摘要： 基于独立成分分析(ICA)的多变量统计过程监控主要用于故障检测,不能有效地进行故障分类。为此,结合极限学习机(ELM),提出一种ICA-ELM的故障分类方法。利用ICA提取故障特征,通过ELM学习算法训练神经网络,从而实现故障分类。采用TE过程数据进行验证,实验结果表 明,与概率神经网络和支持向量机相比,ICA-ELM算法的故障分类准确率更高,训练速度更快。

关键词: 独立成分分析, 极限学习机, 故障分类, 概率神经网络, 支持向量机, TE过程

Abstract: The process monitoring method with multivariate statistics based on Independent Component Analysis(ICA) is mainly used for fault detection,but it is not effective for fault classification.For this reason,combining with Extreme Learning Machine(ELM),a method called ICA-ELM for fault classification is proposed.ICA-ELM extracts the fault features with ICA,and then trains the networks with ELM,so as to realize fault classification.ICA-ELM is tested with Tennessee Eastman (TE) process data and compared with Probabilistic Neural Network(PNN) and Support Vector Machine(SVM).Experimental result shows that the accuracy of ICA-ELM is higher,training speed of ICA-ELM is faster.

Key words: Independent Component Analysis(ICA), Extreme Learning Machine(ELM), fault classification, Probabilistic Neural Network(PNN), Support Vector Machine(SVM), Tennessee Eastman(TE) process

中图分类号: 

• TP39