Abstract:In order to obtain the number and scale of convolution kernels in multi-scale one-dimensional convolution neural network under non-empirical guidance, and realize intelligent looseness diagnosis of fan foundation bolt, a bolt looseness diagnosis method for fan foundation based on multi-scale one-dimensional convolution neural network optimized by particle swarm optimization (PSO) is proposed. Firstly, the one-dimensional original vibration signal of the fan is collected and divided into the training set and validation set. Then, the number and scale of the convolution kernel in the multi-scale one-dimensional convolutional neural network are taken as the particles of the PSO, verification accuracy is set as fitness value, the particle speed and position are updated according to the fitness value. After training, a multi-scale one-dimensional convolution neural network with optimal number of convolution kernels and scale parameters is obtained. Finally, Test samples are input to obtain diagnostic results of the fan foundation bolt looseness. This method is applied to the bolt loosening diagnosis experiment of the fan foundation under stable speed and rising and falling speed. The experimental results show that the multi-scale one-dimensional convolutional neural network optimized by PSO can obtain the optimal parameters under non-empirical guidance, can extract effective looseness features from the one-dimensional original signal, and has a good looseness diagnostic effect.
[1] 《中国公路学报》编辑部. 中国隧道工程学术研究综述:2015[J]. 中国公路学报,2015,28(05):1-65.
EdiCPUtorial Department of China journal of Highway and Transport. Review on China’s tunnel engineering academic research:2015[J]. China journal of Highway and Transport, 2015,28(05):1-65.
[2] 韩坤林. 基于振动分析的公路隧道悬挂风机基础健康性监测方法[J]. 公路隧道,2016(01):45-48+22.
HAN Kun-lin. Health monitoring method for suspension fan foundation of highway tunnel based on vibration analysis[J]. Highway tunnel, 2016 (01): 45-48 + 22.
[3] 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,2015,051(021):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, 051(021): 49-56.
[4] 周飞燕,金林鹏,董 军.卷积神经网络研究综述[J].计算机学报,2017,40(06):1229-1251.
ZHOU Feiyan, JIN Linpeng, DONG Jun. Review of convolutional neural network [J]. Chinese Journal of Computers, 2017, 40(06):1229-1251.
[5] 鄢仁武,林 穿,高硕勋,等.基于小波时频图和卷积神经网络的断路器故障诊断分析[J].振动与冲击,2020,39(10):198-205.
YAN Renwu, LIN Chuan, GAO Shuoxun, et al. Fault diagnosis and analysis of circuit breaker based on wavelet time-frequency representations and convolution neural network [J]. Journal of Vibration and Shock, 2020, 39(10): 198-205.
[6] Wen L, Li X, Gao L, et al. A New Convolutional Neural Network Based Data-Driven Fault Diagnosis Method[J]. IEEE Transactions on Industrial Electronics, 2017, (99):1-1.
[7] Yang Z B, Jia M P. Ga-1dlcnn method and its application in bearing fault diagnosis[J]. Journal of Southeast University, 2019,35(01):36-42.
[8] 曲建岭,余路,袁涛,等.基于一维卷积神经网络的滚动轴承自适应故障诊断算法[J].仪器仪表学报,2018,39(07):134-143.
QU Jianling, YU Lu, YUAN Tao, et al. Adaptive fault diagnosis algorithm for rolling bearings based on one-dimensional convolutional neural network [J]. Chinese Journal of Scientific Instrument, 2018, 39(07): 134-143.
[9] 吴春志,江鹏程,冯辅周,等.基于一维卷积神经网络的齿轮箱故障诊断[J].振动与冲击,2018,37(22):51-56.
WU Chunzhi, JIANG Pengcheng, FENG Fuzhou, et al. Faults diagnosis method for gearboxes based on a 1-D convolutional neural network [J]. Journal of Vibration and Shock,, 2018, 37(22): 51-56.
[10] Huang W Y, Cheng J S, Yang Y, et al. An Improved deep convolutional neural network with multi-scale information for bearing fault diagnosis [J]. Neurocomputing, 2019, 359:77-92.
[11] 郭 晨,简 涛,徐从安,等.基于深度多尺度一维卷积神经网络的雷达舰船目标识别[J]. 电子与信息学报, 2019, 41(6): 1302-1309.
GUO Chen, JIAN Tao, XU Cong'an, et al. Radar HRRP Target Recognition Based on Deep Multi-Scale 1D Convolutional Neural Network [J]. Journal of Electronics & Information Technology, 2019, 41(6): 1302-1309.
[12] 吴 俊,管鲁阳,鲍 明,等.基于多尺度一维卷积神经网络的光纤振动事件识别[J]. 光电工程,2019,46(05):79-86.
WU Jun, GUAN Luyang, BAO Ming, et al. Vibration events recognition of optical fiber based on multi-scale 1-D CNN [J]. Opto-Electronic Engineering, 2019, 46(05): 79-86.
[13] 唐贤伦, 刘 庆, 张 娜, 等. 混合PSO优化卷积神经网络结构和参数[J]. 电子科技大学学报, 2018, 047(002):230-234.
TANG Xianlun, LIU Qing, ZHANG Na, et al. Optimizing Structure and Parameters of Convolutional Neural Networks Using Hybrid PSO [J]. Journal of University of Electronic Science and Technology of China, 2018, 047(002):230-234.