一种基于INW-ESN的故障融合预测方法  

A Prognostic Fusion Algorithm Based on the INW-ESN

作　　者：王浩天 单甘霖 孙健 杨文 杜亚卿 

机构地区：[1]陆军工程大学石家庄校区电子与光学工程系,石家庄050003 [2]中国洛阳电子装备试验中心,洛阳471003

出　　处：《振动．测试与诊断》2018年第1期162-169,共8页Journal of Vibration,Measurement & Diagnosis

基　　金：国家自然科学基金资助项目(51305454)

摘　　要：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。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.

关 键 词：故障预测 回声状态网络 DEZERT-SMARANDACHE理论 液压泵 

分 类 号：TH322[机械工程—机械制造及自动化] TP206.3[自动化与计算机技术—检测技术与自动化装置]