Fault diagnosis of rolling bearing based on deep convolutional neural network and gated recurrent unit

Rolling bearing fault diagnosis with combined convolutional neural networks and support vector machine

(2021) Tian Han et al.   MEASUREMENT

Early Fault Detection Method of Rolling Bearing Based on MCNN and GRU Network with an Attention Mechanism

(2021) Xiaochen Zhang et al.   SHOCK AND VIBRATION

Combination bidirectional long short-term memory and capsule network for rotating machinery fault diagnosis

(2021) Tian Han et al.   MEASUREMENT

New domain adaptation method in shallow and deep layers of the CNN for bearing fault diagnosis under different working conditions

(2021) Tongtong Jin et al.   INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

Technology development and commercial applications of industrial fault diagnosis system: a review

(2021) Chengze Liu et al.   INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

Semi-Random Subspace with Bi-GRU: Fusing Statistical and Deep Representation Features for Bearing Fault Diagnosis

(2020) XuTing Mao et al.   MEASUREMENT

A Sensor Fault-Tolerant Accident Diagnosis System

(2020) Jeonghun Choi et al.   SENSORS

Rolling bearing fault diagnosis based on feature fusion with parallel convolutional neural network

(2020) Mingxuan Liang et al.   The International Journal of Advanced Manufacturing Technology

Conditional t-SNE: more informative t-SNE embeddings

(2020) Bo Kang et al.   MACHINE LEARNING

A New Reinforcement Learning Based Learning Rate Scheduler for Convolutional Neural Network in Fault Classification

(2020) Long Wen et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Sensor Data-Driven Bearing Fault Diagnosis Based on Deep Convolutional Neural Networks and S-Transform

(2019) Guoqiang Li et al.   SENSORS

A New Intelligent Bearing Fault Diagnosis Method Using SDP Representation and SE-CNN

(2019) Hui Wang et al.   IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT

A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load

(2018) Wei Zhang et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Deep learning and its applications to machine health monitoring

(2018) Rui Zhao et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

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

Deep learning in neural networks: An overview

(2015) Jürgen Schmidhuber   NEURAL NETWORKS

Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks

(2012) Miguel Delgado Prieto et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS