QICA在齿轮箱工作模态识别和故障诊断中的应用

The Application of the QICA in the Operational Model Identification and Fault Diagnosis of Gearbox

机械结构工作模态参数识别和振动故障诊断是在工程应用中不可或缺的两个重要技术。借助于LMS齿轮箱故障振动实验,将一种量子优化的独立分量分析算法(Quantum Independent Component Analysis,简称QICA)应用于齿轮箱工作模态参数识别和故障诊断实验中。通过对两种实验的应用分析,结果表明QICA排除了噪声和混频的影响,克服了特征参量难以区分的问题,能够有效地识别出齿轮箱模态固有频率;QICA使微弱故障信息明显增强,并与PNN结合达到了故障诊断目的,使故障诊断可靠性明显提高。

Operational model analysis of mechanical structure and vibrant fault diagnosis are the most essential and indispensable techniques in engineering application.Through the gearbox vibration experiment based on the LMS testing system,this paper applies Quantum Independent Component Analysis （QICA）to the experiments of operational model analysis on gearbox as well as fault diagnosis based on the research object of gearbox.Through analysis of the two kinds of experiments,the application results shows that this optimization algorithm excludes the influence of noises as well as hybrid frequency and surmounts the difficulties of distinguishing fault characteristic parameters,it successfully identified modal natural frequency;QICA makes the weak fault information enhanced obviously,and with the combination of PNN to achieve the purpose of fault diagnosis,making more reliable to diagnose the fault.