基于动态记忆反馈的改进ELM故障预测方法应用研究  

Improved dynamic recurrent-based ELM neural network for fault prediction

作　　者：徐圆[1] 叶亮亮[1] 朱群雄[1] 

机构地区：[1]北京化工大学信息科学与技术学院,北京100029

出　　处：《控制与决策》2015年第4期623-629,共7页Control and Decision

基　　金：国家自然科学基金项目(61104131);北京市自然科学基金项目(4142039);北京市青年英才计划项目(BJYC21);中央高校基本科研业务费项目(ZZ1312)

摘　　要：随着工业系统复杂性的逐步增加,对故障预测的实时性和准确性提出了更高的要求.对此,提出一种基于动态记忆反馈的改进ELM神经网络模型进行故障预测.此模型在结构上增加了反馈层用于记忆隐含层输出,并从反馈层记忆的信息中提取数据变化趋势特征,从而动态更新反馈层的输出权值.通过对非线性动态系统的下一时刻输出进行预测,并对预测输出进行诊断,达到故障预测的目的.通过人工数据Sinc验证和TE过程实例应用表明了所提出方法具有预测精度高、动态适应能力强等优点,对非线性时序系统具有较好的预测能力.With the increase of the complexity of industrial system, the demand of real time of accuracy of fault prediction is higher. Therefore, an improved dynamic recurrent-based extreme learning machine （DR-ELM） neural network is proposed for fault prediction. For the network structure, a feedback layer is added to memorize the hidden output. And the trend characters of data variation is extracted from the feedback information, so as to update the output weight of feedback layer dynamically. Through the prediction of the next-time output for the nonlinear dynamic system, the diagnosis is made for the prediction output, then the fault prediction is realized. A numerical study（Sinc test） and the complicated Tennessee Eastman（TE） benchmark process show the superiority of the proposed approach not only in prediction accuracy but also in dynamic adaptability, and the better prediction ability for non-linear sequential systems.

关 键 词：极限学习机 动态递归 故障预测 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]