☆ 4.7 Article

A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2022)

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

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

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IEEE-ASME TRANSACTIONS ON MECHATRONICS (2021)

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)

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Optimal Transport-Based Deep Domain Adaptation Approach for Fault Diagnosis of Rotating Machine

Zhao-Hua Liu et al.

Summary: A new optimal transport-based deep domain adaptation model is proposed for fault diagnosis in rotating machinery. The method outperforms existing approaches in the discrepancies of joint distribution of the feature and label spaces.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)

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Article Engineering, Electrical & Electronic

A Novel Multitask Adversarial Network via Redundant Lifting for Multicomponent Intelligent Fault Detection Under Sharp Speed Variation

Zhen Shi et al.

Summary: The study introduces a novel multitask redundant lifting adversarial network (MRLAN) which addresses the issue of multicomponent fault diagnosis in mechanical systems through a reconstruction network and multitask category discriminator.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)

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Article Engineering, Electrical & Electronic

Dynamic Joint Distribution Alignment Network for Bearing Fault Diagnosis Under Variable Working Conditions

Changqing Shen et al.

Summary: The traditional deep learning for machine fault diagnosis is hindered by inconsistent distribution between training and testing data due to complex and changeable machine working conditions. Transfer learning is proposed to address this issue and the DJDAN is introduced to dynamically evaluate the relative importance of marginal and conditional distributions for aligning two different domains. Experimental results demonstrate the superiority and generalization of DJDAN for bearing fault diagnosis under different working conditions.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)

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Article Engineering, Electrical & Electronic

Deep Coupled Joint Distribution Adaptation Network: A Method for Intelligent Fault Diagnosis Between Artificial and Real Damages

Yongwen Tan et al.

Summary: A deep coupled joint distribution adaptation network (DCJDAN) is proposed to address the large domain discrepancy between artificial and real damages, utilizing untied deep convolutional networks to learn domain-representative features and minimize domain discrepancy. The method includes a regulation term to keep the coupled networks similar and a joint distribution adaptation module for accurate distribution matching, achieving improved diagnosis performance compared to conventional deep learning models.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)

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Article Computer Science, Information Systems

Deep Transfer Network With Multi-Kernel Dynamic Distribution Adaptation for Cross-Machine Fault Diagnosis

Mingzhu Lv et al.

Summary: Various deep learning models based on data-driven algorithms have gained more attention in intelligent fault diagnosis and prognostics. However, the default assumptions in existing studies may not be applicable in real industrial scenarios, prompting the proposal of a novel method utilizing deep transfer network and multi-kernel dynamic distribution adaptation for cross-machine fault diagnosis.

IEEE ACCESS (2021)

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Article Engineering, Electrical & Electronic

Intelligent Diagnostics for Ball Screw Fault Through Indirect Sensing Using Deep Domain Adaptation

Vibhor Pandhare et al.

Summary: The study proposed an indirect sensing method to address the challenge of impractical sensor locations for ball screws. By utilizing a convolutional neural network-based domain adaptation method, the transferability of the diagnostic model between sensor locations was achieved, resulting in satisfactory accuracy across multiple transfer tasks.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)

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Article Engineering, Electrical & Electronic

Adversarial Multiple-Target Domain Adaptation for Fault Classification

Mohamed Ragab et al.

Summary: Data-driven fault classification methods are widely studied, with domain adaptation techniques addressing the domain shift problem. Existing methods focus on single-source single-target settings, limiting scalability as a new model needs to be trained for each new target domain. A novel adversarial multiple-target domain adaptation method is proposed to generalize to multiple target domains concurrently.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)

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Article Engineering, Electrical & Electronic

Deep Domain Generalization Combining A Priori Diagnosis Knowledge Toward Cross-Domain Fault Diagnosis of Rolling Bearing

Huailiang Zheng et al.

Summary: Recent studies show potential in using knowledge transfer strategies to address cross-domain diagnosis problems. This article introduces a diagnosis scheme for rolling bearings that combines a priori diagnostic knowledge with a deep domain generalization network.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)

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Article Construction & Building Technology

Deep-learning-based guided wave detection for liquid-level state in porcelain bushing type terminal

Xiaobin Hong et al.

Summary: This paper proposes a deep learning method to quantitatively detect the liquid-level state using original guided wave signals, by utilizing convolutional autoencoder for feature extraction and long short-term memory recurrent neural network for liquid-level regression. Experimental results demonstrate that the proposed method has better accuracy and robustness compared to traditional methods.

STRUCTURAL CONTROL & HEALTH MONITORING (2021)

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Article Engineering, Electrical & Electronic

A New Structured Domain Adversarial Neural Network for Transfer Fault Diagnosis of Rolling Bearings Under Different Working Conditions

Wentao Mao et al.

Summary: The article introduces a new deep transfer learning method, SDANN, which integrates DANN and structured relatedness information among multiple failure modes to improve transfer learning. By utilizing a new loss function, relatedness matrix, and symmetry constraint regularizer, the method achieves stability on insufficient data and good diagnostic performance.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)

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Article Automation & Control Systems

Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application

Te Han et al.

ISA TRANSACTIONS (2020)

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Article Computer Science, Artificial Intelligence

Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples

He Zhiyi et al.

KNOWLEDGE-BASED SYSTEMS (2020)

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Article Computer Science, Artificial Intelligence

Fault diagnostics between different type of components: A transfer learning approach

Xudong Li et al.

APPLIED SOFT COMPUTING (2020)

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Article Automation & Control Systems

Diagnosing Rotating Machines With Weakly Supervised Data Using Deep Transfer Learning

Xiang Li et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

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Article Automation & Control Systems

Intelligent Fault Diagnosis for Rotary Machinery Using Transferable Convolutional Neural Network

Zhuyun Chen et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

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Article Automation & Control Systems

Deep Hybrid State Network With Feature Reinforcement for Intelligent Fault Diagnosis of Delta 3-D Printers

Shaohui Zhang et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

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Article Automation & Control Systems

Intelligent Fault Diagnosis Method Based on Full 1-D Convolutional Generative Adversarial Network

Qingwen Guo et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

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Article Engineering, Electrical & Electronic

Generalized Vold-Kalman Filtering for Nonstationary Compound Faults Feature Extraction of Bearing and Gear

Dezun Zhao et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2020)

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Article Engineering, Electrical & Electronic

Multiple Enhanced Sparse Decomposition for Gearbox Compound Fault Diagnosis

Ning Li et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2020)

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Article Computer Science, Interdisciplinary Applications

Exploring Sample/Feature Hybrid Transfer for Gear Fault Diagnosis Under Varying Working Conditions

Fei Shen et al.

JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING (2020)

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Article Engineering, Multidisciplinary

Machinery fault diagnosis with imbalanced data using deep generative adversarial networks

Wei Zhang et al.

MEASUREMENT (2020)

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Article Engineering, Multidisciplinary

An adaptive deep transfer learning method for bearing fault diagnosis

Zhenghong Wu et al.

MEASUREMENT (2020)

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Article Engineering, Multidisciplinary

Transfer fault diagnosis of bearing installed in different machines using enhanced deep auto-encoder

He Zhiyi et al.

MEASUREMENT (2020)

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Article Engineering, Multidisciplinary

Cross-domain learning in rotating machinery fault diagnosis under various operating conditions based on parameter transfer

Fudong Li et al.

MEASUREMENT SCIENCE AND TECHNOLOGY (2020)

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Article Engineering, Multidisciplinary

Deep learning-based cross-domain adaptation for gearbox fault diagnosis under variable speed conditions

Jaskaran Singh et al.

MEASUREMENT SCIENCE AND TECHNOLOGY (2020)

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Article Computer Science, Artificial Intelligence

Intelligent cross-machine fault diagnosis approach with deep auto-encoder and domain adaptation

Xiang Li et al.

NEUROCOMPUTING (2020)

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Article Chemistry, Analytical

Triplet Loss Guided Adversarial Domain Adaptation for Bearing Fault Diagnosis

Xiaodong Wang et al.

SENSORS (2020)

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Article Engineering, Industrial

Transfer learning for enhanced machine fault diagnosis in manufacturing

Peng Wang et al.

CIRP ANNALS-MANUFACTURING TECHNOLOGY (2020)

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Article Engineering, Electrical & Electronic

Deep Ensemble Capsule Network for Intelligent Compound Fault Diagnosis Using Multisensory Data

Ruyi Huang et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2020)

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Article Automation & Control Systems

Intelligent fault diagnosis among different rotating machines using novel stacked transfer auto-encoder optimized by PSO

Shao Haidong et al.

ISA TRANSACTIONS (2020)

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Article Engineering, Industrial

Deep learning-based adversarial multi-classifier optimization for cross-domain machinery fault diagnostics

Xiang Li et al.

JOURNAL OF MANUFACTURING SYSTEMS (2020)

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Article Acoustics

Deep Domain Adaptation Model for Bearing Fault Diagnosis with Domain Alignment and Discriminative Feature Learning

Jing An et al.

SHOCK AND VIBRATION (2020)

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Article Acoustics

Fault Diagnosis under Variable Working Conditions Based on STFT and Transfer Deep Residual Network

Yan Du et al.

SHOCK AND VIBRATION (2020)

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Article Automation & Control Systems

An unsupervised fault diagnosis method for rolling bearing using STFT and generative neural networks

Hongfeng Tao et al.

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS (2020)

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Article Computer Science, Artificial Intelligence

A deep transfer maximum classifier discrepancy method for rolling bearing fault diagnosis under few labeled data

Zhenghong Wu et al.

KNOWLEDGE-BASED SYSTEMS (2020)

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Article Engineering, Multidisciplinary

Multi-fault diagnosis of rotating machinery based on deep convolution neural network and support vector machine

Yan Xue et al.

MEASUREMENT (2020)

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Article Engineering, Multidisciplinary

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

Qikang Li et al.

MEASUREMENT (2020)

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Review Engineering, Mechanical

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

Yaguo Lei et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2020)

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Article Computer Science, Artificial Intelligence

A deep domain adaption model with multi-task networks for planetary gearbox fault diagnosis

Xincheng Cao et al.

NEUROCOMPUTING (2020)

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Article Computer Science, Artificial Intelligence

Wasserstein distance based deep adversarial transfer learning for intelligent fault diagnosis with unlabeled or insufficient labeled data

Cheng Cheng et al.

NEUROCOMPUTING (2020)

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Article Engineering, Electrical & Electronic

A Deep Adversarial Transfer Learning Network for Machinery Emerging Fault Detection

Jipu Li et al.

IEEE SENSORS JOURNAL (2020)

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Article Engineering, Electrical & Electronic

A New Deep Transfer Learning Method for Bearing Fault Diagnosis Under Different Working Conditions

Jun Zhu et al.

IEEE SENSORS JOURNAL (2020)

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Article Engineering, Electrical & Electronic

Knowledge Transfer for Rotary Machine Fault Diagnosis

Ruqiang Yan et al.

IEEE SENSORS JOURNAL (2020)

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Article Automation & Control Systems

A Fine-Grained Adversarial Network Method for Cross-Domain Industrial Fault Diagnosis

Zheng Chai et al.

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2020)

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Article Automation & Control Systems

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

Xiang Li et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2020)

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Article Engineering, Electrical & Electronic

Intelligent Fault Diagnosis via Semisupervised Generative Adversarial Nets and Wavelet Transform

Pengfei Liang et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2020)

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Article Engineering, Electrical & Electronic

Compound Bearing Fault Detection Under Varying Speed Conditions With Virtual Multichannel Signals in Angle Domain

Gang Tang et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2020)

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Article Engineering, Manufacturing

Deep Learning-Based Domain Adaptation Method for Fault Diagnosis in Semiconductor Manufacturing

Moslem Azamfar et al.

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING (2020)

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Article Computer Science, Artificial Intelligence

Single and simultaneous fault diagnosis of gearbox via a semi-supervised and high-accuracy adversarial learning framework

Pengfei Liang et al.

KNOWLEDGE-BASED SYSTEMS (2020)

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Article Engineering, Multidisciplinary

Intelligent fault diagnosis of rotating machinery via wavelet transform, generative adversarial nets and convolutional neural network

Pengfei Liang et al.

MEASUREMENT (2020)

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Article Engineering, Multidisciplinary

Data synthesis using dual discriminator conditional generative adversarial networks for imbalanced fault diagnosis of rolling bearings

Taisheng Zheng et al.

MEASUREMENT (2020)

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Article Engineering, Mechanical

A deep learning method for bearing fault diagnosis based on Cyclic Spectral Coherence and Convolutional Neural Networks

Zhuyun Chen et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2020)

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Article Computer Science, Artificial Intelligence

Domain generalization in rotating machinery fault diagnostics using deep neural networks

Xiang Li et al.

NEUROCOMPUTING (2020)

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Article Automation & Control Systems

Classifier Inconsistency-Based Domain Adaptation Network for Partial Transfer Intelligent Diagnosis

Jinyang Jiao et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

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Article Automation & Control Systems

Retraining Strategy-Based Domain Adaption Network for Intelligent Fault Diagnosis

Yan Song et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

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Article Automation & Control Systems

A Robust Weight-Shared Capsule Network for Intelligent Machinery Fault Diagnosis

Ruyi Huang et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

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Article Automation & Control Systems

Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study

Zhibin Zhao et al.

ISA TRANSACTIONS (2020)

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Article Computer Science, Artificial Intelligence

Double-level adversarial domain adaptation network for intelligent fault diagnosis

Jinyang Jiao et al.

KNOWLEDGE-BASED SYSTEMS (2020)

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Article Engineering, Mechanical

Intelligent ball screw fault diagnosis using a deep domain adaptation methodology

Moslem Azamfar et al.

MECHANISM AND MACHINE THEORY (2020)

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Article Computer Science, Artificial Intelligence

Partial transfer learning in machinery cross-domain fault diagnostics using class-weighted adversarial networks

Xiang Li et al.

NEURAL NETWORKS (2020)

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Review Computer Science, Artificial Intelligence

A systematic review of deep transfer learning for machinery fault diagnosis

Chuan Li et al.

NEUROCOMPUTING (2020)

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Article Computer Science, Artificial Intelligence

An adversarial denoising convolutional neural network for fault diagnosis of rotating machinery under noisy environment and limited sample size case

Lei Zou et al.

NEUROCOMPUTING (2020)

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Article Computer Science, Artificial Intelligence

Deep multi-scale convolutional transfer learning network: A novel method for intelligent fault diagnosis of rolling bearings under variable working conditions and domains

Bo Zhao et al.

NEUROCOMPUTING (2020)

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Article Engineering, Industrial

Multi-scale deep intra-class transfer learning for bearing fault diagnosis

Xu Wang et al.

RELIABILITY ENGINEERING & SYSTEM SAFETY (2020)

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Article Acoustics

Bearing Fault Diagnosis Based on Multilayer Domain Adaptation

Bingru Yang et al.

SHOCK AND VIBRATION (2020)

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Review Automation & Control Systems

Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications

Thanh Thi Nguyen et al.

IEEE TRANSACTIONS ON CYBERNETICS (2020)

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Article Computer Science, Artificial Intelligence

A study on adaptation lightweight architecture based deep learning models for bearing fault diagnosis under varying working conditions

Jie Wu et al.

EXPERT SYSTEMS WITH APPLICATIONS (2020)

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Article Engineering, Electrical & Electronic

Multi-Task Deep Transfer Learning Method for Guided Wave-Based Integrated Health Monitoring Using Piezoelectric Transducers

Bin Zhang et al.

IEEE SENSORS JOURNAL (2020)

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Article Engineering, Multidisciplinary

Unsupervised domain adaptation via enhanced transfer joint matching for bearing fault diagnosis

Zhongwei Zhang et al.

MEASUREMENT (2020)

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Review Computer Science, Artificial Intelligence

A comprehensive review on convolutional neural network in machine fault diagnosis

Jinyang Jiao et al.

NEUROCOMPUTING (2020)

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Article Acoustics

Deep Transfer Learning-Based Fault Diagnosis for Gearbox under Complex Working Conditions

Zitong Wan et al.

SHOCK AND VIBRATION (2020)

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Article Acoustics

Multisignal VGG19 Network with Transposed Convolution for Rotating Machinery Fault Diagnosis Based on Deep Transfer Learning

Jianye Zhou et al.

SHOCK AND VIBRATION (2020)

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Article Automation & Control Systems

Softly Associative Transfer Learning for Cross-Domain Classification

Deqing Wang et al.

IEEE TRANSACTIONS ON CYBERNETICS (2020)

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Article Automation & Control Systems

Discriminative Cross-Modal Transfer Learning and Densely Cross-Level Feedback Fusion for RGB-D Salient Object Detection

Hao Chen et al.

IEEE TRANSACTIONS ON CYBERNETICS (2020)

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Article Chemistry, Analytical

Development of Compound Fault Diagnosis System for Gearbox Based on Convolutional Neural Network

Ming-Chang Lin et al.

SENSORS (2020)

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Article Engineering, Electrical & Electronic

Domain Adversarial Transfer Network for Cross-Domain Fault Diagnosis of Rotary Machinery

Zhuyun Chen et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2020)

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Article Computer Science, Artificial Intelligence

Ensemble transfer CNNs driven by multi-channel signals for fault diagnosis of rotating machinery cross working conditions

He Zhiyi et al.

KNOWLEDGE-BASED SYSTEMS (2020)

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Article Computer Science, Information Systems

A Generic Intelligent Bearing Fault Diagnosis System Using Convolutional Neural Networks With Transfer Learning

Tao Lu et al.

IEEE ACCESS (2020)

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Article Engineering, Mechanical

Residual joint adaptation adversarial network for intelligent transfer fault diagnosis

Jinyang Jiao et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2020)

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Article Computer Science, Information Systems

An Intelligent Fault Diagnosis for Rolling Bearing Based on Adversarial Semi-Supervised Method

Yongchao Zhang et al.

IEEE ACCESS (2020)

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Article Computer Science, Information Systems

Rolling Bearing Fault Diagnosis Using Time-Frequency Analysis and Deep Transfer Convolutional Neural Network

Zhihao Chen et al.

IEEE ACCESS (2020)

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Article Computer Science, Information Systems

Prediction Consistency Guided Convolutional Neural Networks for Cross-Domain Bearing Fault Diagnosis

Songsong Wu et al.

IEEE ACCESS (2020)

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Article Computer Science, Information Systems

Transfer Learning Method Based on Adversarial Domain Adaption for Bearing Fault Diagnosis

Jiajie Shao et al.

IEEE ACCESS (2020)

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Article Automation & Control Systems

Unsupervised Adversarial Adaptation Network for Intelligent Fault Diagnosis

Jinyang Jiao et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2020)

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Article Automation & Control Systems

A Polynomial Kernel Induced Distance Metric to Improve Deep Transfer Learning for Fault Diagnosis of Machines

Bin Yang et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2020)

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Article Computer Science, Information Systems

Normalized Recurrent Dynamic Adaption Network: A New Framework With Dynamic Alignment for Intelligent Fault Diagnosis

Chengdong Zheng et al.

IEEE ACCESS (2020)

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Article Automation & Control Systems

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

Long Wen et al.

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2019)

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Article Automation & Control Systems

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

Xiang Li et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)

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Article Automation & Control Systems

Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning

Siyu Shao et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)

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Article Computer Science, Interdisciplinary Applications

Dynamic condition monitoring for 3D printers by using error fusion of multiple sparse auto-encoders

Shaohui Zhang et al.

COMPUTERS IN INDUSTRY (2019)

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Article Automation & Control Systems

Learning transferable features in deep convolutional neural networks for diagnosing unseen machine conditions

Te Han et al.

ISA TRANSACTIONS (2019)

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Article Engineering, Electrical & Electronic

An Approach for HVCB Mechanical Fault Diagnosis Based on a Deep Belief Network and a Transfer Learning Strategy

Yi Pan et al.

JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY (2019)

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Article Computer Science, Artificial Intelligence

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

Te Han et al.

KNOWLEDGE-BASED SYSTEMS (2019)

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Article Computer Science, Interdisciplinary Applications

Generative adversarial networks for data augmentation in machine fault diagnosis

Siyu Shao et al.

COMPUTERS IN INDUSTRY (2019)

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Article Computer Science, Interdisciplinary Applications

Bearing performance degradation assessment using long short-term memory recurrent network

Bin Zhang et al.

COMPUTERS IN INDUSTRY (2019)

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Article Automation & Control Systems

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

Chuang Sun et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)

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Article Engineering, Multidisciplinary

Acoustic spectral imaging and transfer learning for reliable bearing fault diagnosis under variable speed conditions

Md Junayed Hasan et al.

MEASUREMENT (2019)

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Article Engineering, Mechanical

An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings

Bin Yang et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)

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Article Engineering, Mechanical

Transfer learning with convolutional neural networks for small sample size problem in machinery fault diagnosis

Dengyu Xiao et al.

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE (2019)

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Review Green & Sustainable Science & Technology

Machine learning methods for wind turbine condition monitoring: A review

Adrian Stetco et al.

RENEWABLE ENERGY (2019)

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Article Engineering, Electrical & Electronic

Multi-Layer domain adaptation method for rolling bearing fault diagnosis

Xiang Li et al.

SIGNAL PROCESSING (2019)

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Review Engineering, Electrical & Electronic

Tacholess Speed Estimation in Order Tracking: A Review With Application to Rotating Machine Fault Diagnosis

Siliang Lu et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2019)

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Article Engineering, Multidisciplinary

An intelligent fault diagnosis framework dealing with arbitrary length inputs under different working conditions

Zenghui An et al.

MEASUREMENT SCIENCE AND TECHNOLOGY (2019)

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Article Automation & Control Systems

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

Liang Guo et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)

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Article Automation & Control Systems

A Hierarchical Deep Domain Adaptation Approach for Fault Diagnosis of Power Plant Thermal System

Xiaoxia Wang et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)

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Article Computer Science, Artificial Intelligence

Adversarial Examples: Attacks and Defenses for Deep Learning

Xiaoyong Yu et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2019)

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Article Engineering, Mechanical

Mechanical fault diagnosis using Convolutional Neural Networks and Extreme Learning Machine

Zhuyun Chen et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)

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Article Chemistry, Analytical

Intelligent Detection of a Planetary Gearbox Composite Fault Based on Adaptive Separation and Deep Learning

Guo-dong Sun et al.

SENSORS (2019)

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Article Computer Science, Interdisciplinary Applications

Compound Fault Diagnosis of Gearboxes via Multi-label Convolutional Neural Network and Wavelet Transform

Pengfei Liang et al.

COMPUTERS IN INDUSTRY (2019)

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Article Engineering, Industrial

Digital Twin for rotating machinery fault diagnosis in smart manufacturing

Jinjiang Wang et al.

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2019)

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Article Engineering, Electrical & Electronic

Fault Diagnosis for Electromechanical Drivetrains Using a Joint Distribution Optimal Deep Domain Adaptation Approach

Zhao-Hua Liu et al.

IEEE SENSORS JOURNAL (2019)

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Article Engineering, Mechanical

Deep learning and its applications to machine health monitoring

Rui Zhao et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)

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Article Computer Science, Artificial Intelligence

A survey on Deep Learning based bearing fault diagnosis

Duy-Tang Hoang et al.

NEUROCOMPUTING (2019)

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Article Engineering, Mechanical

Phenomenon-model-based AM-FM vibration mechanism of faulty spur gear

Xiaoqing Yang et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)

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Article Computer Science, Artificial Intelligence

Deep transfer network for rotating machine fault analysis

Weiwei Qian et al.

PATTERN RECOGNITION (2019)

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Article Computer Science, Information Systems

A Cross-Domain Stacked Denoising Autoencoders for Rotating Machinery Fault Diagnosis Under Different Working Conditions

Shan Pang et al.

IEEE ACCESS (2019)

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Review Computer Science, Information Systems

Speech Recognition Using Deep Neural Networks: A Systematic Review

Ali Bou Nassif et al.

IEEE ACCESS (2019)

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Article Computer Science, Information Systems

Deep Learning With Emerging New Labels for Fault Diagnosis

Shaohui Zhang et al.

IEEE ACCESS (2019)

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