参数调节随机共振在机械系统早期故障检测中的应用

Application of Parameter-tuning Stochastic Resonance for Detecting Early Mechanical Faults

随机共振是一种利用噪声使微弱信号增强传输的非线性现象,与线性方法相比能够检测更低信噪比的信号。为了准确捕捉表征机械早期故障的特征信号,在分析双稳系统参数及检测信号幅值对随机共振检测性能影响规律基础上,以输入信噪比为变量、信噪比增益为信号增强程度衡量指标,提出一种自适应调节系统参数的随机共振微弱信号检测新方法,讨论了该方法的基本原理及实现步骤。将该方法用于转子碰摩故障早期检测,结果表明该方法简单稳健、实时性好,在短数据条件下能把信噪比较低的周期信号从强背景噪声中可靠地提取出来。

Stochastic resonance （SR） is a kind of nonlinear phenomenon. Through the enhancement of the transmission of weak input signal through nonlinear system, SR c＇an detect signals with lower signal-to-noise ratio （SNR） than linear methods by converting partial energy of noise into signal energy. In order to catch the characteristic signal of mechanical faults as early and accurately as possible, a new method is presented to detect weak signal buried in heavy noise based on stochastic resonance （SR） theory, Taking the SNR of input signals as variable and SNR improvement as the estimating criterion of signal amplifying degree, this method can adjust system parameters adaptively to detect the weak signal with different frequency. The principle and approach of this method are simply illustrated. This method is applied to detecting the weak frequency component signals characterizing the inception of rub-impact fault of rotor system. The result shows that the weak sinusoid signal of lower signal-to-noise ratio can be reliably extracted from heavy noise when the data length is shorting, and this method is simple and robust.