基于灰色理论的机械设备智能状态预测  

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] 

机构地区：[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

出　　处：《华东理工大学学报（自然科学版）》2001年第4期392-394,共3页Journal of East China University of Science and Technology

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.

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 

分 类 号：TH17[机械工程—机械制造及自动化] N941.5[自然科学总论—系统科学]