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Fault Diagnosis

Bearing fault diagnosis

  • Xin, L., et al., Modified Gaussian convolutional deep belief network and infrared thermal imaging for intelligent fault diagnosis of rotor-bearing system under time-varying speeds. Structural Health Monitoring, 2021: p. 147592172199895.link
  • Chen, J., et al., Unsupervised Cross-domain Fault Diagnosis Using Feature Representation Alignment Networks for Rotating Machinery. IEEE/ASME Transactions on Mechatronics: p. 1-1.link
  • T. Li, Z. Zhao, C. Sun, R. Yan and X. Chen, "Multi-receptive Field Graph Convolutional Networks for Machine Fault Diagnosis," in IEEE Transactions on Industrial Electronics, doi: 10.1109/TIE.2020.3040669.link
  • Jin, Y., et al., Actual Bearing Compound Fault Diagnosis based on Active Learning and Decoupling Attentional Residual Network. Measurement, 2020: p. 108500.link
  • Tong, J., et al., A Novel Cuckoo Search Optimized Deep Auto-Encoder Network-Based Fault Diagnosis Method for Rolling Bearing. Shock and Vibration, 2020. 2020: p. 1-12.link
  • Mao, W., et al., A new deep auto-encoder method with fusing discriminant information for bearing fault diagnosis. Mechanical Systems and Signal Processing, 2021. 150: p. 107233.link
  • Chen, Z., et al., A deep learning method for bearing fault diagnosis based on Cyclic Spectral Coherence and Convolutional Neural Networks. Mechanical Systems and Signal Processing, 2020. 140: p. 106683.link
  • Li, S., et al., An adaptive data fusion strategy for fault diagnosis based on the convolutional neural network. Measurement, 2020. 165: p. 108122.link
  • Chen, Z., et al., Domain Adversarial Transfer Network for Cross-domain Fault Diagnosis of Rotary Machinery. IEEE Transactions on Instrumentation and Measurement: p. 1-1.link
  • Cheng, C., et al., Wasserstein distance based deep adversarial transfer learning for intelligent fault diagnosis with unlabeled or insufficient labeled data. Neurocomputing, 2020. 409: p. 35-45.link
  • Zhang, Z., et al., Unsupervised domain adaptation via enhanced transfer joint matching for bearing fault diagnosis. Measurement, 2020: p. 108071.link
  • Xu, X., et al., Intelligent monitoring and diagnostics using a novel integrated model based on deep learning and multi-sensor feature fusion. Measurement, 2020: p. 108086.link
  • Haidong, S., et al., Intelligent fault diagnosis among different rotating machines using novel stacked transfer auto-encoder optimized by PSO. ISA Transactions, 2020.link
  • Zou, L., Y. Li and F. Xu, An adversarial denoising convolutional neural network for fault diagnosis of rotating machinery under noisy environment and limited sample size case. Neurocomputing, 2020.Link
  • Guo, S., et al., Multitask Convolutional Neural Network With Information Fusion for Bearing Fault Diagnosis and Localization. IEEE Transactions on Industrial Electronics, 2020. 67(9): p. 8005-8015.link
  • Wang, H., et al., An Enhanced Intelligent Diagnosis Method Based on Multi-Sensor Image Fusion via Improved Deep Learning Network. IEEE Transactions on Instrumentation and Measurement, 2020. 69(6): p. 2648-2657.link
  • Mao, W., W. Feng and X. Liang, A novel deep output kernel learning method for bearing fault structural diagnosis. Mechanical Systems and Signal Processing, 2019. 117: p. 293-318.link
  • Zhang, W., et al., A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mechanical Systems and Signal Processing, 2018. 100: p. 439-453.link
  • Zhao, D., T. Wang and F. Chu, Deep convolutional neural network based planet bearing fault classification. Computers in Industry, 2019. 107: p. 59-66.link
  • Jiao J, Zhao M, Lin J, et al. A comprehensive review on convolutional neural network in machine fault diagnosis[J]. arXiv preprint arXiv:2002.07605, 2020. link
  • Li, X., W. Zhang and Q. Ding, Cross-Domain Fault Diagnosis of Rolling Element Bearings Using Deep Generative Neural Networks. IEEE Transactions on Industrial Electronics, 2019. 66(7): p. 5525-5534. link
  • Shao, S., P. Wang and R. Yan, Generative adversarial networks for data augmentation in machine fault diagnosis. Computers in Industry, 2019. 106: p. 85-93.link
  • Wen, L., et al., A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method. IEEE Transactions on Industrial Electronics, 2018. 65(7): p. 5990-5998.link
  • Wen, L., L. Gao and X. Li, A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019. 49(1): p. 136-144.link
  • Wen, L., X. Li and L. Gao, A New Two-Level Hierarchical Diagnosis Network Based on Convolutional Neural Network. IEEE Transactions on Instrumentation and Measurement, 2020. 69(2): p. 330-338.link

Gearbox fault diagnosis

  • Xing, S., et al., Distribution-Invariant Deep Belief Network for Intelligent Fault Diagnosis of Machines Under New Working Conditions. IEEE transactions on industrial electronics (1982), 2021. 68(3): p. 2617-2625.link

  • Jiang, G., et al., Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox. IEEE Transactions on Industrial Electronics, 2019. 66(4): p. 3196-3207.link---Notes

  • Hu, Z., et al., Data-Driven Fault Diagnosis Method Based on Compressed Sensing and Improved Multiscale Network. IEEE Transactions on Industrial Electronics, 2020. 67(4): p. 3216-3225. link

Others

  • Chadha, G.S., et al., Bidirectional deep recurrent neural networks for process fault classification. ISA Transactions, 2020.link
  • Fu, S., et al., A re-optimized deep auto-encoder for gas turbine unsupervised anomaly detection. Engineering Applications of Artificial Intelligence, 2021. 101: p. 104199. link

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