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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.

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).