[1]唐黎哲,李 珊,詹彦豪,等.基于小波分析和深度信念网络的牵引系统故障诊断[J].控制与信息技术(原大功率变流技术),2019,(05):77-81.[doi:10.13889/j.issn.2096-5427.2019.05.400]
 TANG Lizhe,LI Shan,ZHAN Yanhao,et al.Fault Diagnosis of the Traction System Based on Wavelet Analysis and Deep Belief Network[J].High Power Converter Technology,2019,(05):77-81.[doi:10.13889/j.issn.2096-5427.2019.05.400]
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基于小波分析和深度信念网络的牵引系统故障诊断()
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《控制与信息技术》(原《大功率变流技术》)[ISSN:2095-3631/CN:43-1486/U]

卷:
期数:
2019年05期
页码:
77-81
栏目:
故障诊断
出版日期:
2019-10-05

文章信息/Info

Title:
Fault Diagnosis of the Traction System Based on Wavelet Analysis and Deep Belief Network
文章编号:
2096-5427(2019)05-0077-05
作者:
唐黎哲李 珊詹彦豪江 平
(株洲中车时代电气股份有限公司,湖南株洲 412001)
Author(s):
TANG Lizhe LI Shan ZHAN Yanhao JIANG Ping
( Zhuzhou CRRC Times Electric Co., Ltd., Zhuzhou, Hunan 412001, China )
关键词:
故障诊断深度信念网络小波分析牵引系统
Keywords:
fault diagnosis deep belief network(DBN) wavelet analysis traction system
分类号:
U260.13
DOI:
10.13889/j.issn.2096-5427.2019.05.400
文献标志码:
A
摘要:
为实现牵引系统故障的准确定位,文章提出了一种基于小波分析和深度信念网络的故障诊断方法,其分别采集机车牵引系统正常与故障时的数据,利用小波分析计算小波能量分布序列和小波熵来组成特征向量并作为训练数据,建立用于机车牵引系统故障诊断的深度信念网络模型,以实现对牵引系统故障的有效定位。将该模型应用于离线机车故障记录数据分析中,结果表明,该方法能够实现高精度的牵引系统故障分类。
Abstract:
A novel fault diagnosis method based on wavelet analysis and deep belief network was proposed to locate faults of traction system accurately. Locomotive traction system data was collected and preprocessed, including those under normal and failure mode. Then, a wavelet analysis technology was applied to extract feature vectors of signals. The wavelet energy distribution and wavelet entropy of signals were calculated and composed as the feature vectors. Based on the vectors which were regarded as the training data, a deep belief network model for fault diagnosis of locomotive traction system was established. Finally, the recording off-line fault data of locomotive was used to test and verify the performance of our model. Results suggest that our method enables diagnosing the fault of locomotive traction system with high accuracy.

参考文献/References:

[1] MURPHEY Y L, MASRUR M A, CHEN Z, et al. Model-based fault diagnosis in electric drives using machine learning[J]. IEEE/ASME Transactions on Mechatronics, 2006, 11(3): 290-303.

[2] 刘松柏,蒋新华,陈特放. 电力机车主变流器故障智能诊断系统[J]. 机车电传动,2005(5): 57-59, 62.
[3] 张全明,邓亚波. 基于神经网络的机车牵引系统故障诊断研究[J]. 控制与信息技术,2018(3): 74-77.
[4] 田维,崔博文. 基于小波包和支持向量机的逆变器故障诊断[J]. 集美大学学报(自然版),2019,24(2): 125-132.
[5] 崔博文,任章. 基于傅里叶变换和神经网络的逆变器故障检测与诊断[J]. 电工技术学报,2006, 21(7): 37-43.
[6] 罗铁军,徐培刚,丁培义. HXD1C型电力机车逆变过流故障分析[J]. 机车电传动,2014(11): 104-110.
[7] 徐培刚, 彭军华,罗铁军. HXD1C型机车主电路接地故障分析[J]. 机车电传动,2013(3): 103-107.
[8] 朱琴跃,叶双挺,谭喜堂,等. 基于改进蚁群神经网络的牵引逆变器故障诊断[J]. 机电一体化,2014,20(11): 52-57.
[9] 王同辉, 张慧源, 许为,等. 基于EOVW指数和C&RT决策树的逆变过流故障诊断研究及应用[J]. 控制与信息技术, 2018(1):81-86.
[10] 高首聪,刘可安,李鹏. HXD1C型电力机车电传动系统设计及运用[J].机车电传动,2012( 2): 70-74.
[11] ROSSO O A, BLANCO S, YORDANOVA J, et al. Wavelet entropy: A new tool for analysis of short duration brain electrical signals[J]. Journal of Neuroscience Methods, 2001, 105 (1): 65–75.
[12] HUANG X X , WANG X F. The Classification of Synthetic Aperture Radar Oil Spill Images Based on the Texture Features and Deep Belief Network [J]. Lecture Notes in Electrical Engineering, 2014, 277: 661-669.

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备注/Memo

备注/Memo:
收稿日期:2019-07-17
作者简介:唐黎哲(1991—),男,硕士,主要从事故障诊断算法研究工作。
更新日期/Last Update: 2019-10-23