☆ 4.7 Article

Novel Joint Transfer Network for Unsupervised Bearing Fault Diagnosis From Simulation Domain to Experimental Domain

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2022)

Related references

Note: Only part of the references are listed.

Article Automation & Control Systems

Deep Adversarial Subdomain Adaptation Network for Intelligent Fault Diagnosis

Yanxu Liu et al.

Summary: In this article, a deep adversarial subdomain adaptation network is proposed to reduce the distribution discrepancy between the source domain and target domain. The effectiveness and superiority of the proposed method are demonstrated through experimental results.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2022)

Add to Collection

Article Automation & Control Systems

Modified Stacked Autoencoder Using Adaptive Morlet Wavelet for Intelligent Fault Diagnosis of Rotating Machinery

Haidong Shao et al.

Summary: In this article, a modified stacked autoencoder (MSAE) that uses adaptive Morlet wavelet is proposed for automatically diagnosing various fault types and severities of rotating machinery. Experimental results show that the proposed method is superior to other state-of-the-art methods.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2022)

Add to Collection

Article Engineering, Industrial

Unsupervised domain-share CNN for machine fault transfer diagnosis from steady speeds to time-varying speeds

Hongru Cao et al.

Summary: This paper proposes an unsupervised domain-share convolutional neural network method for efficient fault transfer diagnosis of machines from steady speeds to time-varying speeds. By improving the efficiency and robustness of feature adaptation and simultaneously extracting domain invariant features from the source domain and target domain, the proposed method aims to improve diagnosis accuracy and transferability.

JOURNAL OF MANUFACTURING SYSTEMS (2022)

Add to Collection

Article Engineering, Multidisciplinary

Modified Gaussian convolutional deep belief network and infrared thermal imaging for intelligent fault diagnosis of rotor-bearing system under time-varying speeds

Li Xin et al.

Summary: This study presents a deep learning method driven by infrared thermal imaging for automatically diagnosing faults of rotating machinery under time-varying speeds. It characterizes working states using infrared thermal imaging, utilizes Gaussian convolutional deep belief network for image processing, and employs a trackable learning rate to enhance performance. The proposed method demonstrates feasibility and outperforms other diagnostic methods.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2022)

Add to Collection

Article Automation & Control Systems

Highly Efficient Fault Diagnosis of Rotating Machinery Under Time-Varying Speeds Using LSISMM and Small Infrared Thermal Images

Xin Li et al.

Summary: This article proposes a new fault diagnosis method using the least square interactive support matrix machine (LSISMM) and infrared thermal images. The LSISMM is constructed as a matrix-form classifier to leverage the structure information of the thermal images, addressing the issues of vibration analysis and computation efficiency in existing methods. Experimental results demonstrate that this method outperforms state-of-the-art methods in terms of diagnosis accuracy and efficiency.

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2022)

Add to Collection

Article Automation & Control Systems

Cross-Domain Open-Set Machinery Fault Diagnosis Based on Adversarial Network With Multiple Auxiliary Classifiers

Jun Zhu et al.

Summary: This article presents a cross-domain open-set transfer diagnosis method that uses domain adversarial model and multiple auxiliary classifiers to identify unknown and known fault categories in the target domain, addressing the issue of different label spaces between training and testing data.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2022)

Add to Collection

Article Automation & Control Systems

Transfer Learning for Remaining Useful Life Prediction Across Operating Conditions Based on Multisource Domain Adaptation

Yifei Ding et al.

Summary: This article proposes a new framework for predicting the remaining useful life of bearings based on a multisource domain adaptation network (MDAN). By learning domain-invariant features and supervision from multiple sources, MDAN achieves better generalization performance. Case studies and comparisons with other methods validate the effectiveness of the proposed approach.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2022)

Add to Collection

Article Automation & Control Systems

Deep Reinforcement Learning-Based Online Domain Adaptation Method for Fault Diagnosis of Rotating Machinery

Guoqiang Li et al.

Summary: This article presents a new fault diagnosis model based on capsule neural network and introduces a novel online domain adaptation learning method using deep reinforcement learning to enhance the adaptivity of the diagnostic model. Experimental results demonstrate that the proposed method outperforms existing popular methods in terms of diagnostic performance and adaptivity.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2022)

Add to Collection

Article Automation & Control Systems

Deep Reinforcement Learning-Based Online Domain Adaptation Method for Fault Diagnosis of Rotating Machinery

Guoqiang Li et al.

Summary: A new fault diagnosis model based on capsule neural network and deep reinforcement learning is proposed to improve adaptivity through online domain adaptation learning, with experimental results showing better diagnostic performance compared to existing methods.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2022)

Add to Collection

Article Automation & Control Systems

Transfer Learning for Remaining Useful Life Prediction Across Operating Conditions Based on Multisource Domain Adaptation

Yifei Ding et al.

Summary: This article introduces a multisource domain adaptation network (MDAN) for improving prognostics and health management of rotating machinery. MDAN effectively utilizes historical data from multiple sources, learns domain-invariant features, and achieves better generalization in the target domain.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2022)

Add to Collection

Article Automation & Control Systems

Universal Domain Adaptation in Fault Diagnostics With Hybrid Weighted Deep Adversarial Learning

Wei Zhang et al.

Summary: This article proposes a universal domain adaptation method for fault diagnosis without assuming the target label set, achieving selective adaptation through source class-wise and target instance-wise weighting mechanism. By using an additional outlier identifier, the method can automatically recognize unknown fault modes while achieving class-level alignments for the shared health states without knowing the target label set.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

Add to Collection

Article Engineering, Multidisciplinary

Simulation data driven weakly supervised adversarial domain adaptation approach for intelligent cross-machine fault diagnosis

Kun Yu et al.

Summary: This study proposes a simulation data-driven domain adaptation method for intelligent fault diagnosis of mechanical equipment. By using diagnostic knowledge learned from simulation data, the healthy mode identification of mechanical equipment can be achieved in the actual field.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2021)

Add to Collection

Article Engineering, Mechanical

Parameter sharing adversarial domain adaptation networks for fault transfer diagnosis of planetary gearboxes

Yi Qin et al.

Summary: This study proposes a parameter sharing adversarial domain adaptation network (PSADAN) to improve transfer diagnosis accuracy by constructing a shared classifier to unify fault classifiers and domain classifiers, simplifying network structure and enhancing domain confusion.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2021)

Add to Collection

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)

Add to Collection

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)

Add to Collection

Article Automation & Control Systems

Intelligent Fault Diagnosis of Rotor-Bearing System Under Varying Working Conditions With Modified Transfer Convolutional Neural Network and Thermal Images

Haidong Shao et al.

Summary: A new framework for fault diagnosis of rotor-bearing system under varying working conditions is proposed using modified CNN and transfer learning. Infrared thermal images are collected to characterize the health condition, and modified CNN is developed with stochastic pooling and Leaky ReLU. The proposed method outperforms other cutting edge methods in fault diagnosis of rotor-bearing system by adapting to limited available training data in different working conditions.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

Add to Collection

Article Engineering, Industrial

Deep multi-scale adversarial network with attention: A novel domain adaptation method for intelligent fault diagnosis*

Bo Zhao et al.

Summary: Data-driven intelligent fault diagnosis methods are widely used in the health management and maintenance decision-making of rotating machinery. However, domain shift phenomena and label information preparation can affect performance. To address these challenges, a novel MSANA framework with multi-scale modules and attention mechanisms is introduced to improve transferability and stability.

JOURNAL OF MANUFACTURING SYSTEMS (2021)

Add to Collection

Article Automation & Control Systems

A New Multiple Source Domain Adaptation Fault Diagnosis Method Between Different Rotating Machines

Jun Zhu et al.

Summary: The fault diagnosis based on data-driven methods is widely researched when supervised samples of the target machine are available, but labeled samples in practical machines are usually scarce. A new transfer learning approach based on multisource domain adaptation is proposed to address this issue, enabling learning from multiple domains for more general diagnosis knowledge that benefits prediction for the target domain.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

Add to Collection

Article Automation & Control Systems

An Investigation Into Fault Diagnosis of Planetary Gearboxes Using A Bispectrum Convolutional Neural Network

Xinyu Pang et al.

Summary: An intelligent fault diagnosis approach based on deep CNNs and vibration BSP is proposed to improve efficiency and accuracy in diagnosing planetary gearboxes. The method achieves high accuracy in identifying various gear faults, with TL further enhancing diagnostic performance. This study contributes to the development of BSP-based CNN models and extensive evaluation of CNN-TL methods for gear monitoring and diagnosis.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2021)

Add to Collection

Article Automation & Control Systems

A Two-Stage Transfer Adversarial Network for Intelligent Fault Diagnosis of Rotating Machinery With Multiple New Faults

Jipu Li et al.

Summary: Research on intelligent fault diagnosis based on deep transfer learning has led to the proposal of a two-stage transfer adversarial network for rotating machinery, which can effectively separate new fault types and recognize the quantity. Experimental results indicate that the proposed scheme can address fault diagnosis transfer tasks with multiple new faults in the target domain.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2021)

Add to Collection

Article Engineering, Electrical & Electronic

Fault Diagnosis of a Rotor-Bearing System Under Variable Rotating Speeds Using Two-Stage Parameter Transfer and Infrared Thermal Images

Haidong Shao et al.

Summary: This article introduces a new method for fault diagnosis of rotor-bearing systems using two-stage parameter transfer and infrared thermal images. The method allows for the use of the same data for diagnosis under different rotating speeds, and the experimental results demonstrate performance improvement and advantages of the proposed method.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)

Add to Collection

Article Engineering, Electrical & Electronic

Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study

Zhibin Zhao et al.

Summary: Recent progress in intelligent fault diagnosis relies heavily on deep representation learning and labeled data. However, the unsupervised deep transfer learning-based IFD still faces many open and fundamental issues.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)

Add to Collection

Article Engineering, Electrical & Electronic

Deep Dynamic Adaptive Transfer Network for Rolling Bearing Fault Diagnosis With Considering Cross-Machine Instance

Yuxuan Zhou et al.

Summary: The research proposes a deep dynamic adaptive transfer network (DDATN) for intelligent fault diagnosis of rolling bearings by utilizing abundant labeled data obtained under laboratory conditions and inspired by transfer learning. With dynamic domain adaptation and transferable features extraction, the proposed DDATN method demonstrates effectiveness in variable working conditions and cross-machine transfer fault diagnosis tasks. Compared with other intelligent fault diagnosis methods, it shows clear advantages.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)

Add to Collection

Article Engineering, Electrical & Electronic

Convolutional Neural Network With Automatic Learning Rate Scheduler for Fault Classification

Long Wen et al.

Summary: The article proposes a CNN with automatic learning rate scheduler for fault classification, which extracts features using LSTM, controls learning rate with a DDPG-based agent, and enhances stability with a double CNN structure. Testing results show that AutoLR-CNN exhibited superior performance in fault classification.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)

Add to Collection

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)

Add to Collection

Article Automation & Control Systems

FEM Simulation-Based Generative Adversarial Networks to Detect Bearing Faults

Yun Gao et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

Add to Collection

Article Automation & Control Systems

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

Xiaoli Zhao et al.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2020)

Add to Collection

Article Automation & Control Systems

Robust Deep Learning-Based Diagnosis of Mixed Faults in Rotating Machinery

Siyuan Chen et al.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2020)

Add to Collection

Article Automation & Control Systems

Unsupervised Adversarial Adaptation Network for Intelligent Fault Diagnosis

Jinyang Jiao et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2020)

Add to Collection

Article Automation & Control Systems

A Two-Stage Approach for the Remaining Useful Life Prediction of Bearings Using Deep Neural Networks

Min Xia et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)

Add to Collection

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)

Add to Collection

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)

Add to Collection

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)

Add to Collection

Article Automation & Control Systems

Electric Locomotive Bearing Fault Diagnosis Using a Novel Convolutional Deep Belief Network

Haidong Shao et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2018)

Add to Collection

Article Engineering, Electrical & Electronic

Vibration-Based Intelligent Fault Diagnosis for Roller Bearings in Low-Speed Rotating Machinery

Liuyang Song et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2018)

Add to Collection

Article Automation & Control Systems

Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks

Min Xia et al.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2018)

Add to Collection

Article Automation & Control Systems

An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data

Yaguo Lei et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2016)

Add to Collection

Article Automation & Control Systems

Multisensor Wireless System for Eccentricity and Bearing Fault Detection in Induction Motors

Ehsan Tarkesh Esfahani et al.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2014)

Add to Collection

© Peeref 2019-2024. All rights reserved.