Rolling bearing fault diagnosis based on bacterial foraging algorithm and deep belief network
Tao Jie1,2Liu Yilun1,3Yang Dalian1,4Bin Guangfu4
1.College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China;
2. Lab of Knowledge Processing and Networked Manufacturing, Hunan University of Science and Technology, Xiangtan 411201, China;
3. Light Alloy Research Institute, Central South University, Changsha 410083, China;
4. Hunan Provincial Key Lab of Health Maintenance for Mechanical Equipment, Hunan University of Science and Technology, Xiangtan 411201, China
Abstract：When studying rolling bearing fault diagnosis with the deep belief network method, parameters in the deep belief network have a great effect on fault diagnosis results and it is hard to obtain suitable parameters. Here, the fault diagnosis method based on the bacterial foraging algorithm and the deep belief network was proposed to improve the correct rate of bearing fault diagnosis. The parallel search ability of the bacterial foraging algorithm was adopted to effectively choose the number of hidden layer, the number of hidden nodes, the learning rate in a deep belief network. The deep belief network’s training data classification error was used to calculate the fitness function of the bacterial foraging algorithm to build an appropriate fault classifier and finish rolling bearing fault diagnosis. The test results showed that the correct rate of the proposed method for rolling bearing fault diagnosis reaches 98.5%; compared with BPNN, SVM and KNN methods, the proposed method can more stably and more accurately identify rolling bearing faults.
陶洁1,2，刘义伦1,3，杨大炼1，4，宾光富4 . 基于细菌觅食决策和深度置信网络的滚动轴承故障诊断研究[J]. 振动与冲击, 2017, 36(23): 68-74.
Tao Jie1,2Liu Yilun1,3Yang Dalian1,4Bin Guangfu4. Rolling bearing fault diagnosis based on bacterial foraging algorithm and deep belief network. JOURNAL OF VIBRATION AND SHOCK, 2017, 36(23): 68-74.
[1] 郭亮, 高宏力, 张一文, 等. 基于深度学习理论的轴承状态识别研究[J]. 振动与冲击, 2016, 35(12): 166-170.
GUO Liang, GAO Hong-li, Zhang Yi-wen, et al. Research on bearing condition monitoring based on deep learning [J]. Jouranl of Vibaration and Shock,2016, 35(12): 166-170.
[2] El-Thalji I, Jantunen E. A summary of fault modelling and predictive health monitoring of rolling element bearings[J]. Mechanical Systems and Signal Processing, 2015, 60: 252-272.
[3] Bin G F, Gao J J, Li X J, et al. Early fault diagnosis of rotating machinery based on wavelet packets—empirical mode decomposition feature extraction and neural network[J]. Mechanical Systems and Signal Processing, 2012, 27: 696-711.
[4] 王国彪,何正嘉,陈雪峰,等.机械故障诊断基础研究 “何去何从”[J].机械工程学报,2013,49(1):63-72.
WANG Guo-biao, HE Zheng-jia, CHEN Xue-feng, et al. Basic research on machinery fault diagnosis-What is the prescription[J], Journal of Mechanical Engineering, 49(1), 63−72, 2013.
[5] 文成林,吕菲亚，包哲静，刘妹琴.基于数据驱动的微小故障诊断方法综述[J].自动化学报,2016,42(9):1285-1299.
WEN Cheng-Lin, LV Fei-Ya, BAO Zhe-Jing, LIU Mei-Qin. A review of data driven-based incipient fault diagnosis [J].Acta Automatica Sinica, 2016, 42(9): 1285-1299
[6] 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 Ya-guo, 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.
[7] Schmidhuber J. Deep learning in neural networks: An overview[J]. Neural Networks, 2015, 61(9): 85- 117.
[8] Hinton G,Osindero S,Teh Y W.A fast learning algorithm for deep belief nets[J].Neural Computation,2006,18(7):1527-1554.
[9] Erhan D,Bengio Y, A. Courville, Why does unsupervised pre-training help deep learning [J],Journal of Machine Learning Research,2010,11(3) : 625-660.
[10] Hinton G, Salakhutdinov R. Reducion the dimensionality of data with neural networks [J], Science, 2006, 313: 504−507.
[11] Tran V T,Althobiani F,Ball A.Anapproach to fault diagnosis of reciprocating compressor valves using Teager-Kaeser energy operator and deep belief networks[J].Expert Systems with Application,2014,41(9):4113-4122.
[12] Tamilselvan P,Yibin W,Pingfeng W.Deep belief network based state classification for structural health diagnosis[J].Reliability Engineering and System Safety,2013,115(3):124-135.
[13] Sun J,Wyss R,Steinecker A,et al.Automated fault detection using deep belief networks for the quality inspection of electromotors[J]. tm-Technisches Messen, 2014, 81(5): 255- 263.
[14] Gan M, Wang C, Zhu C, Construction of hierarchical diagnosis network based on deep learning and itsapplication in the fault pattern recognition of rolling element bearings [J], Mechanical Systems and Signal Processing, 2016, 72: 92-104.
[15] 单外平, 曾雪琼. 基于深度信念网络的信号重构与轴承故障识别[J].电子设计工程,2016,24(4):67-71.
SHAN Wai-ping， ZENG Xue-qiong.Signal reconstruction and bearing fault identification based on deep belief network [J]. Electronic Design Engineering,2016,24(4):67-71.
[16] Shao H, Jiang H, Zhang X, et al. Rolling bearing fault diagnosis using an optimization deep belief network[J]. Measurement Science & Technology, 2015, 26(11): 115002.
[17] Passino K M.Biomimicry of bacterial foraging for distributed optimization and control[J].IEEE Control Systems Magazine，2002，22：52-67.
[18] 杨大炼, 刘义伦, 李学军,等. 基于细菌觅食优化决策的齿轮箱故障诊断[J]. 中南大学学报(自然科学版), 2015, 46(4): 1224-1229.
YANG Da-lian, LIU Yi-lun, LI Xue-jun, ZHOU Wei. Gearbox fault diagnosis based on bacterial foragingalgorithm optimization decisions [J]. Journal of Central South University (Science and Technology), 2015, 46(4): 1224-1229.
[19] 胡洁. 细菌觅食优化算法的改进及应用研究[D]. 武汉: 武汉理工大学, 2012.
HU Jie. Research on the Modified Bacteria Foraging Optimization Algorithm and Its Application [D]. Wuhan: Wuhan University of Technology, 2012.
[20] 周雅兰. 细菌觅食优化算法的研究与应用[J]. 计算机工程与应用, 2010, 46(20):16-21.
ZHOU Ya-lan. Research and application on bacteria foraging optimization algorithm [J]. Computer Engineering and Applications, 2010, 46(20):16-21.
