[1] 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. [2] 贾继德,贾翔宇,梅检民,等. 基于小波与深度置信网络的柴油机失火故障诊断[J]. 汽车工程,2018,40(7):838-843. JIA Jide,JIA Xiangyu,MEI Jianmin,et al. Misfire fault diagnosis of diesel engine based on wavelet and deep belief network[J]. Automotive Engineering,2018,40(17):838-843. [3] 沈虹,赵红东,梅检民,等. 基于高阶累积量图像特征的柴油机故障诊断研究[J]. 振动与冲击,2015,34(11):133-138. SHEN Hong,ZHAO Hongdong,MEI Jianmin,et al. Diesel engine fault diagnosis based on high-order cumulant image features[J]. Journal of Vibration and Shock,2015,34(11):133-138. [4] 李巍华,张绍辉. 基于最近最远距离保持投影算法的发动机失火状态识别[J]. 机械工程学报,2015,51(20):156-163. LI Weihua,ZHANG Shaohui. Engine misfire condition recognition based on nearest and farthest distance preserving projection[J]. Journal of Mechanical Engineering,2015,51(20):156-163. [5] 牟伟杰,石林锁,蔡艳平,等. 基于振动时频图像全局和局部特征融合的柴油机故障诊断[J]. 振动与冲击,2018,37(10):14-19,49. MU Weijie,SHI Linsuo,CAI Yanping,et al. Diesel engine fault diagnosis based on the global and local features fusion of time-frequency image[J]. Journal of Vibration and Shock,2018,37(10):14-19,49. [6] AHMAD T A,ALIREZA M. Fault detection of injectors in diesel engines using vibration time-frequency analysis[J]. Applied Acoustics,2019,143:48-58. [7] 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. [8] 张西宁,向宙,夏心锐,等. 堆叠自编码网络性能优化及其在滚动轴承故障诊断中的应用[J]. 西安交通大学学报,2018,52(10):49-56,87. ZHANG Xining,XIANG Zhou,XIA Xinrui,et al. Optimization of stacking auto-encoder with applications in bearing fault diagnosis[J]. Journal of Xi'an Jiaotong University,2018,52(10):49-56,87. [9] HINTON G E,OSINDERO S,TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation,2006,18(7):1527-1554. [10] 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. [11] RANZATO M,BOUREAU Y L,LECUN Y. Sparse feature learning for deep belief networks[J]. Advances in Neural Information Processing Systems,2007,20:1185-1192. [12] SOHAIB M,KIM C H,KIM J M. A hybrid feature model and deep-learning-based bearing fault diagnosis[J]. Sensors,2017,17(12):2876. [13] SHAO Haidong,JIANG Hongkai,ZHAO Huiwei,et al. A novel deep autoencoder feature learning method for rotating machinery fault diagnosis[J]. Mechanical Systems and Signal Processing,2017,95:187-204. [14] 孙文珺,邵思羽,严如强. 基于稀疏自动编码深度神经网络的感应电动机故障诊断[J]. 机械工程学报,2016,52(9):65-71. SUN Wenjun,SHAO Siyu,YAN Ruqiang. Induction motor fault diagnosis based on deep neural network of sparse auto-encoder[J]. Journal of Mechanical Engineering. 2016,52(9):65-71. [15] HINTON G E,SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science,2006,313(5786):504-507. [16] SRIVASTAVA N,HINTON G E,KRIZHEVSKY A,et al. Dropout:A simple way to prevent neural networks from overfitting[J]. The Journal of Machine Learning Research,2014,15(1):1929-1958. [17] IOFFE S,SZEGEDY C. Batch normalization:Accelerating deep network training by reducing internal covariate shift[C]//International Conference on International Conference on Machine Learning. JMLR.org,2015:448-456. [18] GEEM Z W,KIM J H,LOGANATHAN G V. A new heuristic optimization algorithm:Harmony search[J]. Simula-tion,2001,76(2):60-68. [19] LAROCHELLE H,BENGIO Y,LOURADOUR J,et al. Exploring strategies for training deep neural networks[J]. Journal of Machine Learning Research,2009,10(1):1-40. |