基于非线性故障重构的旋转机械故障预测方法

马洁; 李钢; 陈默

doi:10.3724/SP.J.1004.2014.02045

基于非线性故障重构的旋转机械故障预测方法

doi: 10.3724/SP.J.1004.2014.02045

马洁^1, ,,
李钢²,
陈默¹

1.
北京信息科技大学自动化学院北京 100192, 中国;
2.
美国南加州大学化学系洛杉矶 90089-0911, 美国

基金项目:

国家自然科学基金（61273173），北京市自然科学基金（4122029）资助

详细信息

作者简介:
马洁北京信息科技大学自动化学院教授.主要研究方向为数据驱动的过程监控，故障预测.本文通信作者.E-mail：mjbeijing@163.com

通讯作者:
马洁北京信息科技大学自动化学院教授.主要研究方向为数据驱动的过程监控，故障预测.本文通信作者.E-mail：mjbeijing@163.com

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- 被引次数: 0
出版历程
- 收稿日期: 2013-06-14
- 修回日期: 2014-02-26
- 刊出日期: 2014-09-20

Nonlinear Fault Reconstruction Based Fault Prognosis for Rotating Machinery

MA Jie^{1
, ,},
LI Gang²,
CHEN Mo¹

1.
Institute of Automation, Beijing Information Science and Technology University, Beijing 100192, China;
2.
Department of Chemical Engineering, University of California, Los Angeles 90089-0911, USA

Funds:

Supported by National Natural Science Foundation of China (61273173), and Natural Science Foundation of Beijing (4122029)

摘要

摘要: 对旋转机械的状态进行在线监测和故障预测是一个具有重要应用价值的工程问题. 采用基于核主元分析的非线性故障重构技术研究了多变量相关条件下旋转机械的故障估计及预测问题. 首先利用核主元分析对旋转机械系统进行离线非线性建模，并进行异常检测. 通过对故障程度进行定量描述，用最优化方法求解故障重构意义下的故障估计；然后用多层递阶的方法对估计出的故障幅值的发展趋势进行预测. 最后，以中国石化北京燕山分公司的烟气轮机作为实际应用对象，验证了该方法的有效性.
- 旋转机械 /
- 非线性故障重构 /
- 核主元分析 /
- 多层递阶预测
Abstract: It is an engineering problem of great application value to monitor the rotating machinery online and perform fault prognosis. A fault prognosis method is proposed for rotating machinery based on kernel principal component analysis and nonlinear fault reconstruction. First, kernel principal component analysis is used to build a model of rotating machinery and perform process monitoring. By describing the fault magnitude quantificationally, an optimal fault estimation is obtained with an optimization method. At last, a multi-layer hierarchical forecasting method is adopted to predict the fault magnitude evolution. The flue gas turbine of Beijing Yanshan Petrochemical Corporation is used to demonstrate the effectiveness of the proposed method.
- Rotating machinery /
- nonlinear fault reconstruction /
- kernel principal component analysis /
- multi-layer hierarchical forecast

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