改进PCA及其在真空自耗炉故障诊断中的应用

Application of Improved PCA to Fault Diagnosis for Vacuum Consumable Electric-Arc Furnace

针对过程变量呈均值阶段性变化的一类生产过程,提出了一种新的主成分分析(PCA)故障诊断方法.该方法通过高通滤波对过程变量进行状态变换,扩展系统,然后采用主成分分析方法对扩展系统进行统计建模,并基于该模型进行过程监测和故障诊断.该方法可以克服普通主成分分析不能消除均值变化对所建模型的负面影响,进而提高故障诊断的鲁棒性和灵敏性.将提出的方法在真空自耗电弧炉中进行应用研究,冷却水泄漏故障诊断结果表明,提出的方法是有效的.

For some processes in which the mean values of process variables vary in different phases, a new fault diagnosis approach based on improved principal component analysis （P CA） was proposed. The state of process variables are transformed by a high-pass filter for system extension, then PCA is applied to the output of the extended system to develop a statistical model, by which the process monitoring and fault diagnosis are both available. This method can eliminate the negative effect of mean value change on the conventional PCA model and improve further the robustness and sensitivity of fault diagnosis. The method was applied to diagnosing the fault of cooling water leakage of the system of vacuum consumable electric-arc（VCEA） furnace, and the simulation results showed that the proposed method is effective.