4.8 Article

Cross-Machine Transfer Fault Diagnosis by Ensemble Weighting Subdomain Adaptation Network

Related references

Note: Only part of the references are listed.
Article Automation & Control Systems

A Multisource Domain Adaptation Network for Process Fault Diagnosis Under Different Working Conditions

Shijin Li et al.

Summary: Transfer learning-based process fault diagnosis has been widely studied, but there is still a challenge in handling multisource domain adaptation under various working conditions. This article proposes a novel transfer learning model, FC-MSDA, for process fault diagnosis. It introduces a common feature extractor, a feature selection module, domain specific feature generators, and a class-level distribution alignment loss to address the challenges. The experimental results demonstrate the effectiveness of FC-MSDA in process fault diagnosis.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2023)

Article Computer Science, Artificial Intelligence

Globally Localized Multisource Domain Adaptation for Cross-Domain Fault Diagnosis With Category Shift

Yong Feng et al.

Summary: This paper proposes a globally localized multisource domain adaptation method with category shift for cross-domain fault diagnosis. By constructing a GlocalNet, which consists of a feature generator and three classifiers, multisource information is comprehensively fused. The Wasserstein discrepancy of classifiers is optimized locally and accumulative higher order multisource moment is used globally to achieve multisource domain adaptation at domain and class levels, thus reducing the shift on domain and category. A distilling strategy is presented to refine the classifier at sample level, and an adaptive weighting policy is employed for reliable result.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2023)

Article Automation & Control Systems

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

Yi Qin et al.

Summary: This study proposes a new domain adaptation mechanism called deep joint distribution alignment (DJDA) to simultaneously reduce the discrepancy in marginal and conditional distributions between source and target domains. By aligning the means and covariances and using a Gaussian mixture model and statistical metric to reduce distribution discrepancy, DJDA can achieve the highest degree of domain confusion. Experimental results demonstrate that DJDA outperforms other typical domain adaptation models in fault transfer diagnosis.

IEEE TRANSACTIONS ON CYBERNETICS (2023)

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)

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)

Article Engineering, Mechanical

Moment matching-based intraclass multisource domain adaptation network for bearing fault diagnosis

Yu Xia et al.

Summary: This study introduces a deep learning-based fault diagnosis method that utilizes multi-source transfer learning to address the issues of insufficient labels and different distributions in data.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2022)

Article Automation & Control Systems

Partial Transfer Fault Diagnosis by Multiscale Weight-Selection Adversarial Network

Quan Qian et al.

Summary: In this study, a novel multiscale weight-selection adversarial network (MWSAN) is proposed to enhance the effect of partial domain adaptation (DA). By using a designed multiscale domain adversarial network (MDAN) and a multiscale weight-selection mechanism for instance and class, the proposed method can achieve partial DA to a larger degree.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2022)

Article Automation & Control Systems

Multisource Partial Transfer Network for Machinery Fault Diagnostics

Yaoxiang Yu et al.

Summary: In real industrial applications, obtaining massive labeled data for fault diagnosis of machineries is difficult. Therefore, transfer learning is introduced to apply knowledge learned from labeled datasets to unlabeled data. However, there are challenges such as unknown label space, limited fault types in labeled datasets, and difficulty in applying ideal datasets to real data. To solve these problems, a new transfer learning model called multisource partial transfer network is proposed, which consists of a common module and three domain-specific modules for feature extraction, fault diagnosis, and domain adaptation.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2022)

Article Automation & Control Systems

Towards Prediction Constraints: A Novel Domain Adaptation Method for Machine Fault Diagnosis

Jinyang Jiao et al.

Summary: This article introduces a domain adaptation method for intelligent fault diagnosis of machinery, which uses minimum class confusion and maximum nuclear norm-based constraints to improve accurate diagnosis results.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2022)

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)

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)

Article Automation & Control Systems

Intelligent Fault Diagnosis by Fusing Domain Adversarial Training and Maximum Mean Discrepancy via Ensemble Learning

Yibin Li et al.

Summary: The article proposes an intelligent fault diagnosis method based on an improved domain adaptation method, utilizing training feature extractors and ensemble learning for industrial equipment health monitoring, effectively addressing domain mismatch issues.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

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)

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)

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)

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)

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)