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The fluid film between piston and cylinder works simultaneously as bearing and lubricating functions, its thickness determines the lubrication efficiency and performance of an axial piston pump. Because of the thickness is usually micron level, and its change in the whole cycle is difficult to observe directly. In order to reveal it particularly, this paper present a mathematical model which considering structure, piston kinematics and a dynamic piston load to predict the film thickness in a cycle. The minimum thickness is used as an index to evaluate lubrication performance and its variation are studied under different load pressure, rotating speed and length ratio between cylinder and piston. A special test rig with four displacement sensors placed through the cylinder surface allows measuring the film thickness at these points. The model was verified by comparing the numerical results with measurements taken on the test rig. Results indicate that: (1) The mathematical model could effectively predict the film thickness trend and the position of the minimum thickness; (2) The minimum thickness in pressure phase is greater than in suction phase, and increases with the increase of load pressure and decreases with the increase of rotation speed, the influence of load pressure on film thickness is much greater than that of rotational speed. (3) Increasing the length ratio reduces the piston eccentric and raises the minimum film thickness, and in order to obtain the better lubrication, the length ratio should be greater than 0.5.

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Brain fatigue is often associated with inattention, mental retardation, prolonged reaction time, decreased work efficiency, increased error rate, and other problems. In addition to the accumulation of fatigue, brain fatigue has become one of the important factors that harm our mental health. Therefore, it is of great significance to explore the practical and accurate brain fatigue detection method, especially for quantitative brain fatigue evaluation. In this study, a biomedical signal of ballistocardiogram (BCG), which does not require direct contact with human body, was collected by optical fiber sensor cushion during the whole process of cognitive tasks for 20 subjects. The heart rate variability (HRV) was calculated based on BCG signal. Machine learning classification model was built based on random forest to quantify and recognize brain fatigue. The results showed that: Firstly, the heart rate obtained from BCG signal was consistent with the result displayed by the medical equipment, and the absolute difference was less than 3 beats/min, and the mean error is 1.30 ± 0.81 beats/min; secondly, the random forest classifier for brain fatigue evaluation based on HRV can effectively identify the state of brain fatigue, with an accuracy rate of 96.54%; finally, the correlation between HRV and the accuracy was analyzed, and the correlation coefficient was as high as 0.98, which indicates that the accuracy can be used as an indicator for quantitative brain fatigue evaluation during the whole task. The results suggested that the brain fatigue quantification evaluation method based on the optical fiber sensor cushion and machine learning can carry out real-time brain fatigue detection on the human brain without disturbance, reduce the risk of human accidents in human–machine interaction systems, and improve mental health among the office and driving personnel.

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