☆ 4.6 Article

An Intelligent Machinery Fault Diagnosis Method Based on GAN and Transfer Learning under Variable Working Conditions

SENSORS (2022)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Chemistry, Analytical

A Fuzzy Fusion Rotating Machinery Fault Diagnosis Framework Based on the Enhancement Deep Convolutional Neural Networks

Daoguang Yang et al.

Summary: This paper utilizes the advantages of artificial intelligence algorithms and deep learning algorithms in rotating machinery fault diagnosis. By processing the signals in different ways, a feature extraction model is proposed, and a fuzzy fusion strategy is used to analyze the importance of classifiers and explore the interaction index.

SENSORS (2022)

添加到收藏夹

Article Engineering, Mechanical

Model Parameter Transfer for Gear Fault Diagnosis under Varying Working Conditions

Chao Chen et al.

Summary: The paper introduces a novel model parameter transfer (NMPT) method to enhance gear fault diagnosis performance under varying working conditions. By combining multi-task learning with transfer learning, NMPT aims to transfer knowledge from source domain samples to assist the target gear fault diagnosis task, improving performance when labeled samples are insufficient.

CHINESE JOURNAL OF MECHANICAL ENGINEERING (2021)

添加到收藏夹

Article Engineering, Mechanical

Knowledge mapping-based adversarial domain adaptation: A novel fault diagnosis method with high generalizability under variable working conditions

Qi Li et al.

Summary: The study proposes a knowledge mapping-based adversarial domain adaptation (KMADA) method, utilizing a discriminator and feature extractor to learn knowledge from the target domain. KMADA fully utilizes parameters obtained from supervised pre-training to accelerate the adversarial training process, achieving the highest diagnosis accuracy compared to other TL methods.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance

Haidong Shao et al.

Summary: Collaborative fault diagnosis can benefit from multisensory fusion technologies, which offer more reliable results with a comprehensive data set. By utilizing a stacked wavelet auto-encoder structure and flexible weighted fusion strategies, the proposed approach overcomes obstacles in integrating multisensory data and fusion of maintenance strategies, providing more accurate fault diagnosis results.

INFORMATION FUSION (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Rolling bearing fault diagnosis using optimal ensemble deep transfer network

Xingqiu Li et al.

Summary: The paper proposes an optimal ensemble deep transfer network (OEDTN) for rolling bearing fault diagnosis, which combines parameter transfer learning, domain adaptation, and ensemble learning to achieve better performance. Experimental results show that OEDTN is more effective than existing methods in diagnosing bearing faults.

KNOWLEDGE-BASED SYSTEMS (2021)

添加到收藏夹

Article Chemistry, Analytical

Rotating Machinery Fault Diagnosis Method by Combining Time-Frequency Domain Features and CNN Knowledge Transfer

Lihao Ye et al.

Summary: This paper proposes a fault diagnosis method based on knowledge transfer in deep learning, which improves the correct rate of fault diagnosis by converting the source domain model into a shallow model suitable for small samples and training it, achieving knowledge transfer from expert system to deep CNN.

SENSORS (2021)

添加到收藏夹

Article Acoustics

Improving phoneme recognition of throat microphone speech recordings using transfer learning

M. A. Tugtekin Turan et al.

Summary: This study addresses the challenge of phoneme recognition for throat microphone (TM) speech recordings by proposing a transfer learning approach based on stacked denoising auto-encoders (SDA), which showed significant improvement in phoneme error rate compared to baseline performances.

SPEECH COMMUNICATION (2021)

添加到收藏夹

Article Engineering, Multidisciplinary

GMC sparse enhancement diagnostic method based on the tunable Q-factor wavelet transform for detecting faults in rotating machines

Wangpeng He et al.

Summary: A novel sparsity-enhanced signal denoising method is proposed to enhance feature extraction performance in mechanical fault diagnosis. The method aims at matching transient fault features, constructs an effective sparse optimization objective function, ensures overall convexity with non-convex controllable parameters, and solves the optimization problem using the proximal algorithm. Results from simulation and practical fault experiment confirm the effectiveness of the proposed method in machinery fault diagnosis.

MEASUREMENT (2021)

添加到收藏夹

Article Engineering, Multidisciplinary

A new bearing fault diagnosis method based on signal-to-image mapping and convolutional neural network

Jing Zhao et al.

Summary: In this paper, a fault feature extraction method based on deep learning is proposed, utilizing data augmentation and signal-to-image mapping techniques. A convolutional neural network model is established to extract fault features and achieve fault classification.

MEASUREMENT (2021)

添加到收藏夹

Article Automation & Control Systems

A multi-rate sampling data fusion method for fault diagnosis and its industrial applications

Keke Huang et al.

Summary: The proposed novel multi-rate sampling data fusion method for fault diagnosis uses convolutional neural network and long short-term memory network to automatically learn features and encode temporal information, achieving satisfactory diagnostic accuracy in fault diagnosis.

JOURNAL OF PROCESS CONTROL (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Residual wide-kernel deep convolutional auto-encoder for intelligent rotating machinery fault diagnosis with limited samples

Daoguang Yang et al.

Summary: This paper introduces a novel deep learning framework for highly accurate rotating machinery fault diagnosis using a residual wide-kernel deep convolutional auto-encoder, which can learn important features from limited raw vibration signals. The model ensures depth without gradient vanishing and overfitting issues, and demonstrates denoising and feature extraction ability with very few training samples.

NEURAL NETWORKS (2021)

添加到收藏夹

Article Computer Science, Information Systems

Deep Convolutional Neural Network Using Transfer Learning for Fault Diagnosis

Dong Zhang et al.

Summary: This paper proposes a new deep convolutional neural network (DCNN) and transfer learning (TL) combined approach for fault diagnosis in industrial systems to handle different fault types. By converting one-dimensional time-series signals into grayscale images using a signal processing method and training an optimal DCNN with ImageNet datasets, this method shows improved generalization performance through TL.

IEEE ACCESS (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Deep learning fault diagnosis method based on global optimization GAN for unbalanced data

Funa Zhou et al.

KNOWLEDGE-BASED SYSTEMS (2020)

添加到收藏夹

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)

添加到收藏夹

Article Automation & Control Systems

Intelligent Fault Identification Based on Multisource Domain Generalization Towards Actual Diagnosis Scenario

Huailiang Zheng et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2020)

添加到收藏夹

Article Engineering, Multidisciplinary

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

He Zhiyi et al.

MEASUREMENT (2020)

添加到收藏夹

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)

添加到收藏夹

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)

添加到收藏夹

Article Computer Science, Artificial Intelligence

EEG based multi-class seizure type classification using convolutional neural network and transfer learning

S. Raghu et al.

NEURAL NETWORKS (2020)

添加到收藏夹

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)

添加到收藏夹

Article Engineering, Electrical & Electronic

Knowledge Transfer for Rotary Machine Fault Diagnosis

Ruqiang Yan et al.

IEEE SENSORS JOURNAL (2020)

添加到收藏夹

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)

添加到收藏夹

Article Computer Science, Information Systems

Rolling Bearing Fault Diagnosis Based on Convolutional Neural Network and Support Vector Machine

Laohu Yuan et al.

IEEE ACCESS (2020)

添加到收藏夹

Article Chemistry, Analytical

Bearing Fault Diagnosis Method Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning

Gaowei Xu et al.

SENSORS (2019)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Deep transfer network for rotating machine fault analysis

Weiwei Qian et al.

PATTERN RECOGNITION (2019)

添加到收藏夹

Article Computer Science, Information Systems

A Deep Transfer Model With Wasserstein Distance Guided Multi-Adversarial Networks for Bearing Fault Diagnosis Under Different Working Conditions

Ming Zhang et al.

IEEE ACCESS (2019)

添加到收藏夹

Article Automation & Control Systems

Sparsity-based signal extraction using dual Q-factors for gearbox fault detection

Wangpeng He et al.

ISA TRANSACTIONS (2018)

添加到收藏夹

Article Computer Science, Information Systems

Intelligent Fault Diagnosis Under Varying Working Conditions Based on Domain Adaptive Convolutional Neural Networks

Bo Zhang et al.

IEEE ACCESS (2018)

添加到收藏夹

Review Engineering, Mechanical

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

Wade A. Smith et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2015)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A Survey on Transfer Learning

Sinno Jialin Pan et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2010)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.