Adversarial domain adaptation convolutional neural network for intelligent recognition of bearing faults

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

(2021) Tian Han et al.   MEASUREMENT

Wasserstein distance based deep multi-feature adversarial transfer diagnosis approach under variable working conditions

(2020) D. She et al.   Journal of Instrumentation

Deep balanced domain adaptation neural networks for fault diagnosis of planetary gearboxes with limited labeled data

(2020) Qikang Li et al.   MEASUREMENT

Applications of machine learning to machine fault diagnosis: A review and roadmap

(2020) Yaguo Lei et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

A Survey of Unsupervised Deep Domain Adaptation

(2020) Garrett Wilson et al.   ACM Transactions on Intelligent Systems and Technology

Intelligent Fault Diagnosis of Multichannel Motor–Rotor System Based on Multimanifold Deep Extreme Learning Machine

(2020) Xiaoli Zhao et al.   IEEE-ASME TRANSACTIONS ON MECHATRONICS

A systematic review of deep transfer learning for machinery fault diagnosis

(2020) Chuan Li et al.   NEUROCOMPUTING

A comprehensive review on convolutional neural network in machine fault diagnosis

(2020) Jinyang Jiao et al.   NEUROCOMPUTING

Semisupervised Graph Convolution Deep Belief Network for Fault Diagnosis of Electormechanical System With Limited Labeled Data

(2020) Xiaoli Zhao et al.   IEEE Transactions on Industrial Informatics

Intelligent fault diagnosis of rotating machinery based on one-dimensional convolutional neural network

(2019) Chunzhi Wu et al.   COMPUTERS IN INDUSTRY

A novel adaptive and fast deep convolutional neural network for bearing fault diagnosis under different working conditions

(2019) Kun Xu et al.   PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING

Generalization of deep neural network for bearing fault diagnosis under different working conditions using multiple kernel method

(2019) Zenghui An et al.   NEUROCOMPUTING

Deep Learning-Based Machinery Fault Diagnostics With Domain Adaptation Across Sensors at Different Places

(2019) Xiang Li et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Generative Adversarial Networks: An Overview

(2018) Antonia Creswell et al.   IEEE SIGNAL PROCESSING MAGAZINE

Artificial intelligence for fault diagnosis of rotating machinery: A review

(2018) Ruonan Liu et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

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

A review on data-driven fault severity assessment in rolling bearings

(2018) Mariela Cerrada et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Deep visual domain adaptation: A survey

(2018) Mei Wang et al.   NEUROCOMPUTING

A survey on Deep Learning based bearing fault diagnosis

(2018) Duy-Tang Hoang et al.   NEUROCOMPUTING

A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults

(2018) Te Han et al.   KNOWLEDGE-BASED SYSTEMS

Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet

(2017) Haidong Shao et al.   ISA TRANSACTIONS

A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals

(2017) Wei Zhang et al.   SENSORS

A novel intelligent method for mechanical fault diagnosis based on dual-tree complex wavelet packet transform and multiple classifier fusion

(2016) Jinxiu Qu et al.   NEUROCOMPUTING

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