[1] 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. LEI Yaguo,JIA Feng,KONG Detong,et al. Opportunities and challenges of machinery intelligent fault diagnosis in big data era[J]. Journal of Mechanical Engineering,2018,54(5):94-104. [2] 王国彪,何正嘉,陈雪峰,等. 机械故障诊断基础研究"何去何从"[J]. 机械工程学报,2013,49(1):63-72. WANG Guobiao,HE Zhengjia,CHEN Xuefeng,et al. Basic research on machinery fault diagnosis-what is the prescription[J]. Journal of Mechanical Engineering,2013,49(1):63-72. [3] BEN A,FNAIECH N,SAIDI L,et al. Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals[J]. Applied Acoustics,2015,89:16-27. [4] 李巍华,翁胜龙,张绍辉. 一种萤火虫神经网络及在轴承故障诊断中的应用[J]. 机械工程学报,2015,51(7):99-106. LI Weihua,WENG Shenglong,ZHANG Shaohui. A firefly neural network and its application in bearing fault diagnosis[J]. Journal of Mechanical Engineering,2015,51(7):99-106. [5] LIU Rongnan,YANG Boyuan,ZHANG Xiaoli,et al. Time-frequency atoms-driven support vector machine method for bearings incipient fault diagnosis[J]. Mechanical Systems and Signal Processing,2015,75:345-370. [6] HANG Jun,ZHANG Jianzhong,CHENG Ming. Application of multi-class fuzzy support vector machine classifier for fault diagnosis of wind turbine[J]. Fuzzy Sets and Systems,2016,297:128-140. [7] LEI Yaguo,ZUO M. Gear crack level identification based on weighted K nearest neighbor classification algorithm[J]. Mechanical Systems and Signal Processing,2009,23(5):1535-1547. [8] LIU Ruonan,YANG Boyuan,ZIO E,et al. Artificial intelligence for fault diagnosis of rotating machinery:a review[J]. Mechanical Systems and Signal Processing,2018,108(5):1535-1547. [9] 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,2015,51(21):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,2015,51(21):49-56. [10] DASGUPTA D,YU S,NINO F. Recent advances in artificial immune systems:Models and applications[J]. Applied Soft Computing,2011,11(2):1574-1587. [11] ZHENG Jieqiong,CHEN Yunfang,ZHANG Wei. A survey of artificial immune applications[J]. Artificial Intelligence Review,2010,34(1):19-34. [12] DASGUPTA D. Advances in artificial immune systems[J]. IEEE Computational Intelligence Magazine,2006,1(4):40-49. [13] BAYAR N,DARMOUL S,HAJRI-GABOUJ S,et al. Fault detection,diagnosis and recovery using artificial immune systems:a review[J]. Engineering Applications of Artificial Intelligence,2015,46:43-57. [14] ZHOU Ji,DASGUPTA D. Revisiting negative selection algorithms[J]. Evolutionary Computation,2007,15(2):223-251. [15] LI Dong,LIU Shulin,ZHANG Hongli. A method of anomaly detection and fault diagnosis with online adaptive learning under small training samples[J]. Pattern Recognition,2017,64:374-385. [16] LI Dong,LIU Shulin. Continual learning classification method for time-varying data based on artificial immune system[CP/OL].[2019-11-25]. https://doi.org/10.24433/CO.1791866.v2. [17] DUA D,GRAFF C. UCI machine learning repository[EB/OL].[2019-11-25]. http://archive.ics.uci.edu/ml. [18] KOTSIANTIS S,ZAHARAKIS I,PINTELAS P. Machine learning:A review of classification and combining techniques[J]. Artificial Intelligence Review,2006,26(3):159-190. [19] 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. 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. [20] WANG Biao,LEI Yaguo,LI Naipeng,et a1. A hybrid prognostics approach for estimating remaining useful life of rolling element bearings[J]. IEEE Transactions on Reliability,2018:l-12. |