☆ 4.6 Article

Deep Joint Distribution Alignment: A Novel Enhanced-Domain Adaptation Mechanism for Fault Transfer Diagnosis

IEEE TRANSACTIONS ON CYBERNETICS (2023)

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 Automation & Control Systems

Intermediate Distribution Alignment and Its Application Into Mechanical Fault Transfer Diagnosis

Yi Qin et al.

Summary: This study proposes a novel domain adaptation mechanism, called Intermediate Distribution Alignment (IDA), to address the issue of dynamically changing aligning targets in existing mechanisms. By building a feature extractor and utilizing KL divergence, IDA can align the prior distributions of two domains and has been successfully applied to fault transfer diagnosis with better performance compared to typical mechanisms.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2022)

添加到收藏夹

Article Automation & Control Systems

Multi-Weight Domain Adversarial Network for Partial-Set Transfer Diagnosis

Jinyang Jiao et al.

Summary: This article discusses domain adaptation technology in intelligent fault diagnosis and proposes a multiweight domain adversarial network (MWDAN) to address the issue of inconsistent label spaces. Extensive experiments demonstrate the outstanding performance of MWDAN.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Duplex adversarial networks for multiple-source domain adaptation

Qiang Zhou et al.

Summary: Domain adaptation is a powerful tool for transferring knowledge from source domains to target tasks, but most existing methods focus on single-source-single-target adaptation settings, ignoring the potential of multiple source domains. This paper introduces a new theoretical generalization bound for multiple source domain adaptation and a new algorithm, DAN_MA, which outperforms other methods on three public datasets by utilizing adversarial networks to learn domain invariant representations and eliminate domain characteristics among different source domains.

KNOWLEDGE-BASED SYSTEMS (2021)

添加到收藏夹

Article Automation & Control Systems

Multiscale Transfer Voting Mechanism: A New Strategy for Domain Adaption

Yi Qin et al.

Summary: Domain adaption models are widely applied in fault transfer diagnosis, but traditional models may face difficulties in capturing domain-invariant information and avoiding overfitting. To address this, a multiscale transfer voting mechanism (MSTVM) was proposed, consisting of MSTM and MTVM strategies to improve performance by enhancing domain confusion and generalization ability through multiple transfer features and classifiers. Through experiments, the advantages of MSTVM in enhancing various domain adaption models were verified.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

添加到收藏夹

Article Automation & Control Systems

A Novel Weighted Adversarial Transfer Network for Partial Domain Fault Diagnosis of Machinery

Weihua Li et al.

Summary: A novel weighted adversarial transfer network (WATN) is proposed for partial domain fault diagnosis, which reduces the distribution discrepancy of shared classes between domains and identifies and filters out irrelevant source examples by introducing adversarial training and a weighting learning strategy. Experiments show that WATN achieves satisfactory performance and outperforms state-of-the-art methods on two diagnosis data sets.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

添加到收藏夹

Article Automation & Control Systems

Automatic Feature Extraction and Construction Using Genetic Programming for Rotating Machinery Fault Diagnosis

Bo Peng et al.

Summary: The article introduces a novel diagnosis approach, AFECGP, based on evolutionary learning for identifying different fault types in rotating machinery. By automatically generating informative features and combining them with k-Nearest Neighbors for fault diagnosis, the proposed approach outperforms competitive methods in accuracy.

IEEE TRANSACTIONS ON CYBERNETICS (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Joint distribution adaptation network with adversarial learning for rolling bearing fault diagnosis

Ke Zhao et al.

Summary: This paper developed a joint distribution adaptation network with adversarial learning to address fault diagnosis challenges. By constructing a deep convolutional neural network to extract features, and using an improved joint maximum mean discrepancy and adversarial domain adaptation to match feature distributions and extract features, precise distribution matching and domain-invariant features extraction were achieved.

KNOWLEDGE-BASED SYSTEMS (2021)

添加到收藏夹

Article Engineering, Multidisciplinary

A new deep transfer learning network based on convolutional auto-encoder for mechanical fault diagnosis

Quan Qian et al.

Summary: The paper introduces a new deep transfer learning network, CAE-DTLN, which incorporates feature extraction, CORAL loss, and domain classification to achieve high diagnostic accuracy and anti-noise performance in mechanical fault diagnosis without labeled data.

MEASUREMENT (2021)

添加到收藏夹

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)

添加到收藏夹

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)

添加到收藏夹

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)

添加到收藏夹

Article Automation & Control Systems

High-Voltage Circuit Breaker Fault Diagnosis Using a Hybrid Feature Transformation Approach Based on Random Forest and Stacked Autoencoder

Suliang Ma et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)

添加到收藏夹

Review Engineering, Mechanical

Artificial intelligence for fault diagnosis of rotating machinery: A review

Ruonan Liu et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2018)

添加到收藏夹

Review Engineering, Mechanical

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

Mariela Cerrada et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2018)

添加到收藏夹

Article Engineering, Manufacturing

A Convolutional Neural Network for Fault Classification and Diagnosis in Semiconductor Manufacturing Processes

Ki Bum Lee et al.

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING (2017)

添加到收藏夹

Article Acoustics

Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings

David Verstraete et al.

SHOCK AND VIBRATION (2017)

添加到收藏夹

Article Computer Science, Artificial Intelligence

EEMD method and WNN for fault diagnosis of locomotive roller bearings

Yaguo Lei et al.

EXPERT SYSTEMS WITH APPLICATIONS (2011)

添加到收藏夹

Article Engineering, Mechanical

Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform

V. K. Rai et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2007)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.