Citation: | LI Man, PAN Nannan, DUAN Yong, et al. Construction of health index and condition assessment of coal mine rotating machinery[J]. Journal of Mine Automation,2022,48(9):33-41. doi: 10.13272/j.issn.1671-251x.18004 |
[1] |
王国法,刘峰,庞义辉,等. 煤矿智能化−煤炭工业高质量发展的核心技术支撑[J]. 煤炭学报,2019,44(2):349-357. doi: 10.13225/j.cnki.jccs.2018.2041
WANG Guofa,LIU Feng,PANG Yihui,et al. Coal mine intellectualization:the core technology of high quality development[J]. Journal of China Coal Society,2019,44(2):349-357. doi: 10.13225/j.cnki.jccs.2018.2041
|
[2] |
王国法,刘峰,孟祥军,等. 煤矿智能化(初级阶段)研究与实践[J]. 煤炭科学技术,2019,47(8):1-36. doi: 10.13199/j.cnki.cst.2019.08.001
WANG Guofa,LIU Feng,MENG Xiangjun,et al. Research and practice on intelligent coal mine construction (primary stage)[J]. Coal Science and Technology,2019,47(8):1-36. doi: 10.13199/j.cnki.cst.2019.08.001
|
[3] |
李国发,王彦博,何佳龙,等. 机电装备健康状态评估研究进展及发展趋势[J]. 吉林大学学报(工学版),2022,52(2):267-279.
LI Guofa,WANG Yanbo,HE Jialong,et al. Research progress and development trend of health assessment of electromechanical equipment[J]. Journal of Jilin University(Engineering and Technology Edition),2022,52(2):267-279.
|
[4] |
潘红光,裴嘉宝,侯媛彬. 智慧煤矿数据驱动检测技术研究[J]. 工矿自动化,2020,46(10):49-54. doi: 10.13272/j.issn.1671-251x.17606
PAN Hongguang,PEI Jiabao,HOU Yuanbin. Research on data-driven detection technology of smart coal mine[J]. Industry and Mine Automation,2020,46(10):49-54. doi: 10.13272/j.issn.1671-251x.17606
|
[5] |
李杰其,胡良兵. 基于机器学习的设备预测性维护方法综述[J]. 计算机工程与应用,2020,56(21):11-19. doi: 10.3778/j.issn.1002-8331.2006-0016
LI Jieqi,HU Liangbing. Review of machine learning for predictive maintenance[J]. Computer Engineering and Applications,2020,56(21):11-19. doi: 10.3778/j.issn.1002-8331.2006-0016
|
[6] |
来杰,王晓丹,向前,等. 自编码器及其应用综述[J]. 通信学报,2021,42(9):218-230. doi: 10.11959/j.issn.1000-436x.2021160
LAI Jie,WANG Xiaodan,XIANG Qian,et al. Review on autoencoder and its application[J]. Journal on Communications,2021,42(9):218-230. doi: 10.11959/j.issn.1000-436x.2021160
|
[7] |
HUANG Feng,LI Zhixiong,XIANG Shuchen,et al. A new wind power forecasting algorithm based on long short-term memory neural network[J]. International Transactions on Electrical Energy Systems,2021,31(12). DOI: 10.1002/2050-7038.13233.
|
[8] |
JANA D,PATIL J,HERKAL S,et al. CNN and convolutional autoencoder (CAE) based real-time sensor fault detection,localization,and correction[J]. Mechanical Systems and Signal Processing,2022,169. DOI: 10.1016/j.ymssp.2021.108723.
|
[9] |
石延新,何进荣,李照奎,等. 3D卷积自编码器高光谱图像分类模型[J]. 中国图象图形学报,2021,26(8):2021-2036. doi: 10.11834/jig.210146
SHI Yanxin,HE Jinrong,LI Zhaokui,et al. Hyperspectral image classification model based on 3D convolutional auto-encoder[J]. Journal of Image and Graphics,2021,26(8):2021-2036. doi: 10.11834/jig.210146
|
[10] |
RICOTTA C,PAVOINE S. A new parametric measure of functional dissimilarity:bridging the gap between the Bray-Curtis dissimilarity and the Euclidean distance[J]. Ecological Modelling,2022,466. DOI: 10.1016/j.ecolmodel.2022.109880.
|
[11] |
赵志宏,李乐豪,杨绍普,等. 一种无监督的轴承健康指标及早期故障检测方法[J]. 中国机械工程,2022,33(10):1234-1243. doi: 10.3969/j.issn.1004-132X.2022.10.013
ZHAO Zhihong,LI Lehao,YANG Shaopu,et al. An unsupervised bearing health indicator and early fault detection method[J]. China Mechanical Engineering,2022,33(10):1234-1243. doi: 10.3969/j.issn.1004-132X.2022.10.013
|
[12] |
WANG Biao,LEI Yaguo,LI Naipeng,et al. A hybrid prognostics approach for estimating remaining useful life of rolling element bearings[J]. IEEE Transactions on Reliability,2020,69(1):401-412. doi: 10.1109/TR.2018.2882682
|
[13] |
雷亚国,韩天宇,王彪,等. XJTU−SY滚动轴承加速寿命试验数据集解读[J]. 机械工程学报,2019,55(16):1-6. doi: 10.3901/JME.2019.16.001
LEI Yaguo,HAN Tianyu,WANG Biao,et al. XJTU-SY rolling element bearing accelerated life test datasets:a tutorial[J]. Journal of Mechanical Engineering,2019,55(16):1-6. doi: 10.3901/JME.2019.16.001
|
[14] |
张永峰,陆志强. 基于集成神经网络的剩余寿命预测[J]. 工程科学学报,2020,42(10):1372-1380.
ZHANG Yongfeng,LU Zhiqiang. Remaining useful life prediction based on an integrated neural network[J]. Chinese Journal of Engineering,2020,42(10):1372-1380.
|
[15] |
KHELIF R,CHEBEL-MORELLO B,MALINOWSKI S,et al. Direct remaining useful life estimation based on support vector regression[J]. IEEE Transactions on Industrial Electronics,2017,64(3):2276-2285. doi: 10.1109/TIE.2016.2623260
|
[16] |
徐海铭,夏乔阳,李勇,等. 基于深度可分离卷积神经网络轴承剩余寿命预测[J]. 机械强度,2022,44(4):763-771. doi: 10.16579/j.issn.1001.9669.2022.04.001
XU Haiming,XIA Qiaoyang,LI Yong,et al. Bearing remaining life prediction based on deep separable convolutional neural network[J]. Journal of Mechanical Strength,2022,44(4):763-771. doi: 10.16579/j.issn.1001.9669.2022.04.001
|
[17] |
张小刚,丁华,王晓波,等. 深度残差网络在滚动轴承故障诊断中的研究[J]. 机械设计与制造,2022,371(1):77-80. doi: 10.3969/j.issn.1001-3997.2022.01.017
ZHANG Xiaogang,DING Hua,WANG Xiaobo,et al. Study on fault diagnosis of rolling bearing by deep residual network[J]. Machinery Design & Manufacture,2022,371(1):77-80. doi: 10.3969/j.issn.1001-3997.2022.01.017
|