文献类型：期刊文献

中文题名：基于灰色理论的机械设备智能状态预测

英文题名：Intelligent Condition Prediction of Mechanical Equipment Based on the Gray-theory

作者：董振兴[1];史定国[2];张东山[2];杨汝清[1]

机构：[1]上海交通大学机器人研究所,上海200030;[2]华东理工大学化工机械研究所,上海200237

年份：2001

卷号：27

期号：4

起止页码：392

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：CSTPCD;;Scopus;北大核心:【北大核心2000】；CSCD:【CSCD2011_2012】；

语种：中文

中文关键词：灰色理论;状态预测;故障诊断;神经网络;机械设备;灰色预测模型

外文关键词：gray theory; condition prediction; fault diagnosis; neural networks

摘要：提出了基于灰色理论并与神经网络有机结合的机械设备智能状态预测方法。着眼于机械设备“内在”规律的研究 ,根据机械设备自身历史数据建立动态微分方程 ,并预测自身的发展 ,具有数据量小、计算简单、预测准确的特点。该方法已在实际工程中应用 。
Based on the gray theory, a novel prediction method of intelligent condition to detect the performance of mechanical equipment, was proposed in this paper, which combined the gray predictive model GM(1,1) and neural networks intimately and organically. With a view to investigate the inherent law of mechanical equipment and according to the own historical data of mechanical equipment, a dynamic different equation is established to predict its own trend. The characters of the gray predictive model GM(1,1) are simple calculation and accurate prediction with smaller amount of data. This method has been applied to a condition monitoring and fault diagnosis system. The industry application shows this method is useful and effective.