1 |
韩淞宇, 邵海东, 姜洪开, 等. 基于提升卷积神经网络的航空发动机高速轴承智能故障诊断[J].航空学报, 2022, 43(9): 625479.
|
|
HAN S Y, SHAO H D, JIANG H K, et al. Intelligent fault diagnosis of aero-engine high-speed bearing using enhanced convolutional neural network[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(9): 625479 (in Chinese).
|
2 |
康玉祥, 陈果, 尉询楷, 等. 深度残差对冲网络及其在滚动轴承故障诊断中的应用[J].航空学报, 2022, 43(8): 625201.
|
|
KANG Y X, CHEN G, WEI X K, et al. Deep residual hedging network and its application in fault diagnosis of rolling bearings[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(8): 625201 (in Chinese).
|
3 |
ZHANG D C, STEWART E, ENTEZAMI M, et al. Intelligent acoustic-based fault diagnosis of roller bearings using a deep graph convolutional network[J]. Measurement, 2020, 156: 107585.
|
4 |
GLOWACZ A. Acoustic based fault diagnosis of three-phase induction motor[J]. Applied Acoustics, 2018, 137: 82-89.
|
5 |
LIU H M, LI L F, MA J. Rolling bearing fault diagnosis based on STFT-deep learning and sound signals[J]. Shock & Vibration, 2016, 6: 1-12.
|
6 |
PARVATHI S, HEMAMALINI S. Rational-dilation wavelet transform based torque estimation from acoustic signals for fault diagnosis in a three-phase induction motor[J]. IEEE Transactions on Industrial Informatics, 2019, 15(6): 3492-3501.
|
7 |
NGAIM J, PANG W, CHEN Z H, et al. Sparse filtering, International Conference on Neural Information Processing Systems[C]∥Curran Associates Incorporated, 2011: 1125-1133.
|
8 |
LEI Y G, JIA F, LIN J, et al. An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data[J]. IEEE Transactions on Industrial Electronics, 2016, 63(5): 3137-3147.
|
9 |
乔美英, 汤夏夏, 闫书豪, 等. 基于改进稀疏滤波与深度网络融合的轴承故障诊断[J]. 浙江大学学报(工学版), 2020, 54(12): 2301-2309, 2422.
|
|
QIAO M Y, TANG X X, YAN S H, et al. Bearing fault diagnosis based on improved sparse filter and deep network fusion[J]. Journal of Zhejiang University (Engineering Science), 2020, 54(12): 2301-2309, 2422 (in Chinese).
|
10 |
ZHANG Z Z, LI S M, LU J T, et al. A novel intelligent fault diagnosis method based on fast intrinsic component filtering and pseudo-normalization[J]. Mechanical Systems and Signal Processing, 2020, 145: 106923.
|
11 |
ZHANG Z Z, LI S M, AN Z H, et al. Fast convolution sparse filtering and its application on gearbox fault diagnosis[J]. Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering, 2020, 234(9): 1-14.
|
12 |
AN Z H, LI S M, WANG J R, et al. An intelligent fault diagnosis approach considering the elimination of the weight matrix multi-correlation[J]. Applied Sciences, 2018, 8(6): 906.
|
13 |
CHENG C, WANG W P, LIU H N, et al. Intelligent fault diagnosis using an unsupervised sparse feature learning method[J]. Measurement Science and Technology, 2020, 31(9): 095903.
|
14 |
NIALL H, SCOOT R. Comparing measures of sparsity[J]. IEEE Transactions on Information Theory, 2009, 55(10): 4723-4741.
|
15 |
JIA X D, ZHAO M, DI Y, et al. Sparse filtering with the generalized lp/lq norm and its applications to the condition monitoring of rotating machinery[J]. Mechanical Systems and Signal Processing, 2018, 102: 198-213.
|
16 |
CHANG D Q, SUN S L, ZHANG C S. An accelerated linearly convergent stochastic L-BFGS algorithm[J]. IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(11): 3338-3346.
|
17 |
ANTONI J. Cyclic spectral analysis of rolling-element bearing signals: facts and fictions[J]. Journal of Sound and Vibration, 2007, 304(3): 497-529.
|
18 |
邵海东, 张笑阳, 程军圣, 等. 基于提升深度迁移自动编码器的轴承智能故障诊断[J]. 机械工程学报, 2020, 56(9): 84-90.
|
|
SHAO H D, ZHANG X Y, CHENG J S, et al. Intelligent fault diagnosis of bearing using enhanced deep transfer auto-encoder[J]. Journal of Mechanical Engineering, 2020, 56(9): 84-90 (in Chinese).
|
19 |
HAN B K, ZHANG X, WANG J R, et al. Hybrid distance-guided adversarial network for intelligent fault diagnosis under different working conditions[J]. Measurement, 2021, 176: 109197.
|
20 |
MAATEN L, HINTON G. Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008, 9: 2579–2605.
|