2020

  • Zhou F, Yang S, Fujita H, et al. Deep learning fault diagnosis method based on global optimization GAN for unbalanced data[J]. Knowledge-Based Systems, 2020, 187: 104837.

2019


  • Yu J. A selective deep stacked denoising autoencoders ensemble with negative correlation learning for gearbox fault diagnosis[J]. Computers in Industry, 2019, 108: 62-72.
  • Xueyi L I, Jialin L I, Yongzhi Q U, et al. Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning[J]. Chinese Journal of Aeronautics, 2019.
  • Wang X, Qin Y, Wang Y, et al. ReLTanh: An activation function with vanishing gradient resistance for SAE-based DNNs and its application to rotating machinery fault diagnosis[J]. Neurocomputing, 2019, 363: 88-98.

2018


  • Zhang Y, Li X, Gao L, et al. A new subset based deep feature learning method for intelligent fault diagnosis of bearing[J]. Expert Systems with Applications, 2018, 110: 125-142.
  • Jia F, Lei Y, Guo L, et al. A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines[J]. Neurocomputing, 2018, 272: 619-628.
  • Lin Y, Li X, Hu Y. Deep diagnostics and prognostics: An integrated hierarchical learning framework in PHM applications[J]. Applied Soft Computing, 2018, 72: 555-564.
  • Shao H, Jiang H, Lin Y, et al. A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders[J]. Mechanical Systems and Signal Processing, 2018, 102: 278-297.
  • Qin Y, Wang X, Zou J. The optimized deep belief networks with improved logistic Sigmoid units and their application in fault diagnosis for planetary gearboxes of wind turbines[J]. IEEE Transactions on Industrial Electronics, 2018, 66(5): 3814-3824.

2017


  • Mao W, He J, Li Y, et al. Bearing fault diagnosis with auto-encoder extreme learning machine: A comparative study[J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2017, 231(8): 1560-1578.
  • Chen Z, Li W. Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network[J]. IEEE Transactions on Instrumentation and Measurement, 2017, 66(7): 1693-1702.
  • Zhang R, Peng Z, Wu L, et al. Fault diagnosis from raw sensor data using deep neural networks considering temporal coherence[J]. Sensors, 2017, 17(3): 549.
  • Lu C, Wang Z Y, Qin W L, et al. Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification[J]. Signal Processing, 2017, 130: 377-388.
  • Chen Z, Deng S, Chen X, et al. Deep neural networks-based rolling bearing fault diagnosis[J]. Microelectronics Reliability, 2017, 75: 327-333.
  • Li K, Wu Y, Nan Y, et al. Hierarchical multi-class classification in multimodal spacecraft data using DNN and weighted support vector machine[J]. Neurocomputing, 2017, 259: 55-65.

2016


  • Tao J, Liu Y, Yang D. Bearing fault diagnosis based on deep belief network and multisensor information fusion[J]. Shock and Vibration, 2016, 2016.
  • Li C, Sánchez R V, Zurita G, et al. Fault diagnosis for rotating machinery using vibration measurement deep statistical feature learning[J]. Sensors, 2016, 16(6): 895.
  • Deng S, Cheng Z, Li C, et al. Rolling bearing fault diagnosis based on Deep Boltzmann machines[C]//2016 Prognostics and System Health Management Conference (PHM-Chengdu). IEEE, 2016: 1-6.
  • Jia F, Lei Y, Lin J, et al. Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data[J]. Mechanical Systems and Signal Processing, 2016, 72: 303-315.
  • Thirukovalluru R, Dixit S, Sevakula R K, et al. Generating feature sets for fault diagnosis using denoising stacked auto-encoder[C]//2016 IEEE International Conference on Prognostics and Health Management (ICPHM). IEEE, 2016: 1-7.
  • Liu H, Li L, Ma J. Rolling bearing fault diagnosis based on STFT-deep learning and sound signals[J]. Shock and Vibration, 2016, 2016.
  • Jia F, Lei Y, Lin J, et al. Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data[J]. Mechanical Systems and Signal Processing, 2016, 72: 303-315.

2015

  • Lu W, Wang X, Yang C, et al. A novel feature extraction method using deep neural network for rolling bearing fault diagnosis[C]//The 27th Chinese Control and Decision Conference (2015 CCDC). IEEE, 2015: 2427-2431.