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• Test result of pressure sensor for high speed train will be affected by vibration. • Simulation of actual vibration condition of train body with large mass method. • Simulation of pressure sensor voltage output by electrical structure simulation. • Vibration with same amplitude and different frequency has different influence. • Linear simplified model can accurately calculate vibration effect. This paper devotes to investigate the influence of vibration acceleration on measurement of piezoresistive pressure. For the sake of definiteness, sensor operation conditions in high-speed train are considered. Where, the sensor chip is loaded with dynamic accelerations with frequency within 20 Hz. The real state of the pressure sensor under different frequency vibration load is simulated by using large mass method (LMM). And the voltage output of the pressure sensor under different vibration loads was obtained by the joint simulation of electrical- structure. By analysis of different vibration loads and the voltage output of sensors, their internal relations are obtained. These outcomes provide a guidance for elimination of vibration influence in high-speed train surface fluctuation pressure measurement. It is also useful in other fields of pressure sensor operation in acceleration-loaded environments. [ABSTRACT FROM AUTHOR]

Highlights • The chaotic features of the signals of high speed trains are extracted. • A feature extraction method based on MPE-LTSA is proposed. • The proposed method is also applied to extract features in high speed trains. • This method can be used in other feature recognition for complex chaotic signals. Abstract Hunting stability is an important factor for high-speed trains to achieve safe operation, which can be monitored by on-board instruments. When analysing measured online tracking data of high-speed trains, the authors have observed that small amplitude hunting tend to appear. When these signals show growth of lateral vibration to high enough amplitude, train derailment would happen. Research on the bifurcation evolution of small amplitude hunting of high-speed trains has been rarely reported so far. In this paper, chaotic features of the data are extracted and the results show that lateral acceleration signals from the bogie frame has strong nonlinear characteristics. Then a commonly used method based on frequency distribution characteristics of bogie vibration energy is first used to separate different states of hunting. However, the results are not satisfactory. So a feature extraction method based on Multiscale Permutation Entropy (MPE) and Local Tangent Space Alignment (LTSA) is proposed to distinguish the different states of complex signals. The proposed method is applied to extract features of the small amplitude hunting signals at high-speed of 320–350 km/h. The results show that the MPE-LTSA method can identify the bifurcation evolution of small amplitude hunting signals much more effectively than the method based on the MPE-ISOMAP (Isometric Feature Mapping) and MPE-PCA (Principle Component Analysis). The method can be used in other feature recognition for the complex chaotic signals. [ABSTRACT FROM AUTHOR]