1 |
侯东晓, 穆金涛, 方成, 等. 基于GADF与引入迁移学习的ResNet34对变速轴承的故障诊断[J]. 东北大学学报(自然科学版), 2022, 43(3): 383-389.
|
|
Hou Dong-xiao, Mu Jin-tao, Fang Cheng, et al. Fault diagnosis of variable speed bearings based on GADF and ResNet34 introduced transfer learning[J]. Journal of Northeastern University (Natural Science), 2022, 43(3): 383-389.
|
2 |
Yan R Q, Shang Z G, Xu H, et al. Wavelet transform for rotary machine fault diagnosis:10 years revisited[J]. Mechanical Systems and Signal Processing, 2023, 200(5): 110545.
|
3 |
Sun Y J, Li S H, Wang X H. Bearing fault diagnosis based on EMD and improved Chebyshev distance in SDP image[J]. Measurement, 2021, 176(17): 109100.
|
4 |
Ye M Y, Yan X A, Chen N, et al. Intelligent fault diagnosis of rolling bearing using variational mode extraction and improved one-dimensional convolutional neural network[J]. Applied Acoustics, 2023, 202: 109143.
|
5 |
Sun Y K, Cao Y, Li P. Fault diagnosis for train plug door using weighted fractional wavelet packet decomposition energy entropy[J]. Accident Analysis & Prevention, 2022, 166: 106549.
|
6 |
周奇才, 沈鹤鸿, 赵炯, 等. 基于改进堆叠式循环神经网络的轴承故障诊断[J]. 同济大学学报(自然科学版), 2019, 47(10): 1500-1507.
|
|
Zhou Qi-cai, Shen He-hong, Zhao Jiong, et al. Bearing fault diagnosis based on improved stacked recurrent neural network[J]. Journal of Tongji University (Natural Science), 2019, 47(10): 1500-1507.
|
7 |
Liang H P, Cao J, Zhao X Q. Average descent rate singular value decomposition and two-dimensional residual neural network for fault diagnosis of rotating machinery[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 3512616.
|
8 |
张龙, 胡燕青, 赵丽娟, 等. 采用递归图编码技术与残差网络的滚动轴承故障诊断[J]. 西安交通大学学报, 2023, 57(2): 110-120.
|
|
Zhang Long, Hu Yan-qing, Zhao Li-juan, et al. Fault diagnosis of rolling bearings using recurrence plot coding technique and residual network[J]. Journal of Xi’an Jiaotong University, 2023, 57(2): 110-120.
|
9 |
武明泽. 基于小波包分解和FPA-SVM的动车组轴箱轴承故障诊断研究[D]. 北京: 北京交通大学, 2021.
|
|
Wu Ming-ze. Research on fault diagnosis of EMU axle box bearing based on wavelet packet decomposition and FPA-SVM[D]. Beijing: Beijing Jiaotong University, 2021.
|
10 |
Han T, Liu C, Yang W G, et al. Deep transfer network with joint distribution adaptation: a new intelligent fault diagnosis framework for industry application[J]. ISA Transactions, 2020, 97: 269-281.
|
11 |
刘涛, 梁成玉. 信息熵融合的PSO-SVC涡旋压缩机故障诊断[J]. 振动、测试与诊断, 2022, 42(1): 141-147,200.
|
|
Liu Tao, Liang Cheng-yu. PSO-SVC fault diagnosis of scroll compressor based on information entropy fusion[J]. Journal of Vibration, Measurement & Diagnosis, 2022,42(1): 141-147,200.
|
12 |
Kok T L, Aldrich C, Zabiri H, et al. Application of unthresholded recurrence plots and texture analysis for industrial loops with faulty valves[J]. Soft Computing, 2022, 26(19): 10477-10492.
|
13 |
金江涛, 许子非, 李春, 等. 基于深度学习与混沌特征融合的滚动轴承故障诊断[J]. 控制理论与应用, 2022, 39(1): 109-116.
|
|
Jin Jiang-tao, Xu Zi-fei, Li Chun, et al. Rolling bearing fault diagnosis based on deep learning and chaotic feature fusion[J]. Control Theory & Applications, 2022, 39(1): 109-116.
|
14 |
Cui L L, Liu Y H, Zhao D Z. Adaptive singular value decomposition for bearing fault diagnosis under strong noise interference[J]. Measurement Science and Technology, 2022, 33(9): 095002.
|
15 |
郭明军, 李伟光, 杨期江, 等. 深度卷积神经网络在滑动轴承转子轴心轨迹识别中的应用[J]. 振动与冲击, 2021, 40(3): 233-239, 283.
|
|
Guo Ming-jun, Li Wei-guang, Yang Qi-jiang,et al. Application of deep convolution neural network in identification of journal bearing rotor center orbit[J]. Journal of Vibration and Shock, 2021, 40(3): 233-239, 283.
|
16 |
Wang Z Y, Yao L G, Chen G, et al. Modified multiscale weighted permutation entropy and optimized support vector machine method for rolling bearing fault diagnosis with complex signals[J]. ISA Transactions, 2021, 114: 470-484.
|
17 |
Faramarzi A, Heidarinejad M, Mirjalili S, et al. Marine predators algorithm: a nature-inspired metaheuristic[J]. Expert Systems with Applications, 2020, 152(4): 113377.
|
18 |
司莉, 毕贵红, 魏永刚, 等. 基于RQA与SVM的声发射信号检测识别方法[J]. 振动与冲击, 2016, 35(2): 97-103, 123.
|
|
Si Li, Bi Gui-hong, Wei Yong-gang, et al. Detection and identification of acoustic emission signals based on recurrence quantification analysis and support vector machines[J]. Journal of Vibration and Shock, 2016, 35(2): 97-103, 123.
|