Tennessee-Eastman过程的学习型案例推理故障诊断方法
Fault diagnosis method using learning case-based reasoning for Tennessee-Eastman process

为了提高Tennessee-Eastman (TE) 过程的故障诊断准确率, 本文研究一种学习型伪度量(learning pseudo metric, LPM)代替距离度量的案例检索方法, 并建立了TE过程的案例推理(case-based reasoning, CBR)故障诊断模 型. 首先建立LPM度量准则并对LPM模型进行训练, 其次度量目标案例与每一个源案例的相似度, 从中检索与目标 案例相似的同类案例, 再采用多数重用原则从同类案例中决策出目标案例的解, 最后通过TE过程的运行数据对该 方法的性能进行测试, 并与典型的CBR和BP(back-propagation)神经网络和支持向量机等方法进行对比, 表明本文方 法能有效提高故障诊断准确率, 在实际化工过程中具有一定的推广应用价值.
To diagnose the fault in the Tennessee-Eastman (TE) process more accurately, a learning pseudo metric (LPM)-based case retrival method is proposed to replace distance measure retrieval method and a case-based reasoning (CBR) fault diagnosis model of TE process is established. Firstly, the LPM metrics are established to train the LPM model. Then, the similarity between the target case and each source case is measured to find the same type of cases as the target case. Next, the solution of the target case is obtained based on the majority of reuse principle. Finally, the running data of TE process are used to carry out a performance test and a comparison experiment. The results show that the proposed LPM-based CBR method is superior to traditional CBR, back-propagation (BP) neural network and support vector machine method and significantly improves the accuracy of the fault diagnosis. It has a promotional value for fault diagnosis in the actual chemical process.