针对高速列车系统早期故障发生时特征信号微弱且淹没在强背景噪声之中的问题,提出了一种状态转移自适应随机共振方法.该方法解决了传统随机共振固定参数或只对单一参数进行优化、忽略参数之间交互作用的不足.首先,利用移频变尺度对实际大信号进行预处理,使信号满足绝热近似小参数的要求;然后,以信噪比作为优化目标,采用状态转移随机共振对系统参数进行同步优化;最终,实现转子断条故障微弱特征信号的最优提取和频率恢复.通过对牵引电机早期转子断条实验进行比较,结果表明,所提方法的微弱信号提取效果明显优于其他算法,验证了该方法的有效性.%In the early fault period of high-speed train systems, the interested characteristic signals are relatively weak and easily submerged in heavy noise. In order to solve this problem, a state-transition-algorithm (STA)-based adaptive stochastic resonance (SR) method is proposed, which provides an alternative solution to the problem that the traditional SR has fixed parameters or optimizes only a single parameter and ignores the interaction between parameters. To be specific, the frequency-shifted and re-scaling are firstly used to pre-process an actual large signal to meet the requirement of the adiabatic approximate small parameter. And then, the signal-to-noise ratio is used as the optimization target, and the STA-based adaptive SR is used to synchronously optimize the system parameters. Finally, the optimal extraction and frequency recovery of a weak characteristic signal from a broken rotor bar fault are realized. The proposed method is compared with the existing methods by the early broken rotor bar experiments of traction motor. Experiment results show that the proposed method is better than the other methods in extracting weak signals, and the validity of this method is verified.
展开▼
