North China Electric Power University
The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)
在工业过程闭环控制系统中，由于控制器的调节作用，使得执行器的故障特征在一定程度上受到掩盖和干扰，而单一的诊断方法总是存在误判现象。针对上述问题，提出了一种基于证据融合的诊断算法。该算法首先利用基于信号分析的方法计算表征故障特征的指标，并针对“一票否决”现象对指标结果加以改进。然后再采用 DS 证据理论融合基于最小二乘支持向量机 (LS-SVM) 的概率分类特征，实现优势互补，将指标所表达的故障机理信息与概率分类所挖掘的数据特征信息结合起来，规避了单一方法的局限性，从而提高诊断的准确率。最后，基于双容水箱系统的实验表明：该方法能有效学习闭环系统中执行器的故障数据特征，提升诊断能力，克服单一方法的误判问题，具有较高的应用价值。
In industrial process closed-loop control system, due to the adjustment function of the controller, the fault characteristics of the actuator are covered and interfered to a certain extent, and the single diagnosis method always has the phenomenon of misjudgment. To solve the above problems, this paper proposes a diagnosis algorithm based on evidence fusion. First, it uses a method based on signal analysis to calculate indicators that represent fault characteristics. And they are improved in response to the “one-vote veto”phenomenon. Then, DS evidence theory is used to fuse the probabilistic classification features based on LS-SVM. It achieves complementary advantages. The failure mechanism information expressed by indicators is combined with the data feature information mined by probability classification, which circumvents the limitations of a single method and improves the accuracy of diagnosis. Experiments based on the dual-capacity water tank system show that this method can effectively learn the fault data characteristics of the actuator in the closed-loop system to improve diagnostic ability, and overcome the misjudgment problem of a single method, which has high application value.