基于多块KPCA和SDG 的故障诊断方法

针对大规模复杂工业过程，提出一种基于多块核主元分析(MBKPCA)和符号有向图(SDG)的故障诊断方法。首先，提出基于SDG和优先级的分块策略，以强连接元SCC为最高优先级、多入/出度节点群为次高优先级、节点链为最低优先级对过程进行分块；在此基础上，采用MBKPCA进行过程监控，对于检测到的故障，先确定故障发生在哪一个数据块，再触发SDG在故障块内完成故障定位。所提出方法克服了多块KPCA故障隔离不完全和SDG推理过程中组合爆炸的缺点，可以提高复杂工业过程故障诊断的准确度和速度。基于Tennessee Eastman过程的仿真研究表明了所提出故障诊断方法的有效性。

Fault diagnosis method based on MBKPCA and SDG

Aiming at the large-scale complex industrial process, a fault diagnosis method based on multiblock kernel principal component analysis(MBKPCA) and signed directed graph(SDG) is proposed. Firstly, by proposing a partition strategy based on SDG and priority, the process is divided into multiple blocks according to the strong connected component as the highest priority, the multiple input or output degree node group as the second priority and the node chain as the lowest priority. On that basis, MBKPCA is used for the process monitoring. If the fault is detected, MBKPCA will determine which block the fault occurs in, then SDG is triggered to complete the fault location in the fault block. The proposed method can improve the accuracy and rapid of the fault diagnosis for the complex industrial process by overcoming the disadvantage of the incomplete fault isolation of MBKPCA and combination explosion in SDG reasoning process. The simulation research on Tennessee Eastman process is performed to show the effectiveness of the proposed method.