Multisource domain factorization network for cross-domain fault diagnosis of rotating machinery: An unsupervised multisource domain adaptation method

2022 ◽  
Vol 164 ◽  
pp. 108219
Author(s):  
Yaowei Shi ◽  
Aidong Deng ◽  
Xue Ding ◽  
Shun Zhang ◽  
Shuo Xu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Rotating Machinery ◽  
Cross Domain ◽  
Adaptation Method

scholarly journals Domain Adaptation for Intelligent Fault Diagnosis under Different Working Conditions

2020 ◽  
Vol 319 ◽  
pp. 03001
Author(s):  
Weigui Li ◽  
Zhuqing Yuan ◽  
Wenyu Sun ◽  
Yongpan Liu
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Domain Adaptation ◽  
Transfer Problem ◽  
Intelligent Manufacturing ◽  
Maximum Mean Discrepancy ◽  
Bearing Fault Diagnosis ◽  
Cross Domain ◽  
Conditional Maximum ◽  
Adaptation Method

Recently, deep learning algorithms have been widely into fault diagnosis in the intelligent manufacturing field. To tackle the transfer problem due to various working conditions and insufficient labeled samples, a conditional maximum mean discrepancy (CMMD) based domain adaptation method is proposed. Existing transfer approaches mainly focus on aligning the single representation distributions, which only contains partial feature information. Inspired by the Inception module, multi-representation domain adaptation is introduced to improve classification accuracy and generalization ability for cross-domain bearing fault diagnosis. And CMMD-based method is adopted to minimize the discrepancy between the source and the target. Finally, the unsupervised learning method with unlabeled target data can promote the practical application of the proposed algorithm. According to the experimental results on the standard dataset, the proposed method can effectively alleviate the domain shift problem.

scholarly journals A Novel Domain Adaptation-Based Intelligent Fault Diagnosis Model to Handle Sample Class Imbalanced Problem

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3382
Author(s):  
Zhongwei Zhang ◽  
Mingyu Shao ◽  
Liping Wang ◽  
Sujuan Shao ◽  
Chicheng Ma
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Class Imbalance ◽  
Rolling Bearing ◽  
Industrial Applications ◽  
Mechanical Equipment ◽  
Reliable Operation ◽  
Cross Domain ◽  
Geometric Difference ◽  
Model Generalization

As the key component to transmit power and torque, the fault diagnosis of rotating machinery is crucial to guarantee the reliable operation of mechanical equipment. Regrettably, sample class imbalance is a common phenomenon in industrial applications, which causes large cross-domain distribution discrepancies for domain adaptation (DA) and results in performance degradation for most of the existing mechanical fault diagnosis approaches. To address this issue, a novel DA approach that simultaneously reduces the cross-domain distribution difference and the geometric difference is proposed, which is defined as MRMI. This work contains three parts to improve the sample class imbalance issue: (1) A novel distance metric method (MVD) is proposed and applied to improve the performance of marginal distribution adaptation. (2) Manifold regularization is combined with instance reweighting to simultaneously explore the intrinsic manifold structure and remove irrelevant source-domain samples adaptively. (3) The ℓ2-norm regularization is applied as the data preprocessing tool to improve the model generalization performance. The gear and rolling bearing datasets with class imbalanced samples are applied to validate the reliability of MRMI. According to the fault diagnosis results, MRMI can significantly outperform competitive approaches under the condition of sample class imbalance.

Deep Adversarial Domain Adaptation Method for Cross-Domain Classification in High-Resolution Remote Sensing Images

2019 ◽  
Vol 56 (11) ◽  
pp. 112801
Author(s):  
滕文秀 Wenxiu Teng ◽  
王妮 Ni Wang ◽  
陈泰生 Taisheng Chen ◽  
王本林 Benlin Wang ◽  
陈梦琳 Menglin Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Domain Adaptation ◽  
Remote Sensing Images ◽  
Cross Domain ◽  
Adaptation Method

Feature transfer based adversarial domain adaptation method for cross-domain road extraction

2020 ◽  
pp. 1-11
Author(s):  
Shuyang Wang ◽  
Xiaodong Mu ◽  
Hao He ◽  
Dongfang Yang ◽  
Peng Zhao
Keyword(s):  
Domain Adaptation ◽  
Road Extraction ◽  
Cross Domain ◽  
Adaptation Method

scholarly journals Tensor Transfer Learning for Intelligence Fault Diagnosis of Bearing with Semisupervised Partial Label Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chaofan Hu ◽  
Zhichao Zhou ◽  
Biao Wang ◽  
WeiGuang Zheng ◽  
Shuilong He
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Subspace Learning ◽  
Rotating Machinery ◽  
Training Data ◽  
Tensor Representation ◽  
Vibration Signals ◽  
Tensor Representations ◽  
Partial Label Learning ◽  
And Training

A new tensor transfer approach is proposed for rotating machinery intelligent fault diagnosis with semisupervised partial label learning in this paper. Firstly, the vibration signals are constructed as a three-way tensor via trial, condition, and channel. Secondly, for adapting the source and target domains tensor representations directly, without vectorization, the domain adaptation (DA) approach named tensor-aligned invariant subspace learning (TAISL) is first proposed for tensor representation when testing and training data are drawn from different distribution. Then, semisupervised partial label learning (SSPLL) is first introduced for tackling a problem that it is hard to label a large number of instances and there exists much data left to be unlabeled. Ultimately, the proposed method is used to identify faults. The effectiveness and feasibility of the proposed method has been thoroughly validated by transfer fault experiments. The experimental results show that the presented technique can achieve better performance.

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