中文摘要：

提出了一种基于变尺度级联单稳随机共振和盲源分离／独立分量分析（BBS／ICA）相结合的轴承故障诊断方法．首先，通过高频信号控制下的变尺度单稳随机共振将信号所含的噪声能量转化为信号能量，再用BSS／ICA分离残余噪声．理论分析及仿真结果表明：该方法能利用噪声来增强信号频率特征，使得大参数信号能从系统中获得更多能量，又能消除噪声，从而实现故障的有效诊断．试验台模拟了滚动轴承内圈及外圈故障，验证了该方法的有效性．

英文摘要：

A fault feature extraction method of rolling bearing based on cascaded mono-stable scale- transformation stochastic resonance and BBS/ICA was proposed. Noise energy was first transformed into noise signal energy through cascaded mono-stable scale-transformation stochastic resonance under the control of high frequency signal, then residual noise was separated from BSS/ICA. Theoretical analysis and numerical simulation results show that the approach can make use of noise to strengthen signal frequency characteristics, make great parameters signal get more energy from the system, and eliminate noise to achieve effective fault diagnosis. Simulation analysis indicates that this mono-stable system can effectively detect impact signal similar to rolling bearing fault, which is useful for engineering application. Simulation test performs in inner and outer fault of rolling bearing, and results sufficiently show the effectiveness of this approach.