using degradation data generated during autonomous driving simulation. A vehicle model was designed using Modelica/Dymola, and autonomous driving simulation was performed by integrating the lane keeping assistant (LKA) system with the vehicle model using Matlab/Simulink. Degradation data for the 3 components (a shock absorber damper, a suspension bush, and a tire) of the chassis system were input into the integrated simulation model. The degradation behavior was monitored with K-nearest neighbor (K-NN) and Gaussian mixture model (GMM).

Periodically, engine block-bearing cap structure is subject to the mixed bearing load from combustion and inertia mass of crank. Recently, due to the trend of lightness, cast steel is replaced with aluminum in the material of gasoline engine block. And, the load acting on the main bearing cap is rapidly rising due to the increase of engine power. Therefore, in the development stage, fretting fatigue failures frequently occurred on the block face contacted with the bearing cap. Fretting is a kind of wear which is occurred by micro relative movement. Even though various researches have been made to investigate fretting fatigue failure with FEA approaches, they are not enough to evaluate the phenomenon. In this study, the new CAE method simulating the fretting fatigue failure on the engine block face is developed and the mechanism of the fretting fatigue on the engine block is investigated.

A piston in a diesel engine is subject to the high pressure and the high thermal load. The high structural reliability is required to the piston in the automotive diesel engine and it is important to confirm the design parameters of piston in initial design stage. There are lots of research works proposing new geometries, materials and manufacturing techniques for engine pistons. But, the failures of piston occur frequently in development stage. Failure mechanisms are mainly fatigue related. This paper presents failure mechanisms of the high cycle fatigue and low cycle thermal fatigue cracks which occur on the piston during durability test using engine dynamometer. In this study, FE analysis was carried out to investigate the root cause of piston failure. The analysis includes the FE model of the piston moving system, temperature dependent material properties, mechanical and thermal loadings.

One of the most effective means of improving the thermal efficiency and the specific fuel consumption in spark ignition engines is the increase of the compression ratio. However, there is a limit to it because of the generation of knocking combustion due to the rise of temperature and pressure in the unburnt mixture. Also in turbo charged spark ignition engines, the ignition timing cannot be advanced until MBT in order to avoid the knocking phenomena. Generally speaking, it is very difficult to investigate the phenomena in an actual engine, because there are many restriction and the phenomena are too complex and too fast. According-ly, it is advantageous to reveal the phenomena fundamentally, including the autoignition process of the end-gas by using simplified model equipment. Therefore, a rapid compression and expansion machine (RCEM) with a pan-cake combustion chamber was designed and developed for the experiments presented here.