Please wait a minute...
浙江大学学报(工学版)
J4  2010, Vol. 44 Issue (7): 1298-1302    DOI: 10.3785/j.issn.1008-973X.2010.07.012
自动化技术     
基于计算智能技术融合的故障识别方法
邓武1,2,3, 陈荣1,宋英杰1
1.大连海事大学 信息科学技术学院,辽宁 大连 116026；2.大连交通大学 软件学院,辽宁 大连 116028；
3. 湘潭大学 智能制造湖南省高等学校重点实验室,湖南 湘潭 411105
Fault detection method based on computational intelligence
technology fusion
DENG Wu1,2,3, CHEN Rong1, SONG Yingjie 1
1.Department of Informational Science and Technology, Dalian Maritime University, Dalian 116026, China;
2. Department of Software, Dalian Jiaotong University, Dalian 116028, China; 3. Key Laboratory of Intelligent
Manufacture of Hunan Province, Xiangtan University, Xiangtan 411105, China
 全文: PDF  HTML
摘要：

为了提高复杂系统故障识别的精度和降低误报率,利用粗糙集理论、遗传算法、神经网络等计算智能方法的优势,提出一种基于计算智能技术融合的故障识别方法.针对原始样本数据的不确定性和不完备性,利用粗糙集对原始样本数据进行数据归一化、离散化、属性约简等预处理,求得能够覆盖原始数据特征的具有最大完备度的最小规则集.利用具有全局搜索能力的遗传算法直接训练反向传播神经网络的权值,将规则集作为网络输入,形成优化网络模型.采用该模型对预处理的各种状态故障特征向量进行分类决策,实现故障识别.通过电机轴承故障识别实验表明,该方法能够优化网络结构,提高故障识别速度和准确率.
Abstract:

A computational intelligence technology fusion method of fault detection was proposed in order to improve the detection precision and decrease the misinformation detection of complex system. The various approaches such as rough set, genetic algorithm and neural network were integrated to synthesize their merits for fault detection. According to the uncertainty and imperfection of the original sample data, the rough set was used to pretreat for the normalization of data, the discretization of continuous data and the attribute reduction in order to obtain the minimum fault feature subset. The genetic algorithm with the ability of strong global search was used to train the weights of back propagation neural network. The minimum reduced subset was inputted into the trained network to construct the fault detection model that can classify the pretreated fault feature vectors under certain states to realize the fault detection. The motor bearing experiment results show that the method can optimize the structure of neural network and improve the rate and precision of fault detection.
出版日期: 2010-07-01
:  TP 391  
基金资助:

国家自然科学基金资助项目（60870009,60775028）；智能制造湖南省高校重点实验室开放课题资助项目（2009IM03）.
通讯作者: 陈荣,男,教授,博导.     E-mail: rchen@dl.cn
作者简介: 邓武 (1976—),男,四川安岳人,博士生,从事计算智能研究.Email: dw7689@163.com
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章　　

引用本文:

邓武, 陈荣, 宋英杰. 基于计算智能技术融合的故障识别方法[J]. J4, 2010, 44(7): 1298-1302.

DENG Wu, CHEN Rong, SONG Yang-Jie. Fault detection method based on computational intelligence
technology fusion. J4, 2010, 44(7): 1298-1302.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2010.07.012    或    http://www.zjujournals.com/eng/CN/Y2010/V44/I7/1298

［1］ 张萍,王桂增,周东华.动态系统的故障诊断方法［J］.控制理论与应用,2000,17(2): 153159.
ZHANG Ping, WANG Guizeng, ZHOU Donghua.Fault diagnosis methods for dynamic systems ［J］. Control Theory and Applications, 2000, 17(2): 153159.
［2］ 林勇,周晓军,杨先勇,等.基于双谱识别和人工免疫网络的智能故障检测［J］.浙江大学学报：工学版,2009,43(10): 17771782.
LIN Yong, ZHOU Xiaojun, YANG Xianyong, et al. Intelligent fault diagnosis methods based on bispectrum recognition and artificial immune network ［J］. Journal of Zhejiang University: Engineering Science, 2009, 43(10): 17771782.
［3］ 姜苍华,周东华.基于计算智能方法的动态系统故障诊断技术［J］.控制工程,2003,10(5): 385390.
JIANG Canghua, ZHOU Donghua. Fault diagnosis techniques based on computational intelligence for dynamic systems ［J］.Control Engineering of China, 2003, 10(5): 385390.
［4］ 代建华,陈卫东,潘云鹤.基于粗糙集的综合推理模型［J］. 浙江大学学报：工学版,2006,40(9): 15261530.
DAI Jianhua, CHEN Weidong, PAN Yunhe. Synthesis reasoning model based on rough set theory ［J］. Journal of Zhejiang University: Engineering Science, 2006, 40(9): 15261530.
［5］ WU J D, WANG Y H, CHIANG P H, et al. A study of fault diagnosis in a scooter using adaptive order tracking technique and neural network ［J］. Expert Systems with Applications, 2009, 36(1): 4956.
［6］ WU J D, LIU C H. Investigation of engine fault diagnosis using discrete wavelet transform and neural network ［J］. Expert Systems with Applications, 2008, 35(3): 12001213.
［7］ PIRMORADI F N, SASSANI F, SILVA C D. Fault detection and diagnosis in a spacecraft attitude determination system ［J］. Acta Astronautica, 2009, 65(5): 710729.
［8］ PAWLAK Z. Rough sets ［J］. International Journal of Computer and Information Science, 1982, 11(5): 341356.
［9］ 张文修,吴伟业,梁吉业,等.粗糙集理论与方法［M］.北京：科学出版社,2001: 940.
［10］ 谭天乐,宋执环,李平.基于粗糙集的故障诊断方法［J］.浙江大学学报：工学版,2003,37(1): 4751.
TAN Tianle, SONG Zhihuan, LI Ping. Approach for fault detection and diagnosis based on rough set ［J］.Journal of Zhejiang University: Engineering Science, 2003, 37(1): 4751.
［11］ GENG Z, ZHU Q. Rough setbased heuristic hybrid recognizer and its application in fault diagnosis ［J］. Expert Systems with Applications, 2009, 36(2): 27112718.
［12］ TAN Y H, HE Y G, CUI C, et al. A novel method for analog fault diagnosis based on neural networks and genetic algorithm ［J］. IEEE Transactions on Neural Networks, 2008, 57(11): 26312639.
［13］ 廖瑛,张绍勇,尹大伟,等.基于粗糙集遗传神经网络的柴油机故障诊断［J］.控制工程,2009,16(6): 709713.
LIAO Ying, ZHANG Shaoyong, YIN Dawei, et al. Faults diagnosis of diesels based on rough set genetic neural networks ［J］. Control Engineering of China, 2009, 16(6): 709713.
［14］ 姚鑫骅,徐月同,傅建中,等.基于粗糙集理论的数控机床智能故障诊断研究［J］.浙江大学学报：工学版,2008,42(10): 17191785.
YAO Xinhua, XU Yuetong, FU Jianzhong, et al. Intelligent fault diagnosis of CNC machine tools based on rough set theory ［J］. Journal of Zhejiang University: Engineering Science, 2008, 42(10): 17191785.
［15］ RYSZARD N. Evaluation of vibroacoustic diagnostic symptoms by means of the rough sets theory ［J］. Computers in Industry, 1992, 20 (2): 141152.
［16］ 金微,陈慧萍.基于分层聚类的kmeans算法［J］.河海大学常州分校学报,2007,21(1): 710.
JIN Wei, CHEN Huiping. A hybrid hierarchical kmeans clustering algorithm ［J］. Journal of Hohai University Changzhou,2007,21(1): 710.
［17］ MASAHIRO I. Attribute reduction in variable precision rough set model ［J］.International Journal of Uncertainty, Fuzziness and Knowledgebased Systems, 2006, 14(4): 461479.
［18］ 王翔飞,须文波.属性约简的一种新计算方法［J］.微电子学与计算机,2007,24(4): 99101.
WANG Xiangfei, XU Wenbo. A new method for attributes reduction ［J］. Microelectronics and Computer, 2007, 24(4): 99101.
[1] 赵建军,王毅,杨利斌. 基于时间序列预测的威胁估计方法[J]. J4, 2014, 48(3): 398-403.
[2] 张天煜, 冯华君, 徐之海, 李奇, 陈跃庭. 基于强边缘宽度直方图的图像清晰度指标[J]. J4, 2014, 48(2): 312-320.
[3] 刘中, 陈伟海, 吴星明, 邹宇华, 王建华. 基于双目视觉的显著性区域检测[J]. J4, 2014, 48(2): 354-359.
[4] 崔光茫, 赵巨峰, 冯华君, 徐之海, 李奇, 陈跃庭. 非均匀介质退化图像快速仿真模型的建立[J]. J4, 2014, 48(2): 303-311.
[5] 王相兵,童水光,钟崴,张健. 基于可拓重用的液压挖掘机结构性能方案设计[J]. J4, 2013, 47(11): 1992-2002.
[6] 王进, 陆国栋, 张云龙. 基于数量化一类分析的IGA算法及应用[J]. J4, 2013, 47(10): 1697-1704.
[7] 刘羽, 王国瑾. 以已知曲线为渐进线的可展曲面束的设计[J]. J4, 2013, 47(7): 1246-1252.
[8] 胡根生,鲍文霞,梁栋,张为. 基于SVR和贝叶斯方法的全色与多光谱图像融合[J]. J4, 2013, 47(7): 1258-1266.
[9] 吴金亮, 黄海斌, 刘利刚. 保持纹理细节的无缝图像合成[J]. J4, 2013, 47(6): 951-956.
[10] 陈潇红,王维东. 基于时空联合滤波的高清视频降噪算法[J]. J4, 2013, 47(5): 853-859.
[11] 朱凡,李悦,蒋 凯,叶树明,郑筱祥. 基于偏最小二乘的大鼠初级运动皮层解码[J]. J4, 2013, 47(5): 901-905.
[12] 吴宁, 陈秋晓, 周玲, 万丽. 遥感影像矢量化图形的多层次优化方法[J]. J4, 2013, 47(4): 581-587.
[13] 计瑜,沈继忠,施锦河. 一种基于盲源分离的眼电伪迹自动去除方法[J]. J4, 2013, 47(3): 415-421.
[14] 王翔,丁勇. 基于Gabor滤波器的全参考图像质量评价方法[J]. J4, 2013, 47(3): 422-430.
[15] 童水光, 王相兵, 钟崴, 张健. 基于BP-HGA的起重机刚性支腿动态优化设计[J]. J4, 2013, 47(1): 122-130.