[1]
|
RAI A, UPADHYAY S H. Intelligent bearing performance degradation assessment and remaining useful life prediction based on self-organising map and support vector regression[J]. Proceedings of the Institution of Mechanical Engineers, Part C:Journal of Mechanical Engineering Science, 2018, 232(6): 1118-1132. doi: 10.1177/0954406217700180
|
[2]
|
ZHAO R, YAN R Q, CHEN Z H, et al. Deep learning and its applications to machine health monitoring[J]. Mechanical Systems and Signal Processing, 2019, 115: 213-237. doi: 10.1016/j.ymssp.2018.05.050
|
[3]
|
TSE P W, WANG D. Enhancing the abilities in assessing slurry pumps' performance degradation and estimating their remaining useful lives by using captured vibration signals[J]. Journal of Vibration and Control, 2017, 23(12): 1925-1937. doi: 10.1177/1077546315604522
|
[4]
|
程道来, 贾玉琛, 潘玉娜. 基于S时频熵的球轴承性能退化特征指标提取方法[J]. 轴承, 2019(4): 59-62. doi: 10.19533/j.issn1000-3762.2019.04.015CHENG D L, JIA Y C, PAN Y N. Extraction method for performance degradation characteristic indexes of ball bearings based on S-Time-Frequency entropy[J]. Bearing, 2019(4): 59-62. (in Chinese) doi: 10.19533/j.issn1000-3762.2019.04.015
|
[5]
|
范国良, 李爱平, 刘雪梅, 等. 基于信息熵与Lempel-Ziv的拧紧设备性能评估方法[J]. 振动、测试与诊断, 2019, 39(1): 88-94. doi: 10.16450/j.cnki.issn.1004-6801.2019.01.014FAN G L, LI A P, LIU X M, et al. Performance evaluation of tightening equipment based on information entropy and Lempel-Ziv[J]. Journal of Vibration, Measurement & Diagnosis, 2019, 39(1): 88-94. (in Chinese) doi: 10.16450/j.cnki.issn.1004-6801.2019.01.014
|
[6]
|
XU F, SONG X B, TSUI K L, et al. Bearing performance degradation assessment based on ensemble empirical mode decomposition and affinity propagation clustering[J]. IEEE Access, 2019, 7: 54623-54637. doi: 10.1109/ACCESS.2019.2913186
|
[7]
|
王冰, 胡雄, 李洪儒, 等. 基于基本尺度熵与GG模糊聚类的轴承性能退化状态识别[J]. 振动与冲击, 2019, 38(5): 190-197 + 221. doi: 10.13465/j.cnki.jvs.2019.05.027WANG B, HU X, LI H R, et al. Rolling bearing performance degradation state recognition based on basic scale entropy and GG fuzzy clustering[J]. Journal of Vibration and Shock, 2019, 38(5): 190-197 + 221. (in Chinese) doi: 10.13465/j.cnki.jvs.2019.05.027
|
[8]
|
WANG F L, ZHOU J M, ZHANG C C, et al. Evaluation of rolling bearing performance degradation using autoregressive model energy ratio and support vector data description[J]. Machine Tool & Hydraulics, 2020, 48(12): 103-111.
|
[9]
|
WANG F, JIANG H K, SHAO H D, et al. An adaptive deep convolutional neural network for rolling bearing fault diagnosis[J]. Measurement Science and Technology, 2017, 28(9): 095005. doi: 10.1088/1361-6501/aa6e22
|
[10]
|
JIA F, LEI Y G, GUO L, et al. A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines[J]. Neurocomputing, 2018, 272: 619-628. doi: 10.1016/j.neucom.2017.07.032
|
[11]
|
GUO L, LEI Y G, LI N P, et al. Deep convolution feature learning for health indicator construction of bearings[C]//2017 Prognostics and System Health Management Conference. Harbin: IEEE, 2017: 1-6
|
[12]
|
YIN J T, ZHAO W T. Fault diagnosis network design for vehicle on-board equipments of high-speed railway: a deep learning approach[J]. Engineering Applications of Artificial Intelligence, 2016, 56: 250-259. doi: 10.1016/j.engappai.2016.10.002
|
[13]
|
DONG S Z, WEN G R, LEI Z H, et al. Transfer learning for bearing performance degradation assessment based on deep hierarchical features[J]. ISA Transactions, 2021, 108: 343-355. doi: 10.1016/j.isatra.2020.09.004
|
[14]
|
朱义. 基于CHMM的设备性能退化评估方法研究[D]. 上海: 上海交通大学, 2009ZHU Y. Research on CHMM based equipment performance degradation assessment[D]. Shanghai: Shanghai Jiao Tong University, 2009. (in Chinese)
|
[15]
|
JIANG H M, CHEN J, DONG G M, et al. An intelligent performance degradation assessment method for bearings[J]. Journal of Vibration and Control, 2017, 23(18): 3023-3040. doi: 10.1177/1077546315624996
|
[16]
|
HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7): 1527-1554. doi: 10.1162/neco.2006.18.7.1527
|
[17]
|
XIAO W B, CHEN J, DONG G M, et al. A multichannel fusion approach based on coupled hidden Markov models for rolling element bearing fault diagnosis[J]. Proceedings of the Institution of Mechanical Engineers, Part C:Journal of Mechanical Engineering Science, 2012, 226(1): 202-216. doi: 10.1177/0954406211412015
|
[18]
|
潘玉娜. 滚动轴承的性能退化特征提取及评估方法研究[D]. 上海: 上海交通大学, 2011PAN Y N. Study on feature extraction and assessment method of rolling element bearing performance degradation[D]. Shanghai: Shanghai Jiao Tong University, 2011. (in Chinese)
|
[19]
|
QIU H, LEE J, LIN J, et al. Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics[J]. Journal of Sound and Vibration, 2006, 289(4-5): 1066-1090. doi: 10.1016/j.jsv.2005.03.007
|
[20]
|
梁珊. 基于深度信念网络的滚动轴承故障诊断研究[D]. 南昌: 南昌航空大学, 2019LIANG S. Research on fault diagnosis of rolling bearing based on deep belief network[D]. Nanchang: Nanchang Hangkong University, 2019. (in Chinese)
|
[21]
|
GOYAL P, DOLLAR P, GIRSHIK R B. Accurate, large mini-batch SGD: training Image Net in 1 Hour[J]. Computer Vision and Pattern Recognition, 2017, 34(2): 102-114.
|
[22]
|
赵洪山, 刘辉海. 基于性能改善深度置信网络的风电机组主轴承状态分析[J]. 电力自动化设备, 2018, 38(2): 44-49. doi: 10.16081/j.issn.1006-6047.2018.02.006ZHAO H S, LIU H H. Condition analysis of wind turbine main bearing based on deep belief network with improved performance[J]. Electric Power Automation Equipment, 2018, 38(2): 44-49. (in Chinese) doi: 10.16081/j.issn.1006-6047.2018.02.006
|
[23]
|
程道来, 贾玉琛, 潘玉娜. 一种新的滚动轴承性能退化指标S-时间熵的提取方法[J]. 机床与液压, 2019, 47(19): 181-185. doi: 10.3969/j.issn.1001-3881.2019.19.036CHENG D L, JIA Y C, PAN Y N. New method for extracting S-time entropy of performance degradation index of rolling bearings[J]. Machine Tool & Hydraulics, 2019, 47(19): 181-185. (in Chinese) doi: 10.3969/j.issn.1001-3881.2019.19.036
|
[24]
|
王斐, 房立清, 赵玉龙, 等. 基于VMD和SVDD的滚动轴承早期微弱故障检测和性能退化评估研究[J]. 振动与冲击, 2019, 38(22): 224-230 + 256.WANG F, FANG L Q, ZHAO Y L, et al. Rolling bearing early weak fault detection and performance degradation assessment based on VMD and SVDD[J]. Journal of Vibration and Shock, 2019, 38(22): 224-230 + 256. (in Chinese)
|