DBN参数对双转子不对中故障特征提取的影响及综合评估优选研究

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摘要针对DBN(deep belief network)结构参数对特征提取性能影响很大而其影响特性不明导致最优参数难以选取的问题，以双转子系统不对中故障振动特征提取为例，通过构建不同DBN迭代次数、隐含层数、学习率及动量的重构误差曲线，分析DBN结构参数对双转子不对中故障特征提取性能影响特性。综合重构误差均方根误差RMSE(root mean square error)及计算时间，选取优化的结构参数。分析结果表明，最优参数为：迭代次数200次、隐含层节点数1 000~1 500、学习率0.01~0.1，动量项0.1~0.4。为DBN在双转子系统不对中故障识别中的应用提供结构参数选取依据。

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	杨大炼1
	张帆宇1
	李仁杰1
	张宏献1
	2
	陶洁3

关键词 ：深度置信网络, 双转子, 不对中, 特征提取, 性能分析

Abstract：The structural parameters of deep belief network (DBN) have great influence on the feature extraction performance and the influence characteristic is not clear, so it is difficult to select optimal parameters.Aiming at the problem, the misalignment vibration feature extraction of dual-rotor system was taken as an example, the reconstruction error curves of different DBN iteration times, hidden layers, learning rate and momentum were constructed, and the performance of DBN structure parameters on the feature extraction of dual-rotor misalignment were analyzed.According to the root mean square error (RMSE) of reconstruction error and calculation time, the optimized structural parameters were selected.The results show that the optimal parameters are as follows: the number of iterations is set as 200, the number of hidden layer nodes is set as 1 000-1 500, the learning rate is set as 0.01-0.1, and the momentum is set as 0.1-0.4.This work provides a reference for the selection of structural parameters for the application of DBN in misalignment fault identification of a dual-rotor system.

Key words： deep belief network dual-rotor misalignment feature extraction performance analysis

收稿日期: 2020-09-21 出版日期: 2021-06-28

引用本文:

杨大炼1，张帆宇1，李仁杰1，张宏献1,2，陶洁3. DBN参数对双转子不对中故障特征提取的影响及综合评估优选研究[J]. 振动与冲击, 2021, 40(12): 151-158.
YANG Dalian1,ZHANG Fanyu1,LI Renjie1,ZHANG Hongxian1,2,TAO Jie3. A study on the influence of DBN’s parameters on dual-rotor misalignment fault feature extraction and its optimization based on comprehensive evaluation. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(12): 151-158.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2021/V40/I12/151

［1］刘长福，邓明.航空发动机结构分析［M］.西安: 西北工业大学出版社, 2006.

［2］韩清凯，王美令，赵广，等.转子系统不对中问题的研究进展［J］.动力学与控制学报, 2016, 14(1):1-13.
HAN Qingkai, WANG Meilin, ZHAO Guang, et al.A review of rotor systems with misalignment［J］.Journal of Dynamics and Control, 2016, 14(1):1-13.

［3］HINTON G E, SALAKHUTDINOV R R.Reducing the dimensionality of data with neural networks［J］.Science, 2006, 313(5786): 504-507.

［4］郑远攀, 李广阳, 李晔.深度学习在图像识别中的应用研究综述［J］.计算机工程与应用, 2019(12): 20-36.
ZHENG Yuanpan, LI Guangyang, LI Ye.Survey of application of deep learning in image recognition［J］.Computer Engineering and Applications, 2019(12): 20-36.

［5］DAHL G E, YU D, DENG L, et al.Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition［J］.IEEE Transactions on Audio, Speech, and Language Processing, 2012, 20(1):30-42.

［6］HUANG H B, HUANG X R, LI R X, et al.Sound quality prediction of vehicle interior noise using deep belief networks ［J］.Applied Acoustics, 2016, 113: 161-169.

［7］张建明, 詹智财, 成科扬, 等.深度学习的研究与发展［J］.江苏大学学报(自然科学版), 2015, 36(2): 191-200.
ZHANG Jianming, ZHAN Zhicai, CHENG Keyang, et al.Review on development of deep learning［J］.Journal of Jiangsu university (Natural Science Edition), 2015, 36(2): 191-200.

［8］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 Instruments and Instruments, 2016,37(9): 1946-1953.

［9］李巍华, 单外平, 曾雪琼.基于深度信念网络的轴承故障分类识别［J］.振动工程学报, 2016, 29(2): 340-347.
LI Weihua, SHAN Waiping, ZENG Xueqiong.Bearing fault identification based on deep belief network ［J］.Journal of Vibration Engineering, 2016, 29 (2): 340-347.

［10］杨宇, 罗鹏, 甘磊, 等.SADBN及其在滚动轴承故障分类识别中的应用［J］.振动与冲击, 2019, 38(15): 11-16.
YANG Yu, LUO Peng, GAN Lei, et al.SADBN and its application in rolling bearing fault identification and classification［J］.Journal of Vibration and Shock, 2019, 38(15): 11-16.

［11］SHAO H D, JIANG H K, WANG F, et al.Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet［J］.ISA Transactions, 2017, 69: 187-201.

［12］李本威, 林学森, 杨欣毅, 等.深度置信网络在发动机气路部件性能衰退故障诊断中的应用研究［J］.推进技术, 2016, 37(11): 2173-2180.
LI Benwei, LIN Xuesen, YANG Xinyi, et al.Research on application of deep belief networks on engine gas path component performance degradation defect diagnostics［J］.Journal of Propulsion Technology, 2016, 37(11): 2173-2180.

［13］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.

［14］郑小霞, 陈广宁, 任浩瀚, 等.基于改进VMD和深度置信网络的风机易损部件故障预警［J］.振动与冲击, 2019, 38(8): 153-160.
ZHEN Xiaoxia, CHEN Guangning, REN Haohan, et al.Fault detection of vulnerable units of wind turbine based on improved VMD and DBN［J］.Journal of Vibration and Shock, 2019, 38(8): 153-160.

［15］TAMILSCLVAN P, WANG P F.Failure diagnosis using deep belief learning based health state classification［J］.Reliability Engineering & System Safety, 2013, 115: 124-135.

［16］TRAN V T, ALTHOBIANI F, BALL A.An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks［J］.Expert Systems with Applications, 2014, 41(9): 4113-4122.

［17］YAN J H, HU Y Y, GUO C Z.Rotor unbalance fault diagnosis using DBN based on multi-source heterogeneous information fusion［J］.Procedia Manufacturing, 2019, 35: 1184-1189.

［18］SALAKHUTDINOV R R, MNIH A, HINTON G.Restricted boltzmann machines for collaborative filtering ［C］∥Proceedings of the 24th International Conference on Machine Learning.New York: ACM, 2007: 791-798.

［19］张春霞, 姬楠楠, 王冠伟.受限玻尔兹曼机［J］.工程数学学报, 2015, 32(2): 159-173.
ZHANG Chunxia, JI Nannan, WANG Guanwei.Restricted boltzmann machines［J］.Chinese Journal of Engineering Mathematics, 2015, 32(2): 159-173.