摘要
现代化的设备状态检测和故障诊断理论已经把设备的寿命预测作为一个重要的组成部分。随着科学技术的发展 ,运行设备状况的复杂化程度越来越明显 ,传统的数学建模预报方法已不能满足设备的复杂化和现代化要求。对于油田用大功率在用柴油机而言 ,这种信息的复杂性、不确定性程度反映更加明显 ,为了准确地对在用柴油机性能作出预测 ,现文将灰色理论和神经网络相结合 ,建立了灰色神经网络模型来预报柴油机的状态。将原始序列作累加生成处理后 ,采用 BP算法神经网络方法进行预报。实践证明 ,采用这种方法提高了预报精度 ,缩短了预测时间 ,取得了较好的预报效果。
The life prediction is looked on as an important part of the modern equipment condition monitoring and fault diagnosis. With the development of science and technology,and the more complicated operation condition, the traditional prediction means can not meet the needs. For diesel engine, those things mentioned are more evidently. In order to predict precisely, this paper uses the gray artificial neural network to build the model to predict the state of diesel engine. In practice, this model improves the precise of prediction and shortens the prediction time which performs a good effect.
出处
《石油矿场机械》
北大核心
2001年第5期1-4,共4页
Oil Field Equipment
基金
北京市自然科学基金项目 (3992 0 0 7)
