Yu Jiang / China University of Mining and Technology
Hua Zhu / China University of Mining and Technology
Currently the coal resource plays a vital role in supporting national economic development. Therefore, the coal machines fault diagnosis are getting increasing attention due to the harsh working condition. As is known that the gearbox regarded as indispensable transmission part most commonly broken down on account of burden the cutting counter-force from the coal rock inevitably. Actually, some rules inherently exist in the dynamic evolution process from the normal to appear different fault for the gearbox system. It is crucial to investigate and explore the gearbox dynamic behavior further effectively to detect the faults in advance.The coal machine is a typical chaotic system, but the chaotic features can not be displayed by the one-dimension vibration signals which affect the fault diagnosis accuracy to some extent. The paper aims at the lack of the chaotic features extraction research especially on the character and dynamic behavior evolution in the high dimension. To realize this goal, the chaotic attractors of the gearbox under different condition have been reconstructed in the high dimension. Then the chaotic characterization parameters have been calculated to explore the chaotic features which can reveal the dynamic behavior evolution mechanism further to identify the gearbox system condition. The results demonstrated the effectiveness of chaotic attractors for the gearbox pattern recognition and fault diagnosis. Moreover, it is worth mentioning that the method in this paper open a new theoretical perspective to investigate the system nature characters and have a wide potential application.