带权重变化和决策融合的ELM在线故障检测

A new online extreme learning machine with varying weights and decision level fusion for fault detection

在线极限学习机对样本数据分批或分块地学习,适用于分析生产过程的在线数据,进而检测生产过程的故障.为了提高检测的准确性和快速性,提出一种权重变化和决策融合的极限学习机(ELM)在线故障检测方法.该方法在学习过程中增加被当前数据监控模型错误预测的新样本权重,同时在数据监控模型中引入决策级融合的方法,提高模型的综合决策能力.利用UCI数据集和TE过程进行仿真实验对比,对比结果表明所提出的方法在训练时间和检测准确率上都具有很好的性能.

Online sequential extreme learning machine learns samples one by one or chunk by chunk, so it is appropriate to analyze online process data and to detect system faults. To improve the detection accuracy and rapidity, a new online extreme learning machine algorithm with varying weights and decision level fusion has been proposed, which increases the weights of the new samples predicted wrongly by current data monitoring model in learning, and introduced decision level fusion to improve integrated decision-making ability of the model. Performance comparisons of the method are presented using UCI datasets and Tennessee Eastman process. The results show that the proposed algorithm produces comparable or better performance with higher accuracies and lower training time.