基于属性粒化聚类与回声状态网络的末制导雷达故障诊断

doi:10.16183/j.cnki.jsjtu.2018.09.016

摘要/Abstract

摘要： 为了提高末制导雷达故障诊断的效率和精度，提出一种基于属性粒化聚类与回声状态网络(ESN)的故障诊断方法.定义一种基于属性值影响度的属性区分能力测量指标，并以此作为相似性度量依据，利用近邻传播(AP)聚类算法得到区分能力相当的若干属性粒，通过选取聚类中心属性来完成故障征兆属性的约简；在储备池构建中，采用Bienenstock-Cooper-Munro (BCM)规则进行连接权矩阵的预训练，在目标函数中添加L1/2范数惩罚项，以提高ESN储备池对样本的动态适应性；同时，采用半阈值迭代法对模型进行求解，通过末制导雷达信号处理模块的故障诊断实例，验证了所提方法的有效性和优越性.结果表明：与BP神经网络和传统ESN模型相比，所提方法的稳定性及诊断准确率更高；将其与基于属性重要度和AP聚类的属性粒化约简算法相结合，能够进一步提高故障诊断的效率和精度，其仿真实验的训练时间仅为 8.98s，诊断正确率可达 95.2%.

关键词: 末制导雷达, 属性粒化聚类, 回声状态网络, BCM规则, 故障诊断

Abstract: In order to improve the efficiency and precision of terminal guidance radar fault diagnosis, a fault diagnosis method based on attribute granulation clustering and echo state network (ESN) was proposed. Firstly, an attribute distinguishing ability index was defined by attribute value influence degree. As the basis of similarity measure, a number of attribute granules of similar distinguish are obtained through affinity propagation clustering algorithm, and then fault attribute reduction was completed by selecting clustering center attributes. In order to improve the dynamic adaptability of ESN reservoir to samples, Bienenstock-Cooper-Munro (BCM) rule was introduced into the reservoir construction to train the connection weight matrix. Meanwhile, the L1/2-norm penalty term was added to the objective function in order to improve the sparsification efficiency, solving a numerical oscillation problem by using a smoothing L1/2 regularizer, the model was solved by using the half threshold iteration method at last. The effectiveness and superiority of the proposed method are verified by a fault diagnosis example of terminal guidance radar signal processing module. The training time of the simulation experiment is only 8.98s, and the diagnostic accuracy can reach 95.2%.

Key words: guidance radar, attribute granulation clustering, echo state network (ESN), Bienenstock-Cooper-Munro (BCM) rule, fault diagnosis

中图分类号:

TP 957

逯程1，徐廷学1，王虹2. 基于属性粒化聚类与回声状态网络的末制导雷达故障诊断[J]. 上海交通大学学报（自然版）, 2018, 52(9): 1112-1119.

LU Cheng,XU Tingxue,WANG Hong. Fault Diagnosis of Terminal Guidance Radar Based on Attribute Granulation Clustering and Echo State Network[J]. Journal of Shanghai Jiaotong University, 2018, 52(9): 1112-1119.

参考文献

［1］潘红兵, 蔡云龙. 基于故障树及LabVIEW的雷达设备故障诊断［J］. 电子测量技术, 2013, 36(9): 115-118. PAN Hongbing, CAI Yunlong. Fault diagnosis of radar equipment based on fault tree and LabVIEW［J］. Electronic Measurement Technology, 2013, 36(9): 115-118. ［2］SHAN X, YANG H, ZHANG P. Fault diagnosis expert system of artillery radar based on neural network［C］∥2010 International Conference on Computer Design and Applications (ICCDA). Qinhuangdao, China: IEEE, 2010, 2: V2-426-V2-429. ［3］涂望明, 宋执环, 陈运涛, 等. 基于小波变换和LS-SVM的雷达故障诊断［J］. 控制工程, 2013, 20(2): 309-312. TU Wangming, SONG Zhihuan, CHEN Yuntao, et al. Radar fault diagnosis based on wavelet transform and LS-SVM［J］. Control Engineering, 2013, 20 (2): 309-312. ［4］KANG J, WU K, CHI K, et al. Multi-class intelligent fault diagnosis approach based on modified relevance vector machine［C］∥2016 International Conference on Intelligent Networking and Collaborative Systems (INCOS). Ostrava, Czech Republic: IEEE, 2016: 27-30. ［5］李伟, 梁玉英, 朱赛. 基于神经网络和证据理论的信息融合在故障诊断中的应用［J］. 计算机测量与控制, 2012, 20(11): 2888-2890. LI Wei, LIANG Yuying, ZHU Sai. Application of information fusion based on neural networks and evidence theory in fault diagnosis［J］. Computer Measurement and Control, 2012, 20 (11): 2888-2890. ［6］JAEGER H. The “echo state” approach to analysing and training recurrent neural networks［EB/OL］. ［2017-05-10］. https:∥citeseerx.ist.psu.edu/showci-ting?cid=418118. ［7］LUN S X, YAO X S, QI H Y, et al. A novel model of leaky integrator echo state network for time-series prediction［J］. Neurocomputing, 2015, 159: 58-66. ［8］郭嘉, 雷苗, 彭喜元.基于相应簇回声状态网络静态分类方法［J］. 电子学报, 2011, 39(3A): 14-18. GUO Jia, LEI Miao, PENG Xiyuan. Static classification method based on corresponding cluster echo state network［J］. Acta Electronica Sinica, 2011, (3A): 14-18. ［9］SCARDAPANE S, UNCINI A. Semi-supervised echo state networks for audio classification［J］. Cognitive Computation, 2017, 9(1): 125-135. ［10］MARTIN C E, REGGIA J A. Fusing swarm intelligence and self-assembly for optimizing echo state networks［J］. Computational Intelligence and Neuroscience, 2015(9): 1-15. doi: 10.1155/2015/642429. ［11］KUMP P, BAI E W, CHAN K, et al. Variable selection via RIVAL (removing irrelevant variables amidst Lasso iterations) and its application to nuclear material detection［J］. Automatica, 2012, 48(9): 2107-2115. ［12］XU H, ZHANG R, LIN C, et al. Novel approach of fault diagnosis in wireless sensor networks node based on rough set and neural network model［J］. International Journal of Future Generation Communication and Networking, 2016, 9(4): 1-16. ［13］YAO Y Y. Granular computing: Past, present and future［C］∥International Conference on Rough Sets and Knowledge Technology. Chengdu, China: IEEE, 2008. ［14］FREY B J, DUECK D. Clustering by passing messages between data points［J］. Science, 2007, 315: 972-976. ［15］CASTELLANI G C, INTRATOR N, SHOUVAL H, et al. Solutions of the BCM learning rule in a network of lateral interacting nonlinear neurons［J］. Network: Computation in Neural Systems, 1999, 10(2): 111-121. ［16］XU Z, ZHANG H, WANG Y, et al. L1/2 regularization［J］. Science China Information Sciences, 2010, 53(6): 1159-1169. ［17］DAUBECHIES I, DEVORE R, FORNASIER M, et al. Iteratively reweighted least squares minimization for sparse recovery［J］. Communications on Pure and Applied Mathematics, 2010, 63(1): 1-38.

[1]	许勇, 蔡云泽, 宋林. 基于数据驱动的核电设备状态评估研究综述[J]. 上海交通大学学报, 2022, 56(3): 267-278.
[2]	刘秀丽, 徐小力. 基于特征金字塔卷积循环神经网络的故障诊断方法[J]. 上海交通大学学报, 2022, 56(2): 182-190.
[3]	马航宇, 周笛, 卫宇杰, 吴伟, 潘尔顺. 变工况下基于自适应深度置信网络的轴承智能故障诊断[J]. 上海交通大学学报, 2022, 56(10): 1368-1377.
[4]	胡晓强，仲训昱，张霄力，彭侠夫，何荧. 基于支持向量机辅助的四轴陀螺两级故障诊断方法[J]. 上海交通大学学报, 2020, 54(11): 1151-1156.
[5]	贾雷，董炜，孙新亚，吉吟东，陈华. 基于节点电压增量方程的含容差轨道电路软故障诊断[J]. 上海交通大学学报（自然版）, 2017, 51(6): 679-685.
[6]	侯彦东，闫治宇，金勇. 小样本下基于特征子空间估计的故障诊断算法[J]. 上海交通大学学报（自然版）, 2015, 49(06): 825-829.
[7]	邱路1，叶银忠2. 基于多Agent系统的动态网络系统拓扑结构故障诊断方法[J]. 上海交通大学学报（自然版）, 2015, 49(06): 902-906.
[8]	王秀青1，侯增广2，曾慧3，吕锋1，潘世英1. 基于多传感器信息融合的机器人故障诊断[J]. 上海交通大学学报（自然版）, 2015, 49(06): 793-798.
[9]	杨旭1，彭开香1，罗浩2，KRUEGER Minjia2,宗大桥1，DING Steven X2. 基于EEMD和SVM的冷轧机垂直振动相关故障的诊断[J]. 上海交通大学学报（自然版）, 2015, 49(06): 751-756.
[10]	李元1，吴杰1，王国柱2. k近邻补值方法在工业过程故障诊断中的应用[J]. 上海交通大学学报（自然版）, 2015, 49(06): 830-836.
[11]	李雄杰1,2，周东华2. 混杂系统故障诊断的粒子滤波器方法[J]. 上海交通大学学报（自然版）, 2015, 49(06): 849-854.
[12]	徐兵1,2，朱亚橙2，苏俊2，范秋敏2. 基于特征Petri网建模的冷水机组故障诊断系统[J]. 上海交通大学学报（自然版）, 2015, 49(06): 923-928.
[13]	李艳峰，王新晴，张梅军，朱会杰. 基于奇异值分解和深度信度网络多分类器的滚动轴承故障诊断方法[J]. 上海交通大学学报（自然版）, 2015, 49(05): 681-686.
[14]	聂冰a，赵慧敏a，丁鸣艳b，李文a. 组态式牵引电动机故障诊断模型[J]. 上海交通大学学报（自然版）, 2015, 49(03): 402-405.
[15]	田福庆1，罗荣1，李万2，谢勇1. 改进的谐波小波包峭度图及其应用[J]. 上海交通大学学报（自然版）, 2014, 48(1): 39-44.