Citation: | ZHANG Jinbao, ZOU Tiangang, WANG Min, GUI Peng, GE Hongxia, WANG Cheng. Review on Remaining Useful Life Prediction of Rolling Bearing[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(1): 1-23. doi: 10.13433/j.cnki.1003-8728.20200489 |
[1] |
彭宇, 刘大同. 数据驱动故障预测和健康管理综述[J]. 仪器仪表学报, 2014, 35(3): 481-495 https://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201403001.htm
PENG Y, LIU D T. Data-driven prognostics and health management: a review of recent advances[J]. Chinese Journal of Scientific Instrument, 2014, 35(3): 481-495 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201403001.htm
|
[2] |
WANG D, TSUI K L, MIAO Q. Prognostics and health management: a review of vibration based bearing and gear health indicators[J]. IEEE Access, 2018, 6: 665-676 doi: 10.1109/ACCESS.2017.2774261
|
[3] |
DUAN Z H, WU T H, GUO S W, et al. Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review[J]. The International Journal of Advanced Manufacturing Technology, 2018, 96(1-4): 803-819 doi: 10.1007/s00170-017-1474-8
|
[4] |
DIEZ-OLIVAN A, DEL SER J, GALAR D, et al. Data fusion and machine learning for industrial prognosis: trends and perspectives towards industry 4.0[J]. Information Fusion, 2019, 50: 92-111 doi: 10.1016/j.inffus.2018.10.005
|
[5] |
孟光, 尤明懿. 基于状态监测的设备寿命预测与预防维护规划研究进展[J]. 振动与冲击, 2011, 30(8): 1-11 https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201108003.htm
MENG G, YOU M Y. Review on condition-based equipment residual life prediction and preventive maintenance scheduling[J]. Journal of Vibration and Shock, 2011, 30(8): 1-11 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201108003.htm
|
[6] |
ATAMURADOV V, MEDJAHER K, DERSIN P, et al. Prognostics and health management for maintenance practitioners-review, implementation and tools evaluation[J]. International Journal of Prognostics and Health Management, 2017, 8(3): 1-31
|
[7] |
ZHANG W T, YANG D, WANG H C. Data-driven methods for predictive maintenance of industrial equipment: a survey[J]. IEEE Systems Journal, 2019, 13(3): 2213-2227 doi: 10.1109/JSYST.2019.2905565
|
[8] |
周琼, 李正美, 张而耕. 涡轮泵轴承寿命预测及研究进展[J]. 应用技术学报, 2017, 17(4): 352-357 https://www.cnki.com.cn/Article/CJFDTOTAL-SHSX201704014.htm
ZHOU Q, LI Z M, ZHANG E G. Review on the turbopump bearing life prediction[J]. Journal of Technology, 2017, 17(4): 352-357 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-SHSX201704014.htm
|
[9] |
宋宏智, 李力, 杨兴宽, 等. 高速机车轴承故障诊断与剩余寿命预测的发展及展望[J]. 轴承, 2020(3): 61-67 https://www.cnki.com.cn/Article/CJFDTOTAL-CUCW202003016.htm
SONG H Z, LI L, YANG X K, et al. Development and prospect for fault diagnosis and remain life prediction of high speed locomotive bearings[J]. Bearing, 2020(3): 61-67 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CUCW202003016.htm
|
[10] |
SAUFI S R, AHMAD Z A B, LEONG M S, et al. An intelligent bearing fault diagnosis system: a review[J]. MATEC Web of Conferences, 2019, 255: 06005 doi: 10.1051/matecconf/201925506005
|
[11] |
CHOUDHARY A, GOYAL D, SHIMI S L, et al. Condition monitoring and fault diagnosis of induction motors: a review[J]. Archives of Computational Methods in Engineering, 2019, 26(4): 1221-1238 doi: 10.1007/s11831-018-9286-z
|
[12] |
LAI C D, MURTHY D N P, XIE M. Weibull distributions[J]. Wiley Interdisciplinary Reviews: Computational Statistics, 2011, 3(3): 282-287 doi: 10.1002/wics.157
|
[13] |
ELATTAR H M, ELMINIR H K, RIAD A M. Prognostics: a literature review[J]. Complex & Intelligent Systems, 2016, 2(2): 125-154
|
[14] |
王小林. 基于非线性Wiener过程的产品退化建模与剩余寿命预测研究[D]. 长沙: 国防科学技术大学, 2014
WANG X L. Research of degradation modeling and residual life estimation for deterioriate products based on nonlinear Wiener process[D]. Changsha: National University of Defense Technology, 2014 (in Chinese)
|
[15] |
ZHANG H Y, ZHANG C W, WANG C L, et al. A survey of non-destructive techniques used for inspection of bearing steel balls[J]. Measurement, 2020, 159: 107773 doi: 10.1016/j.measurement.2020.107773
|
[16] |
SOUALHI M, NGUYEN K T P, SOUALHI A, et al. Health monitoring of bearing and gear faults by using a new health indicator extracted from current signals[J]. Measurement, 2019, 141: 37-51 doi: 10.1016/j.measurement.2019.03.065
|
[17] |
李永波. 滚动轴承故障特征提取与早期诊断方法研究[D]. 哈尔滨: 哈尔滨工业大学, 2017
LI Y B. Investigation of fault feature extraction and early fault diagnosis for rolling bearings[J]. Harbin: Harbin Institute of Technology, 2017 (in Chinese)
|
[18] |
雷亚国, 贾峰, 孔德同, 等. 大数据下机械智能故障诊断的机遇与挑战[J]. 机械工程学报, 2018, 54(5): 94-104 https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201805011.htm
LEI Y G, JIA F, KONG D T, et al. Opportunities and challenges of machinery intelligent fault diagnosis in big data era[J]. Journal of Mechanical Engineering, 2018, 54(5): 94-104 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201805011.htm
|
[19] |
CAESARENDRA W. Vibration and acoustic emission- based condition monitoring and prognostic methods for very low speed slew bearing[D]. Wollongong: University of Wollongong, 2015
|
[20] |
李兴林, 张永恩, 张仰平, 等. 滚动轴承疲劳寿命强化试验评估方法研究[J]. 轴承, 2003(4): 26-29+33 doi: 10.3969/j.issn.1000-3762.2003.04.009
LI X L, ZHANG Y E, ZHANG Y P, et al. Study on estimation methods of accelerated rolling fatigue life test[J]. Bearing, 2003(4): 26-29+33 (in Chinese) doi: 10.3969/j.issn.1000-3762.2003.04.009
|
[21] |
徐人平, 段小建, 胡志勇, 等. 滚动轴承疲劳寿命P-S-N曲线[J]. 轴承, 1996(1): 21-23 https://www.cnki.com.cn/Article/CJFDTOTAL-CUCW601.009.htm
XU R P, DUAN X J, HU Z Y, et al. Fatigue life P-S-N curve of rolling bearings[J]. Bearing, 1996(1): 21-23 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CUCW601.009.htm
|
[22] |
徐东, 徐永成, 陈循, 等. 滚动轴承加速寿命试验技术研究[J]. 国防科技大学学报, 2010, 32(6): 122-129 https://www.cnki.com.cn/Article/CJFDTOTAL-GFKJ201006023.htm
XU D, XU Y C, CHEN X, et al. Research on accelerated life test for rolling element bearings[J]. Journal of National University of Defense Technology, 2010, 32(6): 122-129 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GFKJ201006023.htm
|
[23] |
殷凤龙. 基于加速寿命试验的滚动轴承寿命预测研究[D]. 长沙: 国防科学技术大学, 2012
YIN F L. Research on life prediction for rolling bearing based on accelerated life testing[D]. Changsha: National University of Defense Technology, 2012 (in Chinese)
|
[24] |
Case Western Reserve University. Bearing data center[EB/OL]. 2020-10-10. http://csegroups.case.edu/bearing-datacenter/pages/apparatus-procedures
|
[25] |
SMITH W A, RANDALL R B. Rolling element bearing diagnostics using the Case Western Reserve University data: a benchmark study[J]. Mechanical Systems and Signal Processing, 2015, 64-65: 100-131 doi: 10.1016/j.ymssp.2015.04.021
|
[26] |
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
|
[27] |
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 https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201916001.htm
LEI Y G, HAN T Y, WANG B, et al. XJTU-SY rolling element bearing accelerated life test datasets: a tutorial[J]. Journal of Mechanical Engineering, 2019, 55(16): 1-6 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201916001.htm
|
[28] |
NECTOUX P, GOURIVEAU R, MEDJAHER K, et al. PRONOSTIA: an experimental platform for bearings accelerated degradation tests[C]//Proceedings of IEEE International Conference on Prognostics and Health Management, PHM'12. Denver, USA: IEEE, 2012: 1-8
|
[29] |
HUANG H, BADDOUR N. Bearing vibration data collected under time-varying rotational speed conditions[J]. Data in Brief, 2018, 21: 1745-1749 doi: 10.1016/j.dib.2018.11.019
|
[30] |
LESSMEIER C, KIMOTHO J K, ZIMMER D, et al. Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: a benchmark data set for data-driven classification[C]//Proceedings of European Conference of the Prognostics and Health Management Society 2016. Bilbao, Spain: PHM, 2016: 1-17
|
[31] |
MCFADDEN P D, SMITH J D. Model for the vibration produced by a single point defect in a rolling element bearing[J]. Journal of Sound and Vibration, 1984, 96(1): 69-82 doi: 10.1016/0022-460X(84)90595-9
|
[32] |
王凯, 剡昌锋, 王风涛, 等. 深沟球轴承复合故障动力学特征[J]. 哈尔滨工业大学学报, 2020, 52(1): 133-140 https://www.cnki.com.cn/Article/CJFDTOTAL-HEBX202001022.htm
WANG K, YAN C F, WANG F T, et al. Dynamic characteristics of compound fault in deep groove ball bearing[J]. Journal of Harbin Institute of Technology, 2020, 52(1): 133-140 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HEBX202001022.htm
|
[33] |
BUZZONI M, D'ELIA G, COCCONCELLI M. A tool for validating and benchmarking signal processing techniques applied to machine diagnosis[J]. Mechanical Systems and Signal Processing, 2020, 139: 106618 doi: 10.1016/j.ymssp.2020.106618
|
[34] |
RAI A, UPADHYAY S H. A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings[J]. Tribology International, 2016, 96: 289-306 doi: 10.1016/j.triboint.2015.12.037
|
[35] |
KOPSINIS Y, MCLAUGHLIN S. Development of EMD-based denoising methods inspired by wavelet thresholding[J]. IEEE Transactions on Signal Processing, 2009, 57(4): 1351-1362 doi: 10.1109/TSP.2009.2013885
|
[36] |
FENG Z P, ZHOU Y K, ZUO M J, et al. Atomic decomposition and sparse representation for complex signal analysis in machinery fault diagnosis: a review with examples[J]. Measurement, 2017, 103: 106-132 doi: 10.1016/j.measurement.2017.02.031
|
[37] |
陈昌, 汤宝平, 吕中亮. 基于威布尔分布及最小二乘支持向量机的滚动轴承退化趋势预测[J]. 振动与冲击, 2014, 33(20): 52-56 https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201420011.htm
CHEN C, TANG B P, LYU Z L. Degradation trend prediction of rolling bearings based on Weibull distribution and least squares support vector machine[J]. Journal of Vibration and Shock, 2014, 33(20): 52-56 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201420011.htm
|
[38] |
CONG F Y, CHEN J, PAN Y. Kolmogorov-Smirnov test for rolling bearing performance degradation assessment and prognosis[J]. Journal of Vibration and Control, 2011, 17(9): 1337-1347 doi: 10.1177/1077546310384003
|
[39] |
WANG Y X, XIANG J W, MARKERT R, et al. Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: a review with applications[J]. Mechanical Systems and Signal Processing, 2016, 66-67: 679-698 doi: 10.1016/j.ymssp.2015.04.039
|
[40] |
WANG B, HU X, LI H R. Rolling bearing performance degradation condition recognition based on mathematical morphological fractal dimension and fuzzy C-means[J]. Measurement, 2017, 109: 1-8 doi: 10.1016/j.measurement.2017.05.033
|
[41] |
MENG Z, LI J, YIN N, et al. Remaining useful life prediction of rolling bearing using fractal theory[J]. Measurement, 2020, 156: 107572 doi: 10.1016/j.measurement.2020.107572
|
[42] |
LI Q, LIANG S Y, YANG J G, et al. Long range dependence prognostics for bearing vibration intensity chaotic time series[J]. Entropy, 2016, 18(1): 23 doi: 10.3390/e18010023
|
[43] |
CAESARENDRA W, TJAHJOWIDODO T. A review of feature extraction methods in vibration-based condition monitoring and its application for degradation trend estimation of low-speed slew bearing[J]. Machines, 2017, 5(4): 21 doi: 10.3390/machines5040021
|
[44] |
PINCUS S M. Approximate entropy as a measure of system complexity[J]. Proceedings of the National Academy of Sciences of the United States of America, 1991, 88(6): 2297-2301 doi: 10.1073/pnas.88.6.2297
|
[45] |
RICHMAN J S, MOORMAN J R. Physiological time- series analysis using approximate entropy and sample entropy[J]. American Journal of Physiology-Heart and Circulatory Physiology, 2000, 278(6): H2039-H2049 doi: 10.1152/ajpheart.2000.278.6.H2039
|
[46] |
CHEN W T, ZHUANG J, YU W X, et al. Measuring complexity using FuzzyEn, ApEn, and SampEn[J]. Medical Engineering & Physics, 2009, 31(1): 61-68
|
[47] |
BANDT C, POMPE B. Permutation entropy: a natural complexity measure for time series[J]. Physical Review Letters, 2002, 88(17): 174102 doi: 10.1103/PhysRevLett.88.174102
|
[48] |
ROSTAGHI M, AZAMI H. Dispersion entropy: a measure for time-series analysis[J]. IEEE Signal Processing Letters, 2016, 23(5): 610-614 doi: 10.1109/LSP.2016.2542881
|
[49] |
LOUTAS T H, ROULIAS D, GEORGOULAS G. Remaining useful life estimation in rolling bearings utilizing data-driven probabilistic E-support vectors regression[J]. IEEE Transactions on Reliability, 2013, 62(4): 821-832 doi: 10.1109/TR.2013.2285318
|
[50] |
COSTA M, GOLDBERGER A L, PENG C K. Multiscale entropy analysis of complex physiologic time series[J]. Physical Review Letters, 2002, 89(6): 068102 doi: 10.1103/PhysRevLett.89.068102
|
[51] |
HUMEAU-HEURTIER A. The multiscale entropy algorithm and its variants: a review[J]. Entropy, 2015, 17(5): 3110-3123 doi: 10.3390/e17053110
|
[52] |
LI Y B, WANG X Z, LIU Z B, et al. The entropy algorithm and its variants in the fault diagnosis of rotating machinery: a review[J]. IEEE Access, 2018, 6: 66723-66741 doi: 10.1109/ACCESS.2018.2873782
|
[53] |
SHAO H D, CHENG J S, JIANG H K, et al. Enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy for early fault prognosis of bearing[J]. Knowledge-Based Systems, 2020, 188: 105022 doi: 10.1016/j.knosys.2019.105022
|
[54] |
HONG S, ZHOU Z, ZIO E, et al. An adaptive method for health trend prediction of rotating bearings[J]. Digital Signal Processing, 2014, 35: 117-123 doi: 10.1016/j.dsp.2014.08.006
|
[55] |
YU H, LI H R, XU B H. Rolling bearing degradation state identification based on LCD relative spectral entropy[J]. Journal of Failure Analysis and Prevention, 2016, 16(4): 655-666 doi: 10.1007/s11668-016-0133-y
|
[56] |
任玲辉, 刘凯, 张海燕. 基于图像处理技术的机械故障诊断研究进展[J]. 机械设计与研究, 2011, 27(5): 21-24 https://www.cnki.com.cn/Article/CJFDTOTAL-JSYY201105008.htm
REN L H, LIU K, ZHANG H Y. Progress on mechanical fault diagnosis based on image processing[J]. Machine Design and Research, 2011, 27(5): 21-24 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSYY201105008.htm
|
[57] |
章立军, 刘博, 张彬, 等. 基于时频图像融合的轴承性能退化特征提取方法[J]. 机械工程学报, 2013, 49(22): 53-58 https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201322009.htm
ZHANG L J, LIU B, ZHANG B, et al. Feature extraction method of bearing performance degradation based on time-frequency image fusion[J]. Journal of Mechanical Engineering, 2013, 49(22): 53-58 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201322009.htm
|
[58] |
WANG H C, CHEN J. Performance degradation assessment of rolling bearing based on bispectrum and support vector data description[J]. Journal of Vibration and Control, 2014, 20(13): 2032-2041 doi: 10.1177/1077546313483653
|
[59] |
KAPLAN K, KAYA Y, KUNCAN M, et al. An improved feature extraction method using texture analysis with LBP for bearing fault diagnosis[J]. Applied Soft Computing, 2020, 87: 106019 doi: 10.1016/j.asoc.2019.106019
|
[60] |
张前图, 房立清. 基于图像形状特征和LLTSA的故障诊断方法[J]. 振动与冲击, 2016, 35(9): 172-177 https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201609029.htm
ZHANG Q T, FANG L Q. Fault diagnosis method based on image shape features and LLTSA[J]. Journal of Vibration and Shock, 2016, 35(9): 172-177 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201609029.htm
|
[61] |
QIAN Y N, YAN R Q, HU S J. Bearing degradation evaluation using recurrence quantification analysis and Kalman filter[J]. IEEE Transactions on Instrumentation and Measurement, 2014, 63(11): 2599-2610 doi: 10.1109/TIM.2014.2313034
|
[62] |
张彬. 数据驱动的机械设备性能退化建模与剩余寿命预测研究[D]. 北京: 北京科技大学, 2016
ZHANG B. Research on data-driven performance degradation modelling and remaining useful life prediction for mechanical equipments[D]. Beijing: University of Science and Technology Beijing, 2016 (in Chinese)
|
[63] |
JAVED K, GOURIVEAU R, ZERHOUNI N, et al. Enabling health monitoring approach based on vibration data for accurate prognostics[J]. IEEE Transactions on Industrial Electronics, 2015, 62(1): 647-656 doi: 10.1109/TIE.2014.2327917
|
[64] |
王翔, 胡学钢. 高维小样本分类问题中特征选择研究综述[J]. 计算机应用, 2017, 37(9): 2433-2438+2448 https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY201709001.htm
WANG X, HU X G. Overview on feature selection in high-dimensional and small-sample-size classification[J]. Journal of Computer Applications, 2017, 37(9): 2433-2438+2448 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY201709001.htm
|
[65] |
PENG Y F, CHENG J S, LIU Y F, et al. An adaptive data-driven method for accurate prediction of remaining useful life of rolling bearings[J]. Frontiers of Mechanical Engineering, 2018, 13(2): 301-310 doi: 10.1007/s11465-017-0449-7
|
[66] |
WANG Z P, LU C, WANG Z L, et al. Fault diagnosis and health assessment for bearings using the Mahalanobis-Taguchi system based on EMD-SVD[J]. Transactions of the Institute of Measurement and Control, 2013, 35(6): 798-807 doi: 10.1177/0142331212472929
|
[67] |
陈俊洵. 基于EEMD-马田系统的机械设备关键部件的健康管理研究[D]. 南京: 南京理工大学, 2018
CHEN J X. Research on health management for critical component of mechanical equipment based on EEMD-Mahalanobis Taguchi system[D]. Nanjing: Nanjing University of Science & Technology, 2018 (in Chinese)
|
[68] |
WU J, WU C Y, CAO S, et al. Degradation data-driven time-to-failure prognostics approach for rolling element bearings in electrical machines[J]. IEEE Transactions on Industrial Electronics, 2019, 66(1): 529-539 doi: 10.1109/TIE.2018.2811366
|
[69] |
柏林, 闫康, 刘小峰. 基于状态追踪特征相空间重构的轴承寿命预测方法[J]. 振动与冲击, 2019, 38(23): 119-125 https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201923018.htm
BO L, YAN K, LIU X F. Bearing life prediction method based on phase space reconstruction of state tracking features[J]. Journal of Vibration and Shock, 2019, 38(23): 119-125 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201923018.htm
|
[70] |
QIU G Q, GU Y K, CHEN J J. Selective health indicator for bearings ensemble remaining useful life prediction with genetic algorithm and Weibull proportional hazards model[J]. Measurement, 2020, 150: 107097 doi: 10.1016/j.measurement.2019.107097
|
[71] |
JOLLIFFE I T, CADIMA J. Principal component analysis: a review and recent developments[J]. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2016, 374(2065): 20150202 doi: 10.1098/rsta.2015.0202
|
[72] |
DONG S J, LUO T H. Bearing degradation process prediction based on the PCA and optimized LS-SVM model[J]. Measurement, 2013, 46(9): 3143-3152 doi: 10.1016/j.measurement.2013.06.038
|
[73] |
WANG H, NI G X, CHEN J H, et al. Research on rolling bearing state health monitoring and life prediction based on PCA and Internet of things with multi-sensor[J]. Measurement, 2020, 157: 107657 doi: 10.1016/j.measurement.2020.107657
|
[74] |
康守强, 叶立强, 王玉静, 等. 基于MCEA-KPCA和组合SVR的滚动轴承剩余使用寿命预测[J]. 电子测量与仪器学报, 2017, 31(9): 1365-1371 https://www.cnki.com.cn/Article/CJFDTOTAL-DZIY201709003.htm
KANG S Q, YE L Q, WANG Y J, et al. Remaining useful life prediction of rolling bearing based on MCEA-KPCA and combined SVR[J]. Journal of Electronic Measurement and Instrumentation, 2017, 31(9): 1365-1371 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DZIY201709003.htm
|
[75] |
王奉涛, 陈旭涛, 柳晨曦, 等. 基于KPCA和WPHM的滚动轴承可靠性评估与寿命预测[J]. 振动、测试与诊断, 2017, 37(3): 476-483 https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCS201703011.htm
WANG F T, CHEN X T, LIU C X, et al. Rolling bearing reliability assessment and life prediction based on KPCA and WPHM[J]. Journal of Vibration, Measurement & Diagnosis, 2017, 37(3): 476-483 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCS201703011.htm
|
[76] |
程晓涵, 汪爱明苏一新, 等. 投影寻踪方法在设备预知维护中的应用研究[J]. 振动工程学报, 2016, 29(4): 631-637 https://www.cnki.com.cn/Article/CJFDTOTAL-ZDGC201604010.htm
CHENG X H, WANG A M, SU Y X, et al. Study on application of projection pursuit method in predictive maintenance for equipments[J]. Journal of Vibration Engineering, 2016, 29(4): 631-637 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDGC201604010.htm
|
[77] |
LIU Y B, HE B, LIU F, et al. Remaining useful life prediction of rolling bearings using PSR, JADE, and extreme learning machine[J]. Mathematical Problems in Engineering, 2016, 2016: 8623530
|
[78] |
张田昊. 数据降维算法研究及其应用[D]. 上海: 上海交通大学, 2008
ZHANG T H. Research on dimensionality reduction algorithms and its applications[D]. Shanghai: Shanghai Jiao Tong University, 2008 (in Chinese)
|
[79] |
魏艳涛. 基于流形学习的数据降维方法研究[D]. 武汉: 华中科技大学, 2008
WEI Y T. Data dimension reduction method study based on manifold learning[D]. Wuhan: Huazhong University of Science and Technology, 2008 (in Chinese)
|
[80] |
HE X F, NIYOGI P. Locality preserving projections[C]// Proceedings of the 16th International Conference on Neural Information Processing Systems. Whistler, British Columbia, Canada: MIT Press, 2003: 153-160
|
[81] |
YU J B. Bearing performance degradation assessment using locality preserving projections[J]. Expert Systems with Applications, 2011, 38(6): 7440-7450
|
[82] |
CAI D, HE X F, HAN J W, et al. Orthogonal Laplacianfaces for face recognition[J]. IEEE Transactions on Image Processing, 2006, 15(11): 3608-3614
|
[83] |
TANG B P, LI F, QIN Y. Fault diagnosis model based on feature compression with orthogonal locality preserving projection[J]. Chinese Journal of Mechanical Engineering, 2011, 24(5): 891-898
|
[84] |
LI W, QIU M Q, ZHU Z C, et al. Bearing fault diagnosis based on spectrum images of vibration signals[J]. Measurement Science and Technology, 2016, 27(3): 035005
|
[85] |
陈守海. 基于流形学习的滚动轴承早期故障识别方法研究[D]. 大连: 大连理工大学, 2014
CHEN S H. The research of identification method for rolling bearing early fault based on manifold[D]. Dalian: Dalian University of Technology, 2014 (in Chinese)
|
[86] |
蔡蕾, 朱永生. 基于稀疏性非负矩阵分解和支持向量机的时频图像识别[J]. 自动化学报, 2009, 35(10): 1272-1277 https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO200910004.htm
CAI L, ZHU Y S. Time-frequency spectra recognition based on sparse non-negative matrix factorization and support vector machine[J]. Acta Automatica Sinica, 2009, 35(10): 1272-1277 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO200910004.htm
|
[87] |
EL-THALJI I, JANTUNEN E. Dynamic modelling of wear evolution in rolling bearings[J]. Tribology International, 2015, 84: 90-99
|
[88] |
LEI Y G, LI N P, GUO L, et al. Machinery health prognostics: a systematic review from data acquisition to RUL prediction[J]. Mechanical Systems and Signal Processing, 2018, 104: 799-834
|
[89] |
CERRADA M, SÁNCHEZ R V, LI C, et al. A review on data-driven fault severity assessment in rolling bearings[J]. Mechanical Systems and Signal Processing, 2018, 99: 169-196
|
[90] |
何正嘉, 曹宏瑞, 訾艳阳, 等. 机械设备运行可靠性评估的发展与思考[J]. 机械工程学报, 2014, 50(2): 171-186 https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201402027.htm
HE Z J, CAO H R, ZI Y Y, et al. Developments and thoughts on operational reliability assessment of mechanical equipment[J]. Journal of Mechanical Engineering, 2014, 50(2): 171-186 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201402027.htm
|
[91] |
姜万录, 雷亚飞, 韩可, 等. 基于VMD和SVDD结合的滚动轴承性能退化程度定量评估[J]. 振动与冲击, 2018, 37(22): 43-50 https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201822007.htm
JIANG W L, LEI Y F, HAN K, et al. Performance degradation quantitative assessment method for rolling bearings based on VMD and SVDD[J]. Journal of Vibration and Shock, 2018, 37(22): 43-50 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201822007.htm
|
[92] |
TONG Q J, HU J Z, JIA M P, et al. Rolling bearing performance degradation evaluation by VMD and embedding selection-based NPE[J]. Journal of Southeast University (English Edition), 2019, 35(4): 408-416
|
[93] |
武千惠, 黄必清. 基于支持向量数据描述的剩余寿命预测方法[J]. 计算机集成制造系统, 2018, 24(11): 2725-2733 https://www.cnki.com.cn/Article/CJFDTOTAL-JSJJ201811007.htm
WU Q H, HUANG B Q. Remaining useful life prediction based on support vector data description[J]. Computer Integrated Manufacturing Systems, 2018, 24(11): 2725-2733 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSJJ201811007.htm
|
[94] |
ZENG M, YANG Y, LUO S, et al. One-class classification based on the convex hull for bearing fault detection[J]. Mechanical Systems and Signal Processing, 2016, 81: 274-293
|
[95] |
SHEN Z J, HE Z J, CHEN X F, et al. A monotonic degradation assessment index of rolling bearings using fuzzy support vector data description and running time[J]. Sensors, 2012, 12(8): 10109-10135
|
[96] |
WANG D, TSUI K L. Theoretical investigation of the upper and lower bounds of a generalized dimensionless bearing health indicator[J]. Mechanical Systems and Signal Processing, 2018, 98: 890-901
|
[97] |
RAI A, UPADHYAY S H. The use of MD-CUMSUM and NARX neural network for anticipating the remaining useful life of bearings[J]. Measurement, 2017, 111: 397-410
|
[98] |
HONG S, WANG B Q, LI G Q, et al. Performance degradation assessment for bearing based on ensemble empirical mode decomposition and Gaussian mixture model[J]. Journal of Vibration and Acoustics, 2014, 136(6): 061006
|
[99] |
ZHANG S, ZHANG Y X, ZHU D C. Residual life prediction for rolling element bearings based on an effective degradation indicator[J]. Journal of Failure Analysis and Prevention, 2015, 15(5): 722-729
|
[100] |
NI G X, CHEN J H, WANG H. Degradation assessment of rolling bearing towards safety based on random matrix single ring machine learning[J]. Safety Science, 2019, 118: 403-408
|
[101] |
YUE H H, QIN S J. Reconstruction-based fault identification using a combined index[J]. Industrial & Engineering Chemistry Research, 2001, 40(20): 4403-4414
|
[102] |
DUAN L X, ZHAO F, WANG J J, et al. An integrated cumulative transformation and feature fusion approach for bearing degradation prognostics[J]. Shock and Vibration, 2018, 2018: 9067184
|
[103] |
PENG Y F, LIU Y F, CHENG J S, et al. Remaining useful life prediction of rolling bearing using adaptive sparsest narrow-band decomposition and locality preserving projections[J]. Advances in Mechanical Engineering, 2019, 11(12): 1-13
|
[104] |
MA M, CHEN X F, ZHANG X L, et al. Locally linear embedding on Grassmann manifold for performance degradation assessment of bearings[J]. IEEE Transactions on Reliability, 2017, 66(2): 467-477 doi: 10.1109/TR.2017.2691730
|
[105] |
HUANG R Q, XI L F, LI X L, et al. Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods[J]. Mechanical Systems and Signal Processing, 2007, 21(1): 193-207 doi: 10.1016/j.ymssp.2005.11.008
|
[106] |
刘美芳, 余建波, 尹纪庭. 基于贝叶斯推论和自组织映射的轴承性能退化评估方法[J]. 计算机集成制造系统, 2012, 18(10): 2237-2244 https://www.cnki.com.cn/Article/CJFDTOTAL-JSJJ201210018.htm
LIU M F, YU J B, YIN J T. Bearing performance degradation assessment based on Bayesian inference and self-organizing map[J]. Computer Integrated Manufacturing Systems, 2012, 18(10): 2237-2244 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSJJ201210018.htm
|
[107] |
HONG S, ZHOU Z, ZIO E, et al. Condition assessment for the performance degradation of bearing based on a combinatorial feature extraction method[J]. Digital Signal Processing, 2014, 27: 159-166 doi: 10.1016/j.dsp.2013.12.010
|
[108] |
ZHAO L, WANG X. A deep feature optimization fusion method for extracting bearing degradation features[J]. IEEE Access, 2018, 6: 19640-19653 doi: 10.1109/ACCESS.2018.2824352
|
[109] |
GUO L, LI N P, JIA F, et al. A recurrent neural network based health indicator for remaining useful life prediction of bearings[J]. Neurocomputing, 2017, 240: 98-109 doi: 10.1016/j.neucom.2017.02.045
|
[110] |
PAN Y N, CHEN J, LI X L. Bearing performance degradation assessment based on lifting wavelet packet decomposition and fuzzy c-means[J]. Mechanical Systems and Signal Processing, 2010, 24(2): 559-566 doi: 10.1016/j.ymssp.2009.07.012
|
[111] |
RAI A, UPADHYAY S H. Bearing performance degradation assessment based on a combination of empirical mode decomposition and k-medoids clustering[J]. Mechanical Systems and Signal Processing, 2017, 93: 16-29 doi: 10.1016/j.ymssp.2017.02.003
|
[112] |
LI Y L, LI H R, WANG B, et al. Rolling element bearing performance degradation assessment using variational mode decomposition and Gath-Geva clustering time series segmentation[J]. International Journal of Rotating Machinery, 2017, 2017: 2598169
|
[113] |
LU Y F, XIE R, LIANG S Y. CEEMD-assisted bearing degradation assessment using tight clustering[J]. The International Journal of Advanced Manufacturing Technology, 2019, 104(1-4): 1259-1267 doi: 10.1007/s00170-019-04078-2
|
[114] |
黎慧. 基于EMD和逻辑回归的轴承性能退化评估与剩余寿命预测[D]. 南昌: 华东交通大学, 2017
LI H. Bearing performance degradation assessment and residual life prediction based on EMD and logistic regression[D]. Nanchang: East China Jiaotong University, 2017 (in Chinese)
|
[115] |
DAO P B, STASZEWSKI W J, BARSZCZ T, et al. Condition monitoring and fault detection in wind turbines based on cointegration analysis of SCADA data[J]. Renewable Energy, 2018, 116: 107-122 doi: 10.1016/j.renene.2017.06.089
|
[116] |
LI H R, LI Y L, YU H. A novel health indicator based on cointegration for rolling bearings' run-to-failure process[J]. Sensors, 2019, 19(9): 2151 doi: 10.3390/s19092151
|
[117] |
JARDINE A K S, LIN D M, BANJEVIC D, et al. A review on machinery diagnostics and prognostics implementing condition-based maintenance[J]. Mechanical Systems and Signal Processing, 2006, 20(7): 1483-1510 doi: 10.1016/j.ymssp.2005.09.012
|
[118] |
ZIO E, DI MAIO F. A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system[J]. Reliability Engineering & System Safety, 2010, 95(1): 49-57
|
[119] |
SI X S, WANG W B, HU C H, et al. Remaining useful life estimation-a review on the statistical data driven approaches[J]. European Journal of Operational Research, 2011, 213(1): 1-14 doi: 10.1016/j.ejor.2010.11.018
|
[120] |
TOBON-MEJIA D A, MEDJAHER K, ZERHOUNI N, et al. A data-driven failure prognostics method based on mixture of Gaussians hidden Markov models[J]. IEEE Transactions on Reliability, 2012, 61(2): 491-503 doi: 10.1109/TR.2012.2194177
|
[121] |
ISO. ISO13381-1: 2015 Condition monitoring and diagnostics of machines-prognostics-part 1: general guidelines[S]. Geneva: ISO, 2015
|
[122] |
LI N P, LEI Y C, LIN J, et al. An improved exponential model for predicting remaining useful life of rolling element bearings[J]. IEEE Transactions on Industrial Electronics, 2015, 62(12): 7762-7773 doi: 10.1109/TIE.2015.2455055
|
[123] |
HINES J W, USYNIN A. Current computational trends in equipment prognostics[J]. International Journal of Computational Intelligence Systems, 2008, 1(1): 94-102
|
[124] |
HENG A, ZHANG S, TAN A C C, et al. Rotating machinery prognostics: state of the art, challenges and opportunities[J]. Mechanical Systems and Signal Processing, 2009, 23(3): 724-739 doi: 10.1016/j.ymssp.2008.06.009
|
[125] |
PENG Y, DONG M, ZUO M J. Current status of machine prognostics in condition-based maintenance: a review[J]. The International Journal of Advanced Manufacturing Technology, 2010, 50(1-4): 297-313 doi: 10.1007/s00170-009-2482-0
|
[126] |
张小丽, 陈雪峰, 李兵, 等. 机械重大装备寿命预测综述[J]. 机械工程学报, 2011, 47(11): 100-116
ZHANG X L, CHEN X F, LI B, et al. Review of life prediction for mechanical major equipments[J]. Journal 0f Mechanical Engineering, 2011, 47(11): 100-116 (in Chinese)
|
[127] |
张仕新, 昝翔, 李浩, 等. 状态维修理论及剩余寿命预测的研究现状与展望[J]. 兵工自动化, 2014, 33(9): 15-20 https://www.cnki.com.cn/Article/CJFDTOTAL-BGZD201409005.htm
ZHANG S X, ZAN X, LI H, et al. Condition-based maintenance theory and research status and prospect about prediction of residual useful life[J]. Ordnance Industry Automation, 2014, 33(9): 15-20 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-BGZD201409005.htm
|
[128] |
LIAO L X, KÖTTIG F. Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction[J]. IEEE Transactions on Reliability, 2014, 63(1): 191-207 doi: 10.1109/TR.2014.2299152
|
[129] |
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, 60-61: 252-272 doi: 10.1016/j.ymssp.2015.02.008
|
[130] |
KAN M S, TAN A C C, MATHEW J, et al. A review on prognostic techniques for non-stationary and non-linear rotating systems[J]. Mechanical Systems and Signal Processing, 2015, 62-63: 1-20 doi: 10.1016/j.ymssp.2015.02.016
|
[131] |
中华人民共和国国家质量监督检验检疫总局, 国家标准化管理委员会. GB/T 6391-2010滚动轴承额定动载荷和额定寿命[S]. 北京: 中国标准出版社, 2011
General Administration of Quality Supervision、Inspection and Quarantine of the People's Republic of China, Standardization Administration. GB/T 6391-2010 Rolling bearings-dynamic load ratings and rating life[S]. Beijing: Standards Press of China, 2011 (in Chinese)
|
[132] |
ZARETSKY E V. Rolling bearing life prediction, theory, and application[R]. Washington: NASA, 2013
|
[133] |
HENG A, TAN A C C, MATHEW J, et al. Intelligent condition-based prediction of machinery reliability[J]. Mechanical Systems and Signal Processing, 2009, 23(5): 1600-1614 doi: 10.1016/j.ymssp.2008.12.006
|
[134] |
FENG Y, HUANG X D, CHEN J, et al. Reliability-based residual life prediction of large-size low-speed slewing bearings[J]. Mechanism and Machine Theory, 2014, 81: 94-106 doi: 10.1016/j.mechmachtheory.2014.06.013
|
[135] |
李永华, 智鹏鹏, 陈秉智. 动车组轴箱轴承模糊可靠性寿命评估[J]. 大连交通大学学报, 2017, 38(4): 104-109+115 doi: 10.13291/j.cnki.djdxac.2017.04.021
LI Y H, ZHI P P, CHEN B Z. Fuzzy reliability of life evaluation of EMU axle box bearing[J]. Journal of Dalian Jiaotong University, 2017, 38(4): 104-109+115 (in Chinese) doi: 10.13291/j.cnki.djdxac.2017.04.021
|
[136] |
覃楚东, 贺石中, 庞晋山, 等. 基于有限元分析和油液监测的轴承疲劳磨损寿命研究[J]. 润滑与密封, 2018, 43(7): 121-125+156 doi: 10.3969/j.issn.0254-0150.2018.07.022
QIN C D, HE S Z, PANG J S, et al. Research of fatigue wear life of bearing based on finite element analysis and oil monitoring[J]. Lubrication Engineering, 2018, 43(7): 121-125+156 (in Chinese) doi: 10.3969/j.issn.0254-0150.2018.07.022
|
[137] |
CUBILLO A, PERINPANAYAGAM S, ESPERON- MIGUEZ M. A review of physics-based models in prognostics: application to gears and bearings of rotating machinery[J]. Advances in Mechanical Engineering, 2016, 8(8): 1-21
|
[138] |
LU Y F, LI Q, LIANG S Y. Physics-based intelligent prognosis for rolling bearing with fault feature extraction[J]. The International Journal of Advanced Manufacturing Technology, 2018, 97(1-4): 611-620 doi: 10.1007/s00170-018-1959-0
|
[139] |
王亮, 吕卫民, 滕克难, 等. 基于数据驱动的装备故障预测技术研究[J]. 计算机测量与控制, 2013, 21(8): 2087-2089+2105 doi: 10.3969/j.issn.1671-4598.2013.08.020
WANG L, LYU W M, TENG K N, et al. Research on data-driven fault prediction technologies for equipments[J]. Computer Measurement & Control, 2013, 21(8): 2087-2089+2105 (in Chinese) doi: 10.3969/j.issn.1671-4598.2013.08.020
|
[140] |
喻勇, 司小胜, 胡昌华, 等. 数据驱动的可靠性评估与寿命预测研究进展: 基于协变量的方法[J]. 自动化学报, 2018, 44(2): 216-227 https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201802002.htm
YU Y, SI X S, HU C H, et al. Data driven reliability assessment and life-time prognostics: a review on covariate models[J]. Acta Automatica Sinica, 2018, 44(2): 216-227 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201802002.htm
|
[141] |
裴洪, 胡昌华, 司小胜, 等. 基于机器学习的设备剩余寿命预测方法综述[J]. 机械工程学报, 2019, 55(8): 1-13 https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201908001.htm
PEI H, HU C H, SI X S, et al. Review of machine learning based remaining useful life prediction methods for equipment[J]. Journal of Mechanical Engineering, 2019, 55(8): 1-13 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201908001.htm
|
[142] |
郭一帆, 唐家银. 基于机器学习算法的寿命预测与故障诊断技术的发展综述[J]. 计算机测量与控制, 2019, 27(3): 7-13 https://www.cnki.com.cn/Article/CJFDTOTAL-JZCK201903002.htm
GUO Y F, TANG J Y. A review of the development of life prediction and fault diagnosis technology based on machine learning algorithm[J]. Computer Measurement & Control, 2019, 27(3): 7-13 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JZCK201903002.htm
|
[143] |
孙强, 岳继光. 基于不确定性的故障预测方法综述[J]. 控制与决策, 2014, 29(5): 769-778 https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC201405001.htm
SUN Q, YUE J G. Review on fault prognostic methods based on uncertainty[J]. Control and Decision, 2014, 29(5): 769-778 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC201405001.htm
|
[144] |
JANG J S R. ANFIS: adaptive-network-based fuzzy inference system[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1993, 23(3): 665-685 doi: 10.1109/21.256541
|
[145] |
CHEN C C, VACHTSEVANOS G, ORCHARD M E. Machine remaining useful life prediction: an integrated adaptive neuro-fuzzy and high-order particle filtering approach[J]. Mechanical Systems and Signal Processing, 2012, 28: 597-607 doi: 10.1016/j.ymssp.2011.10.009
|
[146] |
AKUSOK A, BJORK K M, MICHE Y, et al. High performance extreme learning machines: a complete toolbox for big data applications[J]. IEEE Access, 2015, 3: 1011-1025 doi: 10.1109/ACCESS.2015.2450498
|
[147] |
杜占龙, 李小民, 席雷平, 等. 多分类概率极限学习机及其在剩余使用寿命预测中的应用[J]. 系统工程与电子技术, 2015, 37(12): 2777-2784 doi: 10.3969/j.issn.1001-506X.2015.12.18
DU Z L, LI X M, XI L P, et al. Multi-class probabilistic extreme learning machine and its application in remaining useful life prediction[J]. Systems Engineering and Electronics, 2015, 37(12): 2777-2784 (in Chinese) doi: 10.3969/j.issn.1001-506X.2015.12.18
|
[148] |
HUANG H Z, WANG H K, LI Y F, et al. Support vector machine based estimation of remaining useful life: current research status and future trends[J]. Journal of Mechanical Science and Technology, 2015, 29(1): 151-163 doi: 10.1007/s12206-014-1222-z
|
[149] |
SCHMIDHUBER J. Deep learning in neural networks: an overview[J]. Neural Networks, 2015, 61: 85-117 doi: 10.1016/j.neunet.2014.09.003
|
[150] |
KHAN S, YAIRI T. A review on the application of deep learning in system health management[J]. Mechanical Systems and Signal Processing, 2018, 107: 241-265 doi: 10.1016/j.ymssp.2017.11.024
|
[151] |
ZHANG L W, LIN J, LIU B, et al. A review on deep learning applications in prognostics and health management[J]. IEEE Access, 2019, 7: 162415-162438 doi: 10.1109/ACCESS.2019.2950985
|
[152] |
陈志强, 陈旭东, DE OLIVIRA J V, 等. 深度学习在设备故障预测与健康管理中的应用[J]. 仪器仪表学报, 2019, 40(9): 206-226 https://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201909022.htm
CHEN Z Q, CHEN X D, DE OLIVIRA J V, et al. Application of deep learning in equipment prognostics and health management[J]. Chinese Journal of Scientific Instrument, 2019, 40(9): 206-226 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201909022.htm
|
[153] |
REZAEIANJOUYBARI B, SHANG Y. Deep learning for prognostics and health management: state of the art, challenges, and opportunities[J]. Measurement, 2020, 163: 107929 doi: 10.1016/j.measurement.2020.107929
|
[154] |
LIN Y H, LI X D, HU Y. Deep diagnostics and prognostics: an integrated hierarchical learning framework in PHM applications[J]. Applied Soft Computing, 2018, 72: 555-564 doi: 10.1016/j.asoc.2018.01.036
|
[155] |
SANKARARAMAN S, GOEBEL K. Why is the remaining useful life prediction uncertain?[C]//Proceedings of Annual Conference of the Prognostics and Health Management Society 2013. New Orleans: PHM, 2013: 1-13
|
[156] |
SANKARARAMAN S. Significance, interpretation, and quantification of uncertainty in prognostics and remaining useful life prediction[J]. Mechanical Systems and Signal Processing, 2015, 52-53: 228-247 doi: 10.1016/j.ymssp.2014.05.029
|
[157] |
VAPNIK V N. The nature of statistical learning theory[M]. New York: Springer, 1995
|
[158] |
SUYKENS J A K, VANDEWALLE J. Least squares support vector machine classifiers[J]. Neural Processing Letters, 1999, 9(3): 293-300 doi: 10.1023/A:1018628609742
|
[159] |
TIPPING M E. The relevance vector machine[C]// Proceedings of the 12th International Conference on Neural Information Processing Systems. Denver, USA: MIT Press, 1999: 652-658
|
[160] |
何志昆, 刘光斌, 赵曦晶, 等. 高斯过程回归方法综述[J]. 控制与决策, 2013, 28(8): 1121-1129+1137 https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC201308002.htm
HE Z K, LIU G B, ZHAO X J, et al. Overview of Gaussian process regression[J]. Control and Decision, 2013, 28(8): 1121-1129+1137 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC201308002.htm
|
[161] |
雷亚国, 陈吴, 李乃鹏, 等. 自适应多核组合相关向量机预测方法及其在机械设备剩余寿命预测中的应用[J]. 机械工程学报, 2016, 52(1): 87-93 https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201601011.htm
LEI Y G, CHEN W, LI N P, et al. A relevance vector machine prediction method based on adaptive multi-kernel combination and its application to remaining useful life prediction of machinery[J]. Journal of Mechanical Engineering, 2016, 52(1): 87-93 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201601011.htm
|
[162] |
WILSON A G, ADAMS R P. Gaussian process kernels for pattern discovery and extrapolation[C]//Proceedings of the 30th International Conference on International Conference on Machine Learning-Volume 28. Atlanta, USA: JMLR. org, 2013: 1067-1075
|
[163] |
LI C, DE OLIVEIRA J V, CERRADA M, et al. A systematic review of fuzzy formalisms for bearing fault diagnosis[J]. IEEE Transactions on Fuzzy Systems, 2019, 27(7): 1362-1382 doi: 10.1109/TFUZZ.2018.2878200
|
[164] |
TANGKUMAN S, YANG B S. Application of grey model for machine degradation prognostics[J]. Journal of Mechanical Science and Technology, 2011, 25(12): 2979-2985 doi: 10.1007/s12206-011-0902-1
|
[165] |
AN D, KIM N H, CHOI J H. Practical options for selecting data-driven or physics-based prognostics algorithms with reviews[J]. Reliability Engineering & System Safety, 2015, 133: 223-236
|
[166] |
ZHANG X H, XIAO L, KANG J S. Degradation prediction model based on a neural network with dynamic windows[J]. Sensors, 2015, 15(3): 6996-7015 doi: 10.3390/s150306996
|
[167] |
WANG B, LEI Y G, LI N P, 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
|
[168] |
ZHANG B, ZHANG S H, LI W H. Bearing performance degradation assessment using long short-term memory recurrent network[J]. Computers in Industry, 2019, 106: 14-29 doi: 10.1016/j.compind.2018.12.016
|
[169] |
HE M F, ZHOU Y G, LI Y, et al. Long short-term memory network with multi-resolution singular value decomposition for prediction of bearing performance degradation[J]. Measurement, 2020, 156: 107582 doi: 10.1016/j.measurement.2020.107582
|
[170] |
LI X Q, JIANG H K, XIONG X, et al. Rolling bearing health prognosis using a modified health index based hierarchical gated recurrent unit network[J]. Mechanism and Machine Theory, 2019, 133: 229-249 doi: 10.1016/j.mechmachtheory.2018.11.005
|
[171] |
REN L, CHENG X J, WANG X K, et al. Multi-scale dense gate recurrent unit networks for bearing remaining useful life prediction[J]. Future Generation Computer Systems, 2019, 94: 601-609 doi: 10.1016/j.future.2018.12.009
|
[172] |
SHE D M, JIA M P. A BiGRU method for remaining useful life prediction of machinery[J]. Measurement, 2021, 167: 108277 doi: 10.1016/j.measurement.2020.108277
|
[173] |
CHEN Y H, PENG G L, ZHU Z Y, et al. A novel deep learning method based on attention mechanism for bearing remaining useful life prediction[J]. Applied Soft Computing, 2020, 86: 105919 doi: 10.1016/j.asoc.2019.105919
|
[174] |
CHEN Y, LI F, WANG J X, et al. Quantum recurrent encoder-decoder neural network for performance trend prediction of rotating machinery[J]. Knowledge-Based Systems, 2020, 197: 105863 doi: 10.1016/j.knosys.2020.105863
|
[175] |
于重重, 宁亚倩, 秦勇, 等. 基于T-SNE样本熵和TCN的滚动轴承状态退化趋势预测[J]. 仪器仪表学报, 2019, 40(8): 39-46 https://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201908005.htm
YU C C, NING Y Q, QIN Y, et al. Prediction of rolling bearing state degradation trend based on T-SNE sample entropy and TCN[J]. Chinese Journal of Scientific Instrument, 2019, 40(8): 39-46 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201908005.htm
|
[176] |
YAN M M, WANG X G, WANG B X, et al. Bearing remaining useful life prediction using support vector machine and hybrid degradation tracking model[J]. ISA Transactions, 2020, 98: 471-482 doi: 10.1016/j.isatra.2019.08.058
|
[177] |
冯鹏飞, 朱永生, 王培功, 等. 基于相关向量机模型的设备运行可靠性预测[J]. 振动与冲击, 2017, 36(12): 146-149+180 https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201712024.htm
FENG P F, ZHU Y S, WANG P G, et al. Operational reliability prediction of equipment based on relevance vector machine[J]. Journal of Vibration and Shock, 2017, 36(12): 146-149+180 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201712024.htm
|
[178] |
HONG S, ZHOU Z, LU C, et al. Bearing remaining life prediction using Gaussian process regression with composite kernel functions[J]. Journal of Vibroengineering, 2015, 17(2): 695-704
|
[179] |
WANG Y X, LI H X, YANG J W, et al. Sparse coding based RUL prediction and its application on roller bearing prognostics[J]. Journal of Intelligent & Fuzzy Systems, 2018, 34(6): 3719-3733
|
[180] |
李洪儒, 王余奎, 王冰, 等. 面向广义数学形态颗粒特征的灰色马尔科夫剩余寿命预测方法[J]. 振动工程学报, 2015, 28(2): 316-323 https://www.cnki.com.cn/Article/CJFDTOTAL-ZDGC201502019.htm
LI H R, WANG Y K, WANG B, et al. The method of grey Markov remaining service life prediction specific to generalized mathematical morphological particle[J]. Journal of Vibration Engineering, 2015, 28(2): 316-323 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDGC201502019.htm
|
[181] |
ZHANG Z X, SI X S, HUA C H, et al. Degradation data analysis and remaining useful life estimation: a review on Wiener-process-based methods[J]. European Journal of Operational Research, 2018, 271(3): 775-796 doi: 10.1016/j.ejor.2018.02.033
|
[182] |
HU Y G, LI H, SHI P P, et al. A prediction method for the real-time remaining useful life of wind turbine bearings based on the Wiener process[J]. Renewable Energy, 2018, 127: 452-460 doi: 10.1016/j.renene.2018.04.033
|
[183] |
WEN J, GAO H L, ZHANG J Q. Bearing remaining useful life prediction based on a nonlinear Wiener process model[J]. Shock and Vibration, 2018, 2018: 4068431
|
[184] |
WANG Y, PENG Y Z, ZI Y Y, et al. A two-stage data-driven-based prognostic approach for bearing degradation problem[J]. IEEE Transactions on Industrial Informatics, 2016, 12(3): 924-932 doi: 10.1109/TII.2016.2535368
|
[185] |
金晓航, 李建华, 孙毅. 基于二元维纳过程的轴承剩余寿命预测[J]. 仪器仪表学报, 2018, 39(6): 89-95 https://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201806012.htm
JIN X H, LI J H, SUN Y. Bearing remaining useful life prediction based on two-dimensional wiener process[J]. Chinese Journal of Scientific Instrument, 2018, 39(6): 89-95 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201806012.htm
|
[186] |
SONG W Q, LI M, LIANG J K. Prediction of bearing fault using fractional Brownian motion and minimum entropy deconvolution[J]. Entropy, 2016, 18(11): 418 doi: 10.3390/e18110418
|
[187] |
LI Q, LIANG S Y. Degradation trend prognostics for rolling bearing using improved R/S statistic model and fractional Brownian motion approach[J]. IEEE Access, 2017, 6: 21103-21114
|
[188] |
ZHANG H W, ZHOU D H, CHEN M Y, et al. Predicting remaining useful life based on a generalized degradation with fractional Brownian motion[J]. Mechanical Systems and Signal Processing, 2019, 115: 736-752 doi: 10.1016/j.ymssp.2018.06.029
|
[189] |
YOU M Y, MENG G. A framework of similarity-based residual life prediction approaches using degradation histories with failure, preventive maintenance, and suspension events[J]. IEEE Transactions on Reliability, 2013, 62(1): 127-135 doi: 10.1109/TR.2013.2241203
|
[190] |
WANG H, CHEN J H, QU J M, et al. A new approach for safety life prediction of industrial rolling bearing based on state recognition and similarity analysis[J]. Safety Science, 2020, 122: 104530 doi: 10.1016/j.ssci.2019.104530
|
[191] |
ZHANG L P, LU C, TAO L F. Curve similarity recognition based rolling bearing degradation state estimation and lifetime prediction[J]. Journal of Vibroengineering, 2016, 18(5): 2839-2854 doi: 10.21595/jve.2016.17377
|
[192] |
LI Y L, LI H R, WANG B, et al. Research on the feature selection of rolling bearings' degradation features[J]. Shock and Vibration, 2019, 2019: 6450719
|
[193] |
孟文俊, 张四聪, 淡紫嫣, 等. 滚动轴承寿命动态预测新方法[J]. 振动、测试与诊断, 2019, 39(3): 652-658 https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCS201903030.htm
MENG W J, ZHANG S C, DAN Z Y, et al. Method of dynamic life prediction of rolling bearing[J]. Journal of Vibration, Measurement & Diagnosis, 2019, 39(3): 652-658 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCS201903030.htm
|
[194] |
BEN ALI J, CHEBEL-MORELLO B, SAIDI L, et al. Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network[J]. Mechanical Systems and Signal Processing, 2015, 56-57: 150-172 doi: 10.1016/j.ymssp.2014.10.014
|
[195] |
REN L, SUN Y Q, WANG H, et al. Prediction of bearing remaining useful life with deep convolution neural network[J]. IEEE Access, 2018, 6: 13041-13049 doi: 10.1109/ACCESS.2018.2804930
|
[196] |
TAO L F, YANG C, CHENG Y J, et al. Machine component health prognostics with only truncated histories using geometrical metric approach[J]. Mechanical Systems and Signal Processing, 2018, 113: 168-179 doi: 10.1016/j.ymssp.2017.01.052
|
[197] |
张继冬, 邹益胜, 邓佳林, 等. 基于全卷积层神经网络的轴承剩余寿命预测[J]. 中国机械工程, 2019, 30(18): 2231-2235 https://www.cnki.com.cn/Article/CJFDTOTAL-ZGJX201918014.htm
ZHANG J D, ZOU Y S, DENG J L, et al. Bearing remaining life prediction based on full convolutional layer neural networks[J]. China Mechanical Engineering, 2019, 30(18): 2231-2235 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGJX201918014.htm
|
[198] |
KIM H E, TAN A C C, MATHEW J, et al. Bearing fault prognosis based on health state probability estimation[J]. Expert Systems with Applications, 2012, 39(5): 5200-5213 doi: 10.1016/j.eswa.2011.11.019
|
[199] |
LIU X J, SONG P, YANG C, et al. Prognostics and health management of bearings based on logarithmic linear recursive least-squares and recursive maximum likelihood estimation[J]. IEEE Transactions on Industrial Electronics, 2018, 65(2): 1549-1558 doi: 10.1109/TIE.2017.2733469
|
[200] |
LU Y F, LI Q, PAN Z P, et al. Prognosis of bearing degradation using gradient variable forgetting factor RLS combined with time series model[J]. IEEE Access, 2018, 6: 10986-10995 doi: 10.1109/ACCESS.2018.2805280
|
[201] |
ZHANG N N, WU L F, WANG Z H, et al. Bearing remaining useful life prediction based on Naive Bayes and Weibull distributions[J]. Entropy, 2018, 20(12): 944 doi: 10.3390/e20120944
|
[202] |
何兆民, 王少萍. 基于时变状态转移隐半马尔科夫模型的寿命预测[J]. 湖南大学学报(自然科学版), 2014, 41(8): 47-53 https://www.cnki.com.cn/Article/CJFDTOTAL-HNDX201408009.htm
HE Z M, WANG S P. Remaining lifetime prediction based on time-varying state transition probabilities of hidden semi-Markov model[J]. Journal of Hunan University (Natural Sciences), 2014, 41(8): 47-53 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HNDX201408009.htm
|
[203] |
张雨琦, 邹金慧, 马军. 多退化变量灰色预测模型的滚动轴承剩余寿命预测[J]. 探测与控制学报, 2019, 41(3): 112-120 https://www.cnki.com.cn/Article/CJFDTOTAL-XDYX201903021.htm
ZHAGN Y Q, ZOU J H, MA J. Rolling bearing residual life prediction based on grey prediction model with multiple degenerate variables[J]. Journal of Detection & Control, 2019, 41(3): 112-120 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XDYX201903021.htm
|
[204] |
LI Z X, WU D Z, HU C, et al. An ensemble learning-based prognostic approach with degradation-dependent weights for remaining useful life prediction[J]. Reliability Engineering & System Safety, 2019, 184: 110-122
|
[205] |
LEI Y G, LI N P, GONTARZ S, et al. A model-based method for remaining useful life prediction of machinery[J]. IEEE Transactions on Reliability, 2016, 65(3): 1314-1326
|
[206] |
马波, 翟斌, 彭琦, 等. 基于不同退化阶段状态空间模型及粒子滤波的滚动轴承寿命预测[J]. 北京化工大学学报(自然科学版), 2017, 44(3): 81-86 https://www.cnki.com.cn/Article/CJFDTOTAL-BJHY201703014.htm
MA B, ZHAI B, PENG Q, et al. Useful life prediction of rolling element bearings based on a particle filtering model and the state space model at different degradation stages[J]. Journal of Beijing University of Chemical Technology (Natural Science), 2017, 44(3): 81-86 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-BJHY201703014.htm
|
[207] |
LIM C K R, DAVID M. Switching Kalman filter for failure prognostic[J]. Mechanical Systems and Signal Processing, 2015, 52-53: 426-435
|
[208] |
QIAN Y N, YAN R Q, GAO R X. A multi-time scale approach to remaining useful life prediction in rolling bearing[J]. Mechanical Systems and Signal Processing, 2017, 83: 549-567
|
[209] |
BARALDI P, COMPARE M, SAUCO S, et al. Ensemble neural network-based particle filtering for prognostics[J]. Mechanical Systems and Signal Processing, 2013, 41(1-2): 288-300
|
[210] |
DEUTSCH J, HE M, HE D. Remaining useful life prediction of hybrid ceramic bearings using an integrated deep learning and particle filter approach[J]. Applied Sciences, 2017, 7(7): 649
|
[211] |
ELASHA F, SHANBR S, LI X C, et al. Prognosis of a wind turbine gearbox bearing using supervised machine learning[J]. Sensors, 2019, 19(14): 3092
|
[212] |
TRAN V T, PHAM H T, YANG B S, et al. Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine[J]. Mechanical Systems and Signal Processing, 2012, 32: 320-330
|
[213] |
MAIO F D, TSUI K L, ZIO E. Combining relevance vector machines and exponential regression for bearing residual life estimation[J]. Mechanical Systems and Signal Processing, 2012, 31: 405-427
|
[214] |
LI Q, LIANG S Y. Degradation trend prediction for rotating machinery using long-range dependence and particle filter approach[J]. Algorithms, 2018, 11(7): 89
|
[215] |
YU J B. Machine health prognostics using the Bayesian- inference-based probabilistic indication and high-order particle filtering framework[J]. Journal of Sound and Vibration, 2015, 358: 97-110
|
[216] |
XIAO Q L, FANG Y L, LIU Q, et al. Online machine health prognostics based on modified duration-dependent hidden semi-Markov model and high-order particle filtering[J]. The International Journal of Advanced Manufacturing Technology, 2018, 94(1-4): 1283-1297
|
[217] |
PAN S J, YANG Q. A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345-1359
|
[218] |
ZHANG B, WANG H, TANG Y, et al. Residual useful life prediction for slewing bearing based on similarity under different working conditions[J]. Experimental Techniques, 2018, 42(3): 279-289
|
[219] |
SHEN F, XU J W, SUN C, et al. Transfer between multiple working conditions: a new TCCHC-based exponential semi-deterministic extended Kalman filter for bearing remaining useful life prediction[J]. Measurement, 2019, 142: 148-162
|
[220] |
KUNDU P, DARPE A K, KULKARNI M S. Weibull accelerated failure time regression model for remaining useful life prediction of bearing working under multiple operating conditions[J]. Mechanical Systems and Signal Processing, 2019, 134: 106302
|
[221] |
ZHU J, CHEN N, SHEN C Q. A new data-driven transferable remaining useful life prediction approach for bearing under different working conditions[J]. Mechanical Systems and Signal Processing, 2020, 139: 106602
|
[222] |
TAN C Q, SUN F C, KONG T, et al. A survey on deep transfer learning[C]//Proceedings of the 27th International Conference on Artificial Neural Networks. Rhodes, Greece: Springer, 2018: 270-279
|
[223] |
CHENG H, KONG X G, CHEN G G, et al. Transferable convolutional neural network based remaining useful life prediction of bearing under multiple failure behaviors[J]. Measurement, 2021, 168: 108286
|
[224] |
MAO W T, HE J L, ZUO M J. Predicting remaining useful life of rolling bearings based on deep feature representation and transfer learning[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(4): 1594-1608
|
[225] |
陈佳鲜, 毛文涛, 刘京, 等. 基于深度时序特征迁移的轴承剩余寿命预测方法[J]. 控制与决策, 2021, 36(7): 1699-1706 https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC202107019.htm
CHEN J X, MAO W T, LIU J, et al. Remaining useful life prediction of bearing based on deep temporal feature transfer[J]. Control and Decision, 2021, 36(7): 1699-1706 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC202107019.htm
|
[226] |
单珊, 冯玉光, 奚文骏. PHM中预测性能评价方法的发展与展望[J]. 计算机测量与控制, 2015, 23(12): 3909-3912+3924 https://www.cnki.com.cn/Article/CJFDTOTAL-JZCK201512001.htm
SHAN S, FENG Y G, XI W J. Development and perspectives of prognostics performance evaluation in PHM[J]. Computer Measurement & Control, 2015, 23(12): 3909-3912+3924 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JZCK201512001.htm
|
[227] |
SAXENA A, CELAYA J, SAHA B, et al. Metrics for offline evaluation of prognostic performance[J]. International Journal of Prognostics and Health Management, 2010, 1(1): 1-20
|