2020年


  • Zhao M, Zhong S, Fu X, et al. Deep Residual Networks with Adaptively Parametric Rectifier Linear Units for Fault Diagnosis[J]. IEEE Transactions on Industrial Electronics, 2020.
    残差网络
  • Zhang W, Li X, Jia X D, et al. Machinery fault diagnosis with imbalanced data using deep generative adversarial networks[J]. Measurement, 2020, 152: 107377.
    GAN，不平衡数据
  • Zhao Z, Li T, Wu J, et al. Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study[J]. arXiv preprint arXiv:2003.03315, 2020.
    Benchmark
  • Wang R, Jiang H, Li X, et al. A reinforcement neural architecture search method for rolling bearing fault diagnosis[J]. Measurement, 2020, 154: 107417.
    NAS
  • Li X, Hu Y, Zheng J, et al. Neural Architecture Search For Fault Diagnosis[J]. arXiv preprint arXiv:2002.07997, 2020.
    NAS
  • Yuan Y, Ma G, Cheng C, et al. A general end-to-end diagnosis framework for manufacturing systems[J]. National Science Review, 2020, 7(2): 418-429.

2019年


  • Zhu Z, Peng G, Chen Y, et al. A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis[J]. Neurocomputing, 2019, 323: 62-75.
    胶囊网络
  • Chen T, Wang Z, Yang X, et al. A deep capsule neural network with stochastic delta rule for bearing fault diagnosis on raw vibration signals[J]. Measurement, 2019, 148: 106857.
    胶囊网络
  • Huang W, Cheng J, Yang Y, et al. An improved deep convolutional neural network with multi-scale information for bearing fault diagnosis[J]. Neurocomputing, 2019, 359: 77-92.
    多尺度信息
  • Ding Y, Ma L, Ma J, et al. Intelligent fault diagnosis for rotating machinery using deep Q-network based health state classification: A deep reinforcement learning approach[J]. Advanced Engineering Informatics, 2019, 42: 100977.
    强化学习方法
  • Li X, Zhang W, Ding Q. Understanding and improving deep learning-based rolling bearing fault diagnosis with attention mechanism[J]. Signal Processing, 2019, 161: 136-154.
    注意力机制
  • Liang P, Deng C, Wu J, et al. Intelligent Fault Diagnosis Via Semi-Supervised Generative Adversarial Nets and Wavelet Transform[J]. IEEE Transactions on Instrumentation and Measurement, 2019.
    GAN，小波变换
  • Kim S, Choi J H. Convolutional neural network for gear fault diagnosis based on signal segmentation approach[J]. Structural Health Monitoring, 2019, 18(5-6): 1401-1415.
    数据切割方法
  • Jian X, Li W, Guo X, et al. Fault diagnosis of motor bearings based on a one-dimensional fusion neural network[J]. Sensors, 2019, 19(1): 122.
  • Guo Q, Li Y, Song Y, et al. Intelligent Fault Diagnosis Method Based on Full 1D Convolutional Generative Adversarial Network[J]. IEEE Transactions on Industrial Informatics, 2019.
    GAN
  • Tang S, Yuan S, Zhu Y. Deep Learning-Based Intelligent Fault Diagnosis Methods Toward Rotating Machinery[J]. IEEE Access, 2019, 8: 9335-9346.
    CNN+ELM
  • Ma S, Chu F. Ensemble deep learning-based fault diagnosis of rotor bearing systems[J]. Computers in Industry, 2019, 105: 143-152.
    CNN、DBN、AE集成学习

2018年


  • Zhang W, Li C, Peng G, et al. A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load[J]. Mechanical Systems and Signal Processing, 2018, 100: 439-453.
  • Han T, Liu C, Yang W, et al. A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults[J]. Knowledge-Based Systems, 2019, 165: 474-487.
    GAN
  • Huang R, Liao Y, Zhang S, et al. Deep decoupling convolutional neural network for intelligent compound fault diagnosis[J]. IEEE Access, 2018, 7: 1848-1858.
  • Liu H, Zhou J, Xu Y, et al. Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks[J]. Neurocomputing, 2018, 315: 412-424.
    GAN
  • Li X, Zhang W, Ding Q, et al. Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation[J]. Journal of Intelligent Manufacturing, 2018: 1-20.
    残差网络，数据增强
  • Zhang W, Li X, Ding Q. Deep residual learning-based fault diagnosis method for rotating machinery[J]. ISA transactions, 2018.
    残差网络
  • Abdeljaber O, Avci O, Kiranyaz M S, et al. 1-D CNNs for structural damage detection: verification on a structural health monitoring benchmark data[J]. Neurocomputing, 2018, 275: 1308-1317.
    普通的一维CNN

2017年


  • Jing L, Zhao M, Li P, et al. A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox[J]. Measurement, 2017, 111: 1-10.
    普通卷积
  • Eren L. Bearing fault detection by one-dimensional convolutional neural networks[J]. Mathematical Problems in Engineering, 2017, 2017.
    普通卷积
  • Lu C, Wang Z, Zhou B. Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification[J]. Advanced Engineering Informatics, 2017, 32: 139-151.
    普通卷积
  • Pan J, Zi Y, Chen J, et al. LiftingNet: A novel deep learning network with layerwise feature learning from noisy mechanical data for fault classification[J]. IEEE Transactions on Industrial Electronics, 2017, 65(6): 4973-4982.
  • Abdeljaber O, Avci O, Kiranyaz S, et al. Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks[J]. Journal of Sound and Vibration, 2017, 388: 154-170.

2016年


  • Lee D, Siu V, Cruz R, et al. Convolutional neural net and bearing fault analysis[C] //Proceedings of the International Conference on Data Mining (DMIN). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), 2016: 194.
    普通卷积
  • Janssens O, Slavkovikj V, Vervisch B, et al. Convolutional neural network based fault detection for rotating machinery[J]. Journal of Sound and Vibration, 2016, 377: 331-345.
    普通卷积
  • Ding X, He Q. Energy-fluctuated multiscale feature learning with deep convnet for intelligent spindle bearing fault diagnosis[J]. IEEE Transactions on Instrumentation and Measurement, 2017, 66(8): 1926-1935.
    普通卷积
  • Guo X, Chen L, Shen C. Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis[J]. Measurement, 2016, 93: 490-502.
    普通卷积
  • Ince T, Kiranyaz S, Eren L, et al. Real-time motor fault detection by 1-D convolutional neural networks[J]. IEEE Transactions on Industrial Electronics, 2016, 63(11): 7067-7075.
    普通卷积

2015年

  • Chen Z Q, Li C, Sanchez R V. Gearbox fault identification and classification with convolutional neural networks[J]. Shock and Vibration, 2015, 2015.