针对滚动轴承早期故障信号相对微弱,经常淹没在环境噪声中而不易识别和提取的问题。提出了一种基于Chen系统的滚动轴承早期微弱故障检测方法。首先,研究了Chen系统的非线性动力学特性,发现参数和初值的微小变化都有可能引起吸引子形状的明显改变,因此可以用吸引子形状的改变表征系统的微小扰动的存在。然后,设计了基于Chen振子的微弱故障检测系统,根据Chen系统的吸引子在参数变化过程中形状有可能发生阶跃变化的特殊性质,设计了一种基于混沌吸引子形状阶跃变化的阈值选取方法,不仅实现了阈值的自动选取,还能自动识别输入信号中待测成分是否存在。最后,将基于Chen系统的微弱信号检测方法应用于滚动轴承早期微弱故障诊断中。仿真和实验表明,该方法能够有效识别滚动轴承早期微弱故障,具有实际应用价值。
Early fault signals of rolling bearings are relatively weak and often submerged in environmental noise. They are difficult to be recognized and extracted. An early weak fault detection method for rolling bearings based on the Chen system was proposed. Firstly, the nonlinear dynamic characteristics of the Chen system were studied, and it was found that slight changes in parameters and initial values may lead to obvious changes in the shape of attractors, so the change in the shape of attractors can be used to characterize the existence of small perturbations in the system. Then, the weak fault detection system based on the Chen oscillator was designed. According to the shape of the attractor of the Chen system, the shape of the system changes during the process of parameter change. A threshold selection method based on chaotic attractor shape step change was designed, which not only realizes automatic selection of the threshold, but also realizes the automatic recognition of the components to be tested in the input signal. Finally, the weak signal detection method based on the Chen system was applied to the early weak fault diagnosis of rolling bearings to judge the fault type. Simulation and experiments show that the method is effective and can accurately identify early weak faults of rolling bearings.