自适应随机共振新方法及其在故障诊断中的应用

New Adaptive Stochastic Resonance Method and Its Application to Fault Diagnosis

系统参数的选择对随机共振方法的优劣起着决定性的作用。已有的随机共振方法在选择参数过程中存在着致命的问题,例如人为主观选择参数,或者只对单一参数进行自适应优化,而忽略了参数之间的交互作用。为了解决以上问题,提出一种新的自适应随机共振方法。与已有方法相比,该方法的优势在于利用蚁群算法优良的寻优特性,能并行选择和优化随机共振系统的多个参数,考虑了参数之间的交互作用,自适应地实现与输入信号最佳匹配的随机共振系统。因此该方法解决了已有方法在参数选择中存在的问题,从而能更有效地削弱信号中的噪声并增强微弱特征,实现早期故障准确诊断。通过仿真试验和机车轴承早期故障诊断的工程应用,表明提出的方法在微弱特征检测与早期故障诊断中取得了比已有方法更好的效果。

The performance of stochastic resonance methods is mostly decided by its system parameters.The existing stochastic resonance methods have the fatal problems;for example,subjectively selecting parameters or optimizing only one parameter therefore ignoring the interactive effect between parameters.To solve the problems mentioned above,a new adaptive stochastic resonance method is proposed.Compared with the existing methods,the proposed method utilizes the optimization ability of ant colony algorithms,synchronously selecting and optimizing multiple system parameters and considering the interactive effect between parameters,and adaptively realizes the optimal stochastic resonance system matching input signals.Thus,the problems in selecting parameters are solved by using the proposed method.Therefore noise is weakened and weak characteristics are enhanced effectively,and the early faults are diagnosed accurately as well.Both simulations and a real case of locomotive rolling element bearings with an early fault demonstrate that the proposed adaptive stochastic resonance method obtains the improved results compared with the existing methods.