多Agent深度Q学习和模糊积分的行星齿轮箱故障诊断

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Abstract Aiming at the problem of low recognition rate of single-agent in deep Q-learning for fault diagnosis of planetary gearbox, using multi-agent for strategy learning and fuzzy integral for fusing decision results of multiple-agent, a planetary gearbox fault diagnosis method based on multi-agent deep Q-learning and fuzzy integral was proposed.Firstly, the continuous wavelet transform (CWT) and S transform (ST) were performed for vibration signals to obtain the corresponding time-frequency feature matrix, and then using the original time domain data and the obtained time-frequency feature matrix, a multi-domain environmental state space was constructed to interact with multi-agent.Secondly, the original time-domain features, CWT time-frequency features and ST features returned by the environment space were taken as the corresponding agent state in interaction process.The Q function value of each agent was maximized with the deep Q-learning algorithm to obtain the optimal strategy.Finally, fuzzy integral was used to fuse the decision results of multi-agent, and obtain the final diagnosis results.The fault data of planetary gearbox were used to do verification and analysis.It was shown that the proposed planetary gearbox fault diagnosis method based on multi-agent deep Q-learning and fuzzy integral can effectively improve the diagnosis accuracy; its diagnosis results are better than those of the method based on single-agent deep Q-learning.

Key words： fault diagnosis deep Q-learning multi-agent fuzzy integral planetary gearbox

Received: 12 February 2020 Published: 15 June 2021

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	Articles by authors
	CHEN Renxiang1
	ZHOU Jun1
	HU Xiaolin2
	ZENG Li1
	CHEN Cai3
	HU Chaochao1

Cite this article:

CHEN Renxiang1,ZHOU Jun1,HU Xiaolin2, et al. Fault diagnosis of planetary gearbox based on multi-Agent deep Q-learning and fuzzy integral[J]. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(11): 147-153.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2021/V40/I11/147

［1］LEI Y, LIN J, ZUO M J, et al.Condition monitoring and fault diagnosis of planetary gearboxes: a review［J］.Measurement, 2014, 48: 292-305.
［2］胡茑庆, 陈徽鹏, 程哲, 等.基于经验模态分解和深度卷积神经网络的行星齿轮箱故障诊断方法［J］.机械工程学报, 2019, 55(7):9-18.
HU Niaoqing, CHEN Huipeng, CHENG Zhe,et al.Fault diagnosis for planetary gearbox based on EMD and deep convolutional neural networks［J］.Journal of Mechanical Engineering,2019, 55(7):9-18.
［3］CHEN R X, HUANG X, YANG L X, et al.Intelligent fault diagnosis method of planetary gearboxes based on convolution neural network and discrete wavelet transform［J］.Computers in Industry, 2019, 106: 48-59.
［4］ZENG X Q, LIAO Y X, LI W X.Gearbox fault classification using S-transform and convolutional neural network［C］//2016 10th International Conference on Sensing Technology.Nanjing: IEEE, 2016.
［5］CAO L X, ZHANG J Y, WANG J Y, et al.Intelligent fault diagnosis of wind turbine gearbox based on long short-term memory networks［C］//2019 IEEE 28th International Symposium on Industrial Electronics.Vancouver：IEEE, 2019.
［6］LAKE B M, SALAKHUTDINOV R, TENENBAUM J B.Human-level concept learning through probabilistic program induction［J］.Science, 2015, 350(6266): 1332-1338.
［7］MNIH V, KAVUKCUOGLU K, SILVER D, et al.Playing atari with deep reinforcement learning［C］//
Conference and Workshop on Neural Information Processing Systems.Lake Tahoe: Computer Science, 2013．

［8］MNIH V, KAVUKCUOGLU K, SILVER D, et al.Human-level control through deep reinforcement learning［J］.Nature, 2015, 518(7540): 529-533.
［9］刘全, 翟建伟, 章宗长, 等.深度强化学习综述［J］.计算机学报, 2018, 41(1):1-27.
LIU Quan, ZHAI Jianwei, ZHANG Zongzhang, et al.A survey on deep reinforcement learning［J］.Chinese Journal of Computers,2018,41(1):1-27.
［10］林京, 屈梁生.基于连续小波变换的信号检测技术与故障诊断［J］.机械工程学报, 2000, 36(12): 95-100.
LIN Jing, QU Liangsheng.Feature detection and fault diagnosis based on continuous wavelet transform［J］.Journal of Mechanical Engineering, 2000, 36(12): 95-100.
［11］TAKAGI T, SUGENO M.Fuzzy identification of systems and its applications to modeling and control［J］.Readings in Fuzzy Sets for Intelligent Systems, 1993, 15(1):387-403.
［12］张彼德, 田源, 邹江平, 等.基于Choquet模糊积分的水电机组振动故障诊断［J］.振动与冲击, 2013，32(12):66-71.
ZHANG Bide，TIAN Yuan，ZOU Jiangping,et al.Vibration fault diagnosis of a Hydro-generating unit based on Choquet fuzzy integration［J］.Journal of Vibration and Shock,2013，32(12):66-71.
［13］石东源, 熊国江, 陈金富, 等.基于径向基函数神经网络和模糊积分融合的电网分区故障诊断［J］.中国电机工程学报, 2014(4):60-67.
SHI Dongyuan, XIONG Guojiang, CHEN Jinfu, et al.Divisional fault diagnosis of power grids based on RBF neural network and fuzzy integral fusion［J］.Proceedings of the CSEE,2014(4):60-67.
［14］GRABISCH M, MUROFUSHI T, SUGENO M.Fuzzy measures and integrals: theory and applications［M］.Heidelberg; Physica-Verlag, 2000.