Fault diagnosis of rolling bearing based on online transfer convolutional neural network

High-precision bearing signal recovery based on signal fusion and variable stepsize forward-backward pursuit

(2021) Zuozhou Pan et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Wasserstein Distance based Deep Adversarial Transfer Learning for Intelligent Fault Diagnosis with Unlabeled or Insufficient Labeled Data

(2020) Cheng Cheng et al.   NEUROCOMPUTING

An enhanced convolutional neural network for bearing fault diagnosis based on time–frequency image

(2020) Ying Zhang et al.   MEASUREMENT

Domain generalization in rotating machinery fault diagnostics using deep neural networks

(2020) Xiang Li et al.   NEUROCOMPUTING

Interpreting network knowledge with attention mechanism for bearing fault diagnosis

(2020) Zhi-bo Yang et al.   APPLIED SOFT COMPUTING

A two-stage method based on extreme learning machine for predicting the remaining useful life of rolling-element bearings

(2020) Zuozhou Pan et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Ensemble deep transfer learning driven by multisensor signals for the fault diagnosis of bevel-gear cross-operation conditions

(2020) ZiYang Di et al.   Science China-Technological Sciences

Transferable convolutional neural network based remaining useful life prediction of bearing under multiple failure behaviors

(2020) Han Cheng et al.   MEASUREMENT

Improved generative adversarial network for vibration-based fault diagnosis with imbalanced data

(2020) Bingxi Zhao et al.   MEASUREMENT

Sparse representation based on parametric impulsive dictionary design for bearing fault diagnosis

(2019) Ruo-Bin Sun et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Online detection for bearing incipient fault based on deep transfer learning

(2019) Wentao Mao et al.   MEASUREMENT

Online Fault Diagnosis Method Based on Transfer Convolutional Neural Networks

(2019) Gaowei Xu et al.   IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT

Online automatic diagnosis of wind turbine bearings progressive degradations under real experimental conditions based on unsupervised machine learning

(2018) Jaouher Ben Ali et al.   APPLIED ACOUSTICS

A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method

(2018) Long Wen et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Cross-Domain Fault Diagnosis of Rolling Element Bearings Using Deep Generative Neural Networks

(2018) Xiang Li et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Highly-Accurate Machine Fault Diagnosis Using Deep Transfer Learning

(2018) Siyu Shao et al.   IEEE Transactions on Industrial Informatics

Transferable Representation Learning with Deep Adaptation Networks

(2018) Mingsheng Long et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

An enhancement denoising autoencoder for rolling bearing fault diagnosis

(2018) Zong Meng et al.   MEASUREMENT

Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in Manufacturing

(2018) Chuang Sun et al.   IEEE Transactions on Industrial Informatics

Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data

(2018) Liang Guo et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Deep Model Based Domain Adaptation for Fault Diagnosis

(2017) Weining Lu et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis

(2017) Long Wen et al.   IEEE Transactions on Systems Man Cybernetics-Systems

An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data

(2016) Yaguo Lei et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

A Survey on Wind Turbine Condition Monitoring and Fault Diagnosis—Part II: Signals and Signal Processing Methods

(2015) Wei Qiao et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Transfer learning using computational intelligence: A survey

(2015) Jie Lu et al.   KNOWLEDGE-BASED SYSTEMS

Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study

(2015) Wade A. Smith et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING