[1] 曹宏瑞,景新,苏帅鸣,等. 中介轴承故障动力学建模与振动特征分析[J]. 机械工程学报,2020,56(21):89-99. CAO Hongrui,JING Xin,SU Shuaiming,et al. Dynamic modeling and vibration analysis for inter-shaft bearing fault[J]. Journal of Mechanical Engineering,2020,56(21):89-99. [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] 廖明夫,马振国,刘永泉,等. 航空发动机中介轴承的故障特征与诊断方法[J]. 航空动力学报,2013,28(12):2752-2758. LIAO Mingfu,MA Zhenguo,LIU Yongquan,et al. Fault characteristics and diagnosis method of intershaft bearing in aero-engine[J]. Journal of Aerospace Power,2013,28(12):2752-2758. [4] JIANG Zhinong,HU Minghui,FENG Kun,et al. Weak fault feature extraction scheme for intershaft bearings based on linear prediction and order tracking in the rotation speed difference domain[J]. Applied Sciences,2017,7(9):937. [5] FYFE K,MUNCK E. Analysis of computed order tracking[J]. Mechanical Systems and Signal Processing,1997,11(2):187-205. [6] 赵明,林京. 变转速下机械动态信息的自适应提取与状态评估[J]. 机械工程学报,2015,51(8):83. ZHAO Ming,LIN Jing. Adaptive extraction and state evaluation of mechanical dynamic information under variable speed[J]. Journal of Mechanical Engineering,2015,51(8):83. [7] 梁玉前,秦树人,郭瑜. 旋转机械升降速信号的瞬时频率估计[J]. 机械工程学报,2003(9):75-80. LIANG Yuqian,QIN Shuren,GUO Yu. Instantaneous frequency estimation of run-up or run-down sign of rotating machinery[J]. Journal of Mechanical Engineering,2003(9):75-80. [8] DAUBECHIES I,LU Jianfeng,WU H. Synchrosqueezed wavelet transforms:An empirical mode decomposition like tool[J]. Applied and Computational Harmonic Analysis,2011,30:243-261. [9] 陈雪峰,王诗彬,程礼. 航空发动机快变信号的匹配同步压缩变换研究[J]. 机械工程学报,2019,55(13):13-22. CHEN Xuefeng,WANG Shibin,CHENG Li. Matching synchrosqueezing transform for aero-engine's signals with fast varying instantaneous frequency[J]. Journal of Mechanical Engineering,2019,55(13):13-22. [10] MEIGNEN S,OBERLIN T,DEPALLE P,et al. Adaptive multimode signal reconstruction from time-frequency representations[J]. Philosophical Transactions,2016,374:2065. [11] YU Gang,YU Mingjin,XU Chuanyan. Synchroextracting Transform[J]. IEEE Transactions on Industrial Electronics,2017,64(10):8042-8054. [12] LI Zhen,GAO Jinghuai,LI Hui,et al. Synchroextracting transform:The theory analysis and comparisons with the synchrosqueezing transform[J]. Signal Processing,2020,166:107243. [13] LÜ Site,LÜ Yong,YUAN Rui,et al. High-order synchroextracting transform for characterizing signals with strong AM-FM features and its application in mechanical fault diagnosis[J]. Mechanical Systems and Signal Processing,2022,172:108959. [14] 张忠强,张新,王家序,等. 基于重加权谱峭度方法的航空发动机故障诊断[J]. 航空学报,2022,43(9):148-157. ZHANG Zhongqiang,ZHANG Xin,WANG Jiaxu,et al. Reweighted Kurtogram for aero-engine fault diagnosis[J]. Acta Aeronautica et Astronautica Sinica,2022,43(9):148-157. [15] ANTONI J. The spectral kurtosis:A useful tool for characterising non-stationary signals[J]. Mechanical Systems and Signal Processing,2004,20(2):282-307. [16] DWYER R. Detection of non-Gaussian signals by frequency domain Kurtosis estimation[C]//Acoustics,Speech,and Signal Processing,IEEE International Conference on ICASSP' 83. IEEE,1983. [17] ANTONI J. Fast computation of the kurtogram for the detection of transient faults[J]. Mechanical Systems and Signal Processing,2005,21(1):108-124. [18] ANTONI J. The infogram:Entropic evidence of the signature of repetitive transients[J]. Mechanical Systems and Signal Processing,2016,74:73-94. [19] WANG Dong. An extension of the infograms to novel Bayesian inference for bearing fault feature identification[J]. Mechanical Systems and Signal Processing,2016,80:19-30. [20] 王兴龙,郑近德,潘海洋,等. 基于MED与自相关谱峭度图的滚动轴承故障诊断方法[J]. 振动与冲击,2020,39(18):118-124,131. WANG Xinglong,ZHENG Jinde,PAN Haiyang,et al. Rolling bearing fault diagnosis method based on MED and autocorrelation spectral kurtosis[J]. Journal of Vibration and Shock,2020,39(18):118-124,131. [21] SAWALHI N,RANDALL R B,ENDO H. The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis[J]. Mechanical Systems and Signal Processing,2007,21(6):2616-2633. [22] 李宏坤,杨蕊,任远杰,等. 利用粒子滤波与谱峭度的滚动轴承故障诊断[J]. 机械工程学报,2017,53(3):63-72. LI Hongkun,YANG Rui,REN Yuanjie,et al. Rolling element bearing diagnosis using particle filter and kurtogram[J]. Journal of Mechanical Engineering,2017,53(3):63-72. [23] HE Ya,JIANG Zhinong,HU Minghui,et al. Local maximum synchrosqueezing chirplet transform:An effective tool for strongly non-stationary signals of gas turbine[J]. IEEE Transactions on Instrumentation and Measurement,2021,70:1-2. [24] ZHAO Ming,LIN Jing,MIAO Yonghao,et al. Detection and recovery of fault impulses via improved harmonic product spectrum and its application in defect size estimation of train bearings[J]. Measurement,2016,91:421-439. [25] LI Hua,WU Xing,LIU Tao,et al. Composite fault diagnosis for rolling bearing based on parameter-optimized VMD[J]. Measurement,2022,201:111637. |