%0 Journal Article
%T 一种基于INW-ESN的故障融合预测方法<br>A Prognostic Fusion Algorithm Based on the INW-ESN
%A 王浩天
%A 单甘霖
%A 孙健
%A 杨文
%A 杜亚卿
%J 振动.测试与诊断
%D 2018
%R 10.16450/j.cnki.issn.1004-6801.2018.25
%X 针对传统方法对液压泵故障预测效果不佳的问题，提出了一种基于改进的Newman-Watts小世界 回声状态网络（improved Newman-Watts-echo state network, 简称INW-ESN）的故障融合预测方法。首先，对回声状态网络(echo state network，简称ESN)储备池结构进行优化，建立INW ESN基础预测模型，并重新定义邻接矩阵元素取值，以改善网络预测性能；其次，在此基础上建立故障融合预测模型，利用Dezert-Smarandache理论（Dezert-Smarandache theory，简称DSmT）将INW-ESN预测信息与液压泵性能退化模型信息进行融合，以提高预测精度；最后，通过对液压泵性能退化试验的应用分析，验证了该方法的有效性。<br>To solve the unsatisfying fault predicting effect of hydraulic pumps based on traditional algorithms, a novel fault fusion predicting method based upon the improved Newman-Watts(INW) is proposed. First, the structure of the reserve pool in traditional echo state network (ESN) is modified, and the INW-ESN fundamental predicting model is established. Neighboring matrix elements are redefined to improve the network performance. Furthermore, the fault fusion prognostic model is proposed. By the employment of Dezert-Smarandache theory (DSmT), the INW-ESN prognostic information and degradation model information are fused to increase predicting accuracy. Finally, the proposed method is verified by the application on the hydraulic pump performance degradation experiment.
%K 故障预测
%K 回声状态网络
%K Dezert-Smarandache 理论
%K 液压泵&lt
%K br&gt
%K prognostic
%K echo state network (ESN)
%K Dezert-Smarandache theory (DSmT)
%K hydraulic pump
%U http://zdcs.nuaa.edu.cn/ch/reader/view_abstract.aspx?file_no=201801025&flag=1