基于指定元分析的多级相对微小故障诊断方法

 设备运作过程中可能出现的微小故障,往往会因其呈现的异常征兆较小而被淹没在显著故障或噪声中,从而现有的方法难以很好地对其进行监控.本文在DCA空间投影框架下建立了观测空间的多级分解思想,并在此基础上提出一种多级相对微小故障诊断算法.将观测数据关于显著指定模式进行DCA分析,并移除显著变化模式的影响,以提高微小故障信号的信噪比.根据其向故障子空间投影能量的显著性判断残差数据中是否还包含仍未被诊断出、且具有一定影响的微小故障;根据各故障方向上投影能量的显著性进行微小故障诊断;重复以上过程,直到各级微小故障均被诊断出来.包含四种共存故障的观测数据的仿真研究,验证了该算法的有效性.

DCA Based Multi-level Small Fault Diagnosis Method

Small faults with insignificant abnormal symptoms were usually submerged in large faults or noise.Most fault diagnosis methods were invalid in the case when small faults occurred.We present a multi-level space decomposition mechanism and a small fault diagnosis algorithm.Implement designated component analysis (DCA) to the observation data for significant variation patterns;Remove the effect of significant designated patterns to get the residual which will increase the signal-to-noise rate of small fault signal;Determining whether small faults have occurred in the system using the projection significance index in the residual space;Repeat this process until all possible small faults are diagnosed.Simulation for observation data involved 4 faults shows its efficiency of this algorithm.