Citation: | LI Jingzhao, HE Na, ZHANG Jinwei, et al. Fault diagnosis method for mine hoisting motor based on VMD and CNN-BiLSTM[J]. Journal of Mine Automation,2023,49(7):49-59. doi: 10.13272/j.issn.1671-251x.2022120065 |
[1] |
何俊峰,肖慧明. 矿井提升机健康管理系统研究[J]. 制造业自化化,2020,42(6):4-7,38.
HE Junfeng,XIAO Huiming. Reaserch on system of maganement for mine hoist[J]. Manufacturing Automation,2020,42(6):4-7,38.
|
[2] |
赵书涛,王二旭,陈秀新,等. 声振信号联合1D−CNN的大型电机故障诊断方法[J]. 哈尔滨工业大学学报,2020,52(9):116-122.
ZHAO Shutao,WANG Erxu,CHEN Xiuxin,et al. Fault diagnosis method for large motor based on sound-vibration signal combined with 1D-CNN[J]. Journal of Harbin Institute of Technology,2020,52(9):116-122.
|
[3] |
李伟,李硕. 理解数字声音——基于一般音频/环境声的计算机听觉综述[J]. 复旦学报(自然科学版),2019,58(3):269-313.
LI Wei,LI Shuo. Understanding digital audio−a review of general audio/ambient sound based computer audition[J]. Journal of Fudan University(Natural Science),2019,58(3):269-313.
|
[4] |
李海. 基于EMD和特征融合的电机故障诊断[D]. 杭州: 浙江大学, 2013.
LI Hai. Faults diagnosis of motor based on EMD and feature-fusion[D]. Hangzhou: Zhejiang University, 2013.
|
[5] |
孙杰臣. 基于音频的矿井提升机故障诊断和健康预测系统[D]. 淮南: 安徽理工大学, 2021.
SUN Jiechen. Fault diagnosis and health prediction system for mine hoists based on audio signal[D]. Huainan: Anhui University of Science and Technology, 2021.
|
[6] |
路敬祎,马雯萍,叶东,等. 基于VMD的音频信号增强算法研究[J]. 机械工程学报,2018,54(10):10-15. doi: 10.3901/JME.2018.10.010
LU Jingyi,MA Wenping,YE Dong,et al. Algorithm of sound signal enhancement based on VMD[J]. Journal of Mechanical Engineering,2018,54(10):10-15. doi: 10.3901/JME.2018.10.010
|
[7] |
丁石川,厉雪衣,杭俊,等. 深度学习理论及其在电机故障诊断中的研究现状与展望[J]. 电力系统保护与控制,2020,48(8):172-187.
DING Shichuan,LI Xueyi,HANG Jun,et al. Deep learning theory and its application to fault diagnosis of an electric machine[J]. Power System Protection and Control,2020,48(8):172-187.
|
[8] |
马立玲,刘潇然,沈伟,等. 基于一种改进的一维卷积神经网络电机故障诊断方法[J]. 北京理工大学学报,2020,40(10):1088-1093.
MA Liling,LIU Xiaoran,SHEN Wei,et al. Motor fault diagnosis method based on an improved one-dimensional convolutional neural network[J]. Transactions of Beijing Institute of Technology,2020,40(10):1088-1093.
|
[9] |
张鹏,束小曼,厉雪衣,等. 基于LSTM的交流电机系统故障诊断方法研究[J]. 电机与控制学报,2022,26(3):109-116.
ZHANG Peng,SHU Xiaoman,LI Xueyi,et al. LSTM-based fault diagnosis of AC electric machine system[J]. Electric Machines and Control,2022,26(3):109-116.
|
[10] |
向玲,王朋鹤,李京蓄. 基于CNN−LSTM的风电机组异常状态检测[J]. 振动与冲击,2021,40(22):11-17.
XIANG Ling,WANG Penghe,LI Jingxu. Abnormal state detection of wind turbines based on CNN-LSTM[J]. Journal of Vibration and Shock,2021,40(22):11-17.
|
[11] |
李可,牛园园,宿磊,等. 参数优化VMD的滚动轴承故障诊断方法[J]. 振动工程学报,2023,36(1):280-287.
LI Ke,NIU Yuanyuan,SU Lei,et al. Rolling bearing fault diagnosis method based on parameter optimized VMD[J]. Journal of Vibration Engineering,2023,36(1):280-287.
|
[12] |
ZOSSO D,DRAGOMIRETSKIY K. Variational mode decomposition[J]. IEEE Transactions on Signal Processing:A Publication of the IEEE Signal Procession Society,2014,62(3):531-544.
|
[13] |
MIRJALILI S,LEWIS A. The whale optimization algorithm[J]. Advances in Engineering Software,2016,95:51-67. doi: 10.1016/j.advengsoft.2016.01.008
|
[14] |
曹仕骏,郑近德,潘海洋,等. 基于改进自适应经验傅里叶分解的滚动轴承故障诊断方法[J]. 振动与冲击,2022,41(15):287-299.
CAO Shijun,ZHENG Jinde,PAN Haiyang,et al. Enhanced adaptive empirical Fourier decomposition based rolling bearing fault diagnosis method[J]. Journal of Vibration and Shock,2022,41(15):287-299.
|
[15] |
王前,王刚,蒋晗晗,等. 基于MFCC与CDET的滚动轴承故障诊断方法研究[J]. 控制工程,2019,26(9):1682-1686.
WANG Qian,WANG Gang,JIANG Hanhan et al. Study on fault diagnosis of rolling bearing based on MFCC and CDET[J]. Control Engineering of China,2019,26(9):1682-1686.
|
[16] |
李宏全,郭兴明,郑伊能. 基于EMD和MFCC的舒张期心杂音的分类识别[J]. 振动与冲击,2017,36(11):8-13.
LI Hongquan,GUO Xingming,ZHENG Yineng. Classification and recognition of diastolic heart murmurs based on EMD and MFCC[J]. Journal of Vibration and Shock,2017,36(11):8-13.
|
[17] |
刘思思,谭建平,易子馗. 基于MFCC和SVM的车窗电机异常噪声辨识方法研究[J]. 振动与冲击,2017,36(5):102-107.
LIU Sisi,TAN Jianping,YI Zikui. A window motor abnormal noiseidentification method based on MFCC and SVM[J]. Journal of Vibration and Shock,2017,36(5):102-107.
|
[18] |
崔佳嘉,马宏忠. 基于改进MFCC和3D-CNN的变压器铁心松动故障声纹识别模型[J]. 电机与控制学报,2022,26(12):150-160.
CUI Jiajia,MA Hongzhong. Voiceprint recognition model of transformer core looseness fault based on improved MFCC and 3D-CNN[J]. Electric Machines and Control,2022,26(12):150-160.
|
[19] |
汪欣,毛东兴,李晓东. 基于声信号和一维卷积神经网络的电机故障诊断研究[J]. 噪声与振动控制,2021,41(2):125-129.
WANG Xin,MAO Dongxing,LI Xiaodong. Motor fault diagnosis using microphones and one-dimensional convolutional neural network[J]. Noise and Vibration Control,2021,41(2):125-129.
|
[20] |
董绍江,李洋,梁天,等. 基于CNN−BiLSTM的滚动轴承变工况故障诊断方法[J]. 振动. 测试与诊断,2022,42(5):1009-1016,1040.
DONG Shaojiang,LI Yang,LIANG Tian,et al. Fault diagnosis method of rolling bearing based on CNN-BiLSTM under variable working conditions[J]. Journal of Vibration,Measurement & Diagnosis,2022,42(5):1009-1016,1040.
|
[21] |
王宏伟,孙文磊,张小栋,等. 基于优化VMD复合多尺度散布熵及LSTM的风力发电机齿轮箱故障诊断方法研究[J]. 太阳能学报,2022,43(4):288-295.
WANG Hongwei,SUN Wenlei,ZHANG Xiaodong,et al. Fault diagnosis method of wind turbine's gearbox based on composite multiscale dispersion entropy of optimised WMD and LSTM[J]. Acta Energiae Solaris Sinica,2022,43(4):288-295.
|