基于深度学习序贯检验的电源车故障诊断方法

doi:10.16182/j.issn1004731x.joss.18-0288

系统仿真学报 ›› 2020, Vol. 32 ›› Issue (4): 638-648.doi: 10.16182/j.issn1004731x.joss.18-0288

• 仿真支撑平台/系统技术 • 上一篇下一篇

基于深度学习序贯检验的电源车故障诊断方法

李炜^1,2,3, 周丙相^1,2,3, 蒋栋年^1,2,3

1. 兰州理工大学电气工程与信息工程学院,甘肃兰州 730050;
2. 甘肃省工业过程先进控制重点实验室,甘肃兰州 730050;
3. 兰州理工大学电气与控制工程国家级实验教学示范中心,甘肃兰州 730050

收稿日期:2018-05-15 修回日期:2018-09-05 出版日期:2020-04-18 发布日期:2020-04-16
作者简介:李炜(1963-),女,陕西西安,硕士,教授,博导,研究方向为动态系统故障诊断与容错控制;周丙相(1992-),男,贵州盘州,硕士生,研究方向为动态系统故障诊断与可靠性评估。
基金资助:
国家自然科学基金(61763027),甘肃省自然科学基金(1610RJTA022)

Fault Diagnosis Method of Vehicle Power Supply Based on Deep Learning and Sequential Test

Li Wei^1,2,3, Zhou Bingxiang^1,2,3, Jiang Dongnian^1,2,3

1. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China;
2. Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou 730050, China;
3. National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou 730050, China

Received:2018-05-15 Revised:2018-09-05 Online:2020-04-18 Published:2020-04-16

摘要/Abstract

摘要： 针对电源车健康维护存在的问题,提出了一种基于长短时间记忆LSTM (Long Short Time Memory)网络与序贯概率比检验SPRT (Sequential Probability Ratio Test)融合的电源车故障诊断方法。该方法基于LSTM网络建立电源车的多变量时间序列模型,并引入SPRT方法进行自适应多样本故障诊断。经在电源车仿真系统上进行对比实验,结果表明LSTM诊断模型有更强的学习和映射能力,LSTM-SPRT融合的故障诊断方法,显著提高了电源车故障诊断的准确率和可靠性。

关键词: 长短时间记忆网络, 序贯概率比检验, 电源车仿真系统, 故障诊断

Abstract: Focus on the health maintenance of vehicle power supply, a fault diagnosis method of vehicle power supply is proposed, which is based on the long and short time memory LSTM(Long Short Time Memory) network and the sequential probability ratio test SPRT(Sequential Probability Ratio Test). Based on the LSTM network, the multivariate time series model of vehicle power supply is established, and the SPRT method is used to perform the adaptive multi-sample fault diagnosis. The experiment on the vehicle power supply simulation system shows that the LSTM diagnosis model has stronger learning and mapping capabilities, and the fault diagnosis method based on the LSTM-SPRT fusion significantly improves the accuracy and reliability of the vehicle power supply fault diagnosis.

Key words: long short-time memory network, sequential probability ratio test, vehicle power supply simulation system, fault diagnosis

中图分类号:

李炜, 周丙相, 蒋栋年. 基于深度学习序贯检验的电源车故障诊断方法[J]. 系统仿真学报, 2020, 32(4): 638-648.

Li Wei, Zhou Bingxiang, Jiang Dongnian. Fault Diagnosis Method of Vehicle Power Supply Based on Deep Learning and Sequential Test[J]. Journal of System Simulation, 2020, 32(4): 638-648.

参考文献 24

[1]	任浩, 屈剑锋, 柴毅, 等. 深度学习在故障诊断领域中的研究现状与挑战[J]. 控制与决策, 2017, 32(8): 1345-1358.Ren Hao, Qu Jianfeng, Chai Yi, et al.Deep learning for fault diagnosis: The state of the art and challenge[J]. Control and Decision, 2017, 32(8): 1345-1358.
[2]	李晗, 萧德云. 基于数据驱动的故障诊断方法综述[J]. 控制与决策, 2011, 26(1): 1-9.Li Han, Xiao Deyun.Survey on data driven fault diagnosis methods[J]. Control and Decision, 2011, 26(1): 1-9.
[3]	Gao Z, Cecati C, Ding S X.A Survey of Fault Diagnosis and Fault-Tolerant Techniques-Part II: Fault Diagnosis With Knowledge-Based and Hybrid/Active Approaches[J]. IEEE Transactions on Industrial Electronics (S0278-0046), 2015, 62(6): 3768-3774.
[4]	雷亚国, 贾峰, 周昕, 等. 基于深度学习理论的机械装备大数据健康监测方法[J]. 机械工程学报, 2015, 51(21): 49-56.Lei Yaguo, Jia Feng, Zhou Xin, et al.A Deep Learning-based Method for Machinery Health Monitoring with Big Data[J]. Journal of Mechanical Engineering, 2015, 51(21): 49-56.
[5]	Xia F, Zhang H, Liu W, et al.Fault Analysis of Condenser Based on RBF Network and D-S Evidence Theory[C]. International Conference on Artificial Intelligence and Computational Intelligence. Berlin, Heidelberg: Springer-Verlag, 2012: 506-513.
[6]	Chen J, Li Y, Ye F.Uncertain information fusion for gearbox fault diagnosis based on BP neural network and DS evidence theory[C]. Intelligent Control and Automation.Guilin, China: IEEE, 2016: 1372-1376.
[7]	赵光权, 葛强强, 刘小勇, 等. 基于DBN的故障特征提取及诊断方法研究[J]. 仪器仪表学报, 2016, 37(9): 1946-1953.Zhao Guangquan, Ge Qiangqiang, Liu Xiaoyong, et al.Fault feature extraction and diagnosis method based on deep belief network[J]. Journal of Instrumentation and Measurement, 2016, 37(9): 1946-1953.
[8]	He K, Zhang X, Ren S, et al.Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence (S0162-8828), 2015, 37(9): 1904-1916.
[9]	Kim J, El-Khamy M, Lee J.Residual LSTM: Design of a Deep Recurrent Architecture for Distant Speech Recognition[C]. Interspeech(S2308-457X), 2017: 1591-1595. DOI:10.21437/Interspeech. 2017-477.
[10]	Hassan A, Mahmood A.Deep Learning approach for sentiment analysis of short texts[C]. International Conference on Control, Automation and Robotics. Nagoya, Japan: IEEE, 2017: 705-710.
[11]	段艳杰, 吕宜生, 张杰, 等. 深度学习在控制领域的研究现状与展望[J]. 自动化学报, 2016, 42(5): 643-654.Duan Yanjie, Lü Yisheng, Zhang Jie, et al.Deep Learning for Control: The State of the Art and Prospects. Acta Automatica Sinica, 2016, 42(5): 643-654.
[12]	Zhang Z, Zhao J.A deep belief network based fault diagnosis model for complex chemical processes[J]. Computers & Chemical Engineering (S0098-1354), 2017, 107: 395-407.
[13]	Shao H, Jiang H, Wang F, et al.Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet[J]. Isa Trans (S0019-0578), 2017, 69: 187-201.
[14]	Jing L, Zhao M, Li P, et al.A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox[J]. Measurement Journal of the International Measurement Confederation (S0263-2241), 2017, 111: 1-10.
[15]	Sun W, Shao S, Zhao R, et al.A sparse auto-encoder-based deep neural network approach for induction motor faults classification[J]. Measurement (S0263-2241), 2016, 89: 171-178.
[16]	Liu H, Zhou J, Zheng Y, et al.fault diagnosis of rolling bearing with recurrent neural network-based autoencoders, ISA Transactions (S0019-0578), 2018, 77: 167-178. https: //doi.org/10.1016/j.isatra.2018.04. 005.
[17]	De B T, Verbert K, Babuska R.Railway Track Circuit Fault Diagnosis Using Recurrent Neural Networks[J]. IEEE Transactions on Neural Networks & Learning Systems (S2162-237X), 2017, 28(3): 523-533.
[18]	Lee I, Kim D, Kang S, et al.Ensemble Deep Learning for Skeleton-Based Action Recognition Using Temporal Sliding LSTM Networks[C]. IEEE International Conference on Computer Vision. Venice, Italy: IEEE Computer Society, 2017: 1012-1020.
[19]	Zhao H, Sun S, Jin B.Sequential Fault Diagnosis based on LSTM Neural Network[J]. IEEE Access (S2169-3536), 2018, 6: 12929-12939.
[20]	De B T, Verbert K, Babuska R.Railway Track Circuit Fault Diagnosis Using Recurrent Neural Networks[J]. IEEE Transactions on Neural Networks & Learning Systems (S2162-237X), 2017, 28(3): 522-533.
[21]	Zhang J, Song Y, Li G, et al.A Method of Fault Diagnosis for Rolling Bearing of Wind Turbines Based on Long Short-term Memory Neural Network[J]. Computer Measurement & Control (S1671-4598), 2017, 25(1): 16-19.
[22]	Yuan M, Wu Y, Lin L.Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network[C]. IEEE International Conference on Aircraft Utility Systems. Beijing China: IEEE, 2016: 135-140.
[23]	Graves A.Supervised Sequence Labelling with Recurrent Neural Networks[M]. Berlin Heidelberg: Springer-Verlag, 2012.
[24]	李炜, 张婧瑜. 多模型传感器故障软闭环容错控制研究[J]. 计算机应用研究, 2015(2): 447-450.Li Wei, Zhang Jingyu.Study of multi-model soft close-loop fault-tolerant control with sensor faults[J]. Application Research of Computers, 2015(2): 447-450.

基于深度学习序贯检验的电源车故障诊断方法

Fault Diagnosis Method of Vehicle Power Supply Based on Deep Learning and Sequential Test

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