基于多任务深度学习的齿轮箱多故障诊断方法

摘要
图/表
参考文献(20)
相关文章 (15)

全文: PDF (2415 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要机械故障诊断领域已进入了“大数据”时代，且深度学习以其强大的自适应特征提取和分类能力也在机械大数据处理方面取得了丰硕的成果。然而这些研究均运用在单标签体系下，诊断单一目标故障。在大数据背景下，单标签体系不仅割裂了机械装备不同目标故障之间的联系，也难以完整描述装备故障位置、类型、程度等种类繁多的健康状态信息。提出了一种基于多任务深度学习模型的诊断方法，对齿轮箱的轴承及齿轮这两种目标的故障同时进行诊断。其优势在于通过单独的任务层，能够从同一信号中自适应的提取不同目标的特征，并进行诊断。实验结果表明，该方法实现了在多种工况，大量样本下对齿轮箱内轴承和齿轮不同故障的准确诊断。

	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
	赵晓平1
	吴家新1
	钱承山1
	张永宏2
	王丽华2

关键词 ：机械故障诊断, 多任务深度学习, 轴承, 齿轮

Abstract：The field of mechanical fault diagnosis enters a "big data" era, and the deep learning achieves fruitful results in mechanical big data processing with its powerful adaptive feature extraction and classification capabilities.However, this method is used in a single-label system to diagnose a single target fault.Under the background of big data, the single-label system not only cuts apart connections among different target faults of mechanical equipment, but also is difficult to fully describe lots of equipment fault state information, such as, fault location, type, and degree, etc.Here, a diagnosis method based on the multi-task deep learning model was proposed to simultaneously diagnose faults of bearing and gear in gearbox.It was shown that with this method, features of different targets can adaptively be extracted from the same signal, and then these features are used to perform fault diagnosis through a separate task layer.The test results showed that the proposed method realizes simultaneous correct diagnosis of bearing and gear different faults in gearbox under multiple working conditions and a large number of samples.

Key words： mechanical fault diagnosis multi-task deep learning bearing gear

收稿日期: 2018-05-29 出版日期: 2019-11-28

引用本文:

赵晓平1，吴家新1，钱承山1，张永宏2，王丽华2 . 基于多任务深度学习的齿轮箱多故障诊断方法[J]. 振动与冲击, 2019, 38(23): 271-278.
ZHAO Xiaoping1,WU Jiaxin1,QIAN Chengshan1,ZHANG Yonghong2, WANG Lihua2. Multi-fault diagnosis for gearboxes based on multi-task deep learning. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(23): 271-278.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2019/V38/I23/271

[1] 张震. 基于小波神经网络专家系统的齿轮箱故障诊断研究[D]. 燕山大学, 2010.
ZHANG Zhen. Fault diagnosis of gear-box based on wavelet neural network expert systems[D]. Yanshan University, 2010
[2] 刘胜, 江娜. 基于SVM的故障预报中的并行优化仿真研究[J]. 系统仿真学报, 2008, 20(13): 3445-3448.
LIU Sheng, JIANG Na. Research on parallel optimization simulation of fault prediction based on SVM[J]. Journal of System Simulation, 2008, 20(13): 3445-3448.
[3] SHAO Hai-dong, JIANG Hong-kai, ZHANG Xun, et al. Rolling bearing fault diagnosis using an optimization deep belief network[J]. Measurement Science & Technology, 2015, 26(11).
[4] WANG Xing-qing, LI Yan-feng, TING Rui, et al. Bearing fault diagnosis method based on Hilbert envelope spectrum and deep belief network[J]. Journal of Vibroengineering, 2015, 17(3): 1295-1308.
[5] CHEN Zhi-qiang, LI Chuan, Sánchez RV. Multi-layer neural network with deep belief network for gearbox fault diagnosis[J]. Journal of Vibroengineering, 2015, 17(5): 2379-2392.
[6] LI Chuan, Sánchez RV, Zurita G, et al. Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis[J]. Neurocomputing, 2015, 168(C): 119-127.
[7] SUN Wen-jun, SHAO Si-yu, ZHAO Rui, et al. A sparse auto-encoder-based deep neural network approach for induction motor faults classification [J]. Measurement, 2016, 89(ISFA):171-178.
[8] PANG Shan, YANG X,in-yi ZHANG Xiao-feng. Aero Engine Component Fault Diagnosis Using Multi-Hidden-Layer Extreme Learning Machine with Optimized Structure[J]. International Journal of Aerospace Engineering, 2016, 1329561.
[9] SHAHERYAR A, YIN Xu, HAO Hong-wei, et al. A Denoising based Auto-Associative model for robust sensor monitoring in Nuclear Power Plant[J]. Science and Technology of Nuclear Installations,2016, 9746948.
[10] YANG Zhi-xin, WANG Xiao-bo, ZHONG Jian-hua. Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach [J]. Energies, 2016, 9060379.
[11] 张绍辉, 罗洁思. 基于频谱包络曲线的稀疏自编码算法及在齿轮箱故障诊断的应用[J]. 振动与冲击, 2018, 37(4): 249-256.
ZHANG Shao-hui, LUO Jie-si. Sparse autoencoder algorithm based on spectral envelope curve and Its application in gearbox fault diagnosis[J]. Journal of vibration and shock, 2018, 37(4): 249-256.
[12] LIU Hong-mei, LI Lian-feng, MA Jian. Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals[J]. Shock and Vibration, 2016, 6127479.
[13] WANG Lu-kun, ZHAO Xiao-ying, PEI Jian-nan, et al. Transformer fault diagnosis using continuous sparse autoencoder[J]. Springer plus, 2016, 5(1): 448-461.
[14] LU Chen, WANG Zhen-ya, QIN Wei-li, et al. Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification[J]. Signal Processing, 2017, 130(C): 377-388.
[15] ZHANG Wei, PENG Gao-liang, LI Chuan-hao. Advances in Intelligent Information Hiding and Multimedia Signal Processing[M], Taiwan, Springer, 2016:77-84
[16] WANG Li-hua, ZHAO Xiao-ping, WU Jia-xin, et al. Motor Fault Diagnosis Based on Short-time Fourier Transform and Convolutional Neural Network[J]. Chinese Journal of Mechanical Engineering, 2017, 30(6): 1357-1368.
[17] Verstraete D, ferrada A S, Droguett E L, et al. Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings[J]. Shock & Vibration, 2017, 5067651.
[18] Lecun Y, Bengio Y. Convolutional Networks for Images, Speech, and Time-Series[C]// The Handbook of Brain Theory and Neural Networks. 1995.
[19] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]// International Conference on Neural Information Processing Systems. Curran Associates Inc. 2012:1097-1105.
[20] Salakhutdinov R, Hinton G E. Replicated Softmax: an Undirected Topic Model[C]// International Conference on Neural Information Processing Systems. Curran Associates Inc. 2010:1607-1614.