In order to realize a premixed compression ignition (PCI) engine by utilizing bio-alcohol, combustion characteristics of bio-alcohol blended with gas oil were compared between ethanol and n-butanol in a diesel engine. The effects of the ethanol blend ratio and the butanol blend ratio on ignition delay, premixed combustion, diffusion combustion, fuel consumption and exhaust emissions such as smoke density, nitrogen oxide (NOx) and so on were investigated experimentally. It is found that ethanol almost burns out together with low evaporation temperature composition of gas oil in the premixed combustion period and the heat release in the diffusion combustion is based on mainly high evaporation temperature composition of gas oil, then, soot is formed in the diffusion combustion of gas oil. On the other hand, a part of butanol burns in the diffusion combustion, and the combustion of butanol in the diffusion stage is not the cause of soot formation. Butanol is more useful in diesel engine compared with ethanol because butanol can be blended with gas oil without surface-active agent, and fuel consumption and smoke are almost equal in both blend fuels if the alcohol blend ratio is the same.

Machine equipment usually comprises many mechanical elements that can fail because of functional deterioration and friction. For tribo-elements like plane bearings, it is extremely important to diagnose the abnormal conditions and prevent such parts from breakdown caused by wear. However, diagnosing tribo-elements requires expensive diagnostic equipment and expertise. This study aims to propose a cost- and time- effective system that detect the signs of breakdown during equipment operation by using machine learning to identify abnormalities. We conducted wear tests in contaminated oil and used multiple sensors to collect data regarding the friction force, the electrical contact resistance, the acoustic emission (AE) signal, and vibration. An appropriate learning sample was selected using k-fold cross-validation. The electrical contact resistance was found to contribute relatively little to the detection of abnormalities, whereas the friction coefficient contributed greatly. Furthermore, the AE signal and the vibration detected local changes on the sliding surface. Consequently, we found that machine learning can judge whether monitoring data are normal or abnormal.

A large quantity of engineering plastics are used in electric industrial products. It is necessary to estimate the life of the product made of engineering plastics because of its shorter life and lower strength than some of ceramics or metals. New test device was developed for accelerated stress test. It can make the unsaturated environment above 100 oC under atmospheric pressure using superheated water vapor. Then it becomes possible to set wide range condition of temperature and humidity. Polybutylene terephthalate (PBT) is one of the hydrolyzed engineering plastics. Test pieces of PBT were submitted to accelerated stress test under some conditions of elevated temperature and humidity. Then they were provided to three point bending test. The time required for strength degradation has a good relation with water vapor pressure at each temperature. Then it became clear that the time required for strength degradation on each water vapor pressure was accelerated with temperature using the Arrhenius type equation.