基于模糊粗糙集和鲸鱼优化支持向量机的化工过程故障诊断

Fault diagnosis of chemical processes based on the SVM optimized by fuzzy rough sets and a whale optimization algorithm

针对化工过程复杂,故障数据量大、属性多,难以保证故障诊断准确率和速度的问题,提出了一种基于模糊粗糙集(fuzzy rough sets,FRS)和鲸鱼优化的支持向量机(support vector machine,SVM)的化工过程故障诊断方法。通过对化工过程历史数据分析,判别故障类型。首先,利用模糊粗糙集对离散化后的过程数据进行特征选择,经过属性约简得出最小故障特征集合;然后,利用一种新型元启发式算法——鲸鱼优化算法(whale optimization algorithm,WOA),对支持向量机的参数进行优化,根据全局最佳适应度函数值,构建故障数据分类模型;最后,将属性约简后的数据集输入到鲸鱼优化的支持向量机故障分类模型中,实现化工过程的故障诊断。利用田纳西-伊斯曼(Tennessee Eastman,TE)过程对构建的FRS-WOA-SVM故障分类模型进行测试及比较。结果表明,该方法故障诊断准确率高、诊断速度快,可以有效地对化工过程中的故障做出诊断。

Chemical processes are complex and it is not easy to establish corresponding accurate mathematical models.In order to solve the problem that it is difficult to ensure the accuracy and speed of fault diagnosis due to the large amount of fault data and lots of attributes,a fault diagnosis method for chemical processes based on the support vector machine(SVM)optimized by fuzzy rough sets(FRS)and a whale optimization algorithm(WOA)was proposed.By analyzing the historical data of the chemical process,the fault types can be identified.Firstly,the fuzzy rough sets was used to select the features of the discretized process data,and the minimum fault feature set was obtained by attribute reduction.Then,the whale optimization algorithm,a new meta heuristic algorithm,was used to optimize the parameters of the SVM,and a fault data classifier was constructed according to the global optimal fitness function.Finally,the data set after attribute reduction was input into the SVM fault classifier optimized by the WOA,which formed a FRS-WOA-SVM fault classifier,so as to realize the fault diagnosis of chemical processes,and the results were compared with those of fault classifiers optimized by the conventional genetic algorithm and optimization algorithm.The Tennessee Eastman(TE)process particle swarm optimization was used as an example.The results show that the method proposed has high accuracy and fast diagnosis speed,and can effectively diagnose the faults in chemical processes.