基于PCA和IGWO-SVM的水泥回转窑故障诊断研究

Research of fault diagnosis of cement rotary kiln based on PCA and IGWO-SVM

为实现水泥回转窑故障的精确诊断,提出一种基于主成分分析(PCA)和支持向量机(SVM)的回转窑故障诊断模型。通过引入差分进化(DE)算法的变异、交叉、选择操作来维持种群的多样性,克服灰狼算法易早熟收敛的缺陷,然后采用这种改进的灰狼算法(IGWO)对SVM的惩罚因子c和核函数参数g进行动态的寻优。运用PCA对采集数据进行降维处理,消除非相关因素,降低数据处理难度,然后将特征提取后的数据作为输入建立故障诊断模型,并与普通的SVM建模方法进行比较。实例表明:在有用信息量损失较小的前提下,分类准确率达到96.153 8%,模型构建时间为2.972 0 s,从而验证模型的准确性和高效性。

In order to precisely diagnose the based on principal component analysis（PCA） faults of cement rotary kiln, a fault diagnosis model and support vector machine（SVM） is proposed. The variation, crossover and selection operation of differential evolution（DE） are used to maintain the diversity of the population and are introduced into GWO to avoid premature convergence. Improved gray wolf optimizer（IGWO） is used to dynamically optimize the penalty factor （c） and the kernel function parameter （g） of SVM model. PCA is used to reduce the dimension of the collected data, eliminate the irrelevant factors and reduce the difficulty of data processing. Then the data after feature extraction are used as the inputs to establish the fault diagnosis model and the built IGWO-SVM model is compared with the general SVM model. The experiment shows that under the condition of less useful information loss, the classification precision is 96.153 8% and the model building time reaches 2.9720 s, which verifies the accuracy and high efficiency of the IGWO-SVM model.