|本期目录/Table of Contents|

[1]王冰,刁鸣,宋凯. 基于小波奇异熵和相关向量机的氢气传感器故障诊断[J].电机与控制学报,2015,19(01):96-101.[doi:10. 15938/j. emc.2015.01.014]
 WANG Bing DIAO Ming SONG Kai. Fault diagnosis of hydrogen sensor based on wavelet singularentropy and relevance vector machine[J].,2015,19(01):96-101.[doi:10. 15938/j. emc.2015.01.014]
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 基于小波奇异熵和相关向量机的氢气传感器故障诊断(PDF)
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《电机与控制学报》[ISSN:1007-449X/CN:23-1 408/TM]

卷:
19
期数:
2015年01
页码:
96-101
栏目:
出版日期:
2015-01-15

文章信息/Info

Title:
 Fault diagnosis of hydrogen sensor based on wavelet singular
entropy and relevance vector machine
作者:
 王冰 12刁鸣 1宋凯3
 1.哈尔滨工程大学信息与通信工程学院,黑龙江哈尔滨150001;2中国电子科技集团公司第49研究所,黑龙江哈尔滨150001;3哈尔滨工业大学电气工程及自动化学院,黑龙江哈尔滨150001
Author(s):
 WANG Bing 12DIAO Ming 1SONG Kai 3
 1. College of Information and Communication Engineering, Harbin Engineering University,Harbin 150001,China;
2. Chinese Electron Science and Technology Conglomerate 49th Research Institute, Harbin 150001 , China;
3. School of Electrical Engineering and Automation,Harbin Institute of Technology,Harbin 150001 ,China
关键词:
 小波奇异熵相关向量机氢气传感器小生境粒子群优化故障诊断
Keywords:
 Keywords:wavelet singular entropyrelevance vector machinehydrogen sensorniche particle swam optimizationfault diagnosis
分类号:
TP 206. 3
DOI:
10. 15938/j. emc.2015.01.014
文献标志码:
A
摘要:
 摘要:针对氢气传感器故障问题,提出了一种智能化的传感器故障诊断方法,可以对自身故障状
态进行诊断和识别。提出了一种基于小波奇异墒( wavelet singular entropy, WSE)和相关向量机
( relevance vector machine , RV M)原理的氢气传感器故障诊断方法,将小波变换和奇异熵两种分析
思想相结合,提取信号的完备故障特征;利用小生境粒子群优化算法(niche particle swarm optimiza-
tion , NPSO )对相关向量机的核参数进行优化,提高故障诊断的准确率。将提出的方法与其他成熟
算法进行了比较,实验结果表明所提方法故障诊断识别率达到98%以上,解决了非线性、小样本条
件下的传感器故障诊断问题,提高了传感器的可靠性。
Abstract:
 Abstract:Aiming at the fault problem of hydrogen sensor,an intelligent fault diagnosis method which can
diagnose and distinguish the fault state of the sensor was proposed. The fault diagnosis method based on
wavelet singular entropy and relevance vector machine was researched,the feature of fault signal was ex-
traded completely by combining the theory of the wavelet transform and singular entropy. The niche parti-
cle swarm optimization algorithm was used to optimize kernel parameter of RVM,and the accuracy of the
fault diagnosis was improved. The proposed method was compared with other mature algorithms. Results
indicates that the fault diagnosis recognizable rate reaches 98%.It resolves the problem of sensor fault di-
agnosis under the condition of nonlinear and small sample,and promote the reliability of sensor.

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备注/Memo

备注/Memo:
 基金项目:国家自然科学基金(61201306)
更新日期/Last Update: 2015-07-21