基于SDAE和GRUNN的行星齿轮故障识别

摘要
图/表
参考文献(22)
相关文章 (15)

全文: PDF (1621 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要对噪声环境和时变转速工况下行星齿轮故障识别率低的问题，提出一种基于堆叠消噪自动编码器(SDAE)和门控循环单元神经网络(GRUNN)的行星齿轮故障识别方法。构建基于SDAE和GRUNN的混合模型，处理前后关联的时序数据，自动地从含噪样本中提取鲁棒故障特征；将行星齿轮故障诊断的训练样本看作该混合模型的输入数据，采用Adam优化算法和dropout技术训练该混合模型，实现多参数的优化，防止过拟合现象的发生；根据训练后的混合模型，利用softmax分类器识别待诊样本中行星齿轮的状态。通过行星齿轮的故障识别实验验证该方法的有效性，实验结果表明该方法具有较强的抗噪能力和时变转速适应能力。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	于军1
	2
	3
	高莲莲4
	于广滨5
	刘可1
	3
	郭振宇2

关键词 ：行星齿轮, 故障识别, 噪声环境, 时变转速, 堆叠消噪自动编码器(SDAE), 门控循环单元神经网络(GRUNN)

Abstract：In order to address the problem of low fault identification accuracy of planetary gears under noisy environment and time-varying rotational speed conditions, a fault diagnosis method for planetary gears using the stacked denoising autoencoder (SDAE) and gated recurrent unit neural network (GRUNN) was proposed.A hybrid model based on the SDAE and GRUNN was constructed to process pre and post correlation time-series data, and automatically extract robust fault features.The training samples for planetary gear fault diagnosis were regarded as the input data of the hybrid model.The Adam optimization algorithm and the dropout technique were employed to train the hybrid model so as to realize the optimization of multiple parameters and prevent from overfitting.A softmax classifier was employed to identify the planetary gear states of test samples according to the hybrid model after training.The effectiveness of the proposed method was validated through a fault identification experiment of planetary gears.The experimental results demonstrate that the proposed method is of stronger anti-noise ability and excellent adaptability to time-varying rotational speed.

Key words： planetary gear fault identification noisy environment time-varying rotational speed stacked denoising autoencoder(SDAE) gated recurrent unit neural network(GRUNN)

收稿日期: 2019-04-14 出版日期: 2021-01-28

引用本文:

于军1,2,3，高莲莲4，于广滨5，刘可1,3，郭振宇2. 基于SDAE和GRUNN的行星齿轮故障识别[J]. 振动与冲击, 2021, 40(2): 156-163.
YU Jun1,2,3,GAO Lianlian4,YU Gangbin5,LIU Ke1,3,GUO Zhenyu2. Fault identification of planetary gears based on the SDAE and GRUNN. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(2): 156-163.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2021/V40/I2/156

[1] SALAMEH J P, CAUET S, ETIEN E, et al. Gearbox condition monitoring in wind turbines: A review [J]. Mechanical Systems and Signal Processing, 2018, 111: 251-264.
[2] ZHOU L, DUAN F, MBA D, et al. Using frequency domain analysis techniques for diagnosis of planetary bearing defect in a CH-46E helicopter aft gearbox [J]. Engineering Failure Analysis, 2018, 92: 71-83.
[3] WANG L, ZHANG Z, LONG H, et al. Wind turbine gearbox failure identification with deep neural networks [J]. IEEE Transactions on Industrial Informatics, 2017, 13(3): 1360-1368.
[4] 郑小霞，陈广宁，任浩翰, 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, 179.
ZHENG 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, 179.
[5] CHEN H, WANG J, TANG B, et al. An integrated approach to planetary gearbox fault diagnosis using deep belief networks [J]. Measurement Science and Technology, 2017, 28(2): 025010.
[6] WANG X, QIN Y, ZHANG A. An intelligent fault diagnosis approach for planetary gearboxes based on deep belief networks and uniformed features [J]. Journal of Intelligent and Fuzzy System, 2018, 34(6): 3619-3634.
[7] JIA F, LEI Y, LIN J. Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data [J]. Mechanical Systems and Signal Processing, 2016, 72-73: 303-315.
[8] WANG Z, WANG J, WANG Y. An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition [J]. Neurocomputing, 2018, 310: 213-222.
[9] JIA F, LEI Y, GUO L, et al. A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines [J]. Neurocomputing, 2018, 272: 619-628.
[10] 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, 2017, 111: 1-10.
[11] ZHAO M, KANG M, TANG B, et al. Deep Residual Networks With Dynamically Weighted Wavelet Coefficients for Fault Diagnosis of Planetary Gearboxes [J]. IEEE Transactions on Industrial Electronics, 2018, 65(5): 4290-4300.
[12] HAN Y, TANG B, DENG L. Multi-level wavelet packet fusion in dynamic ensemble convolutional neural network for fault diagnosis [J]. Measurement, 2018, 127: 246-255.
[13] ZHAO R, WANG D, YAN R, et al. Machine health monitoring using local feature-based gated recurrent unit networks [J]. IEEE Transactions on Industrial Electronics, 2018, 65(2): 1539-1548.
[14] LIU H, ZHOU J, ZHENG Y, et al. Fault diagnosis of rolling bearings with recurrent neural network based autoencoders [J]. ISA Transactions, 2018, 77: 167-178.
[15] JIANG H, LI X, SHAO H, et al. Intelligent fault diagnosis of rolling bearing using improved deep recurrent neural network [J]. Measurement Science and Technology, 2018, 29(6): 065107.
[16] VINCENT P, LAROCHELLE H, BENGIO Y, et al. Extracting and composing robust features with denoising autoencoders [C] // Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland. 2008.
[17] VINCENT P, LAROCHELLE H, LAJOIE I, et al. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion [J]. Journal of Machine Learning Research, 2010, 11: 3371-3408.
[18] KINGMA D P, BA J. Adam: a method for stochastic optimization [C] // Proceedings of the 3rd International Conference on Learning Representations, San Diego, USA. 2015.
[19] 李松柏，康子剑，陶洁. 基于信息融合及堆栈降噪自编码的齿轮故障诊断 [J]. 振动与冲击, 2019, 38(5): 216-221.
LI Songbai, KANG Zijian, TAO Jie. Gear fault diagnosis based on information fusion and stacked de-noising auto- encoder [J]. Journal of Vibration and Shock, 2019, 38(5): 216-221.
[20] RUMELHART D E, HINTON G E, WILLIAMS R J. Learning representations by back-propagating errors [J]. Nature, 1986, 323: 533-536.
[21] CHUNG J, GULCEHRE C, CHO K, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling [C] //Proceedings of NIPS Workshop on Deep Learning and Representation Learning, 2014.
[22] HINTON G E, SRIVASTAVA N, KRIZHEVSKY A, et al. Improving neural networks by preventing co-adaptation of feature detectors [J]. arXivpreprint arXiv: 1207.0580, 2012.