Готові списки джерел за темами / Cross-domain fault diagnosis

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Добірка наукової літератури з теми "Cross-domain fault diagnosis"

Автор: Grafiati
Опубліковано: 19 жовтня 2024

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Статті в журналах з теми "Cross-domain fault diagnosis"

1

Wang, Xiaodong, Feng Liu, and Dongdong Zhao. "Cross-Machine Fault Diagnosis with Semi-Supervised Discriminative Adversarial Domain Adaptation." Sensors 20, no. 13 (July 4, 2020): 3753. http://dx.doi.org/10.3390/s20133753.

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Анотація:
Bearings are ubiquitous in rotating machinery and bearings in good working conditions are essential for the availability and safety of the machine. Various intelligent fault diagnosis models have been widely studied aiming to prevent system failures. These data-driven fault diagnosis models work well when training data and testing data are from the same distribution, which is not easy to sustain in industry since the working environment of rotating machinery is often subject to change. Recently, the domain adaptation methods for fault diagnosis between different working conditions have been extensively researched, which fully utilize the labeled data from the same machine under different working conditions to address this domain shift diploma. However, for a target machine with seldom occurred faulty data under any working conditions, the domain adaptation approaches between working conditions are not applicable. Hence, the cross-machine fault diagnosis tasks are recently proposed to utilize the labeled data from related but not identical machines. The larger domain shift between machines makes the cross-machine fault diagnosis a more challenging task. The large domain shift may cause the well-trained model on source domain deteriorates on target domain, and the ambiguous samples near the decision boundary are prone to be misclassified. In addition, the sparse faulty samples in target domain make a class-imbalanced scenario. To address the two issues, in this paper we propose a semi-supervised adversarial domain adaptation approach for cross-machine fault diagnosis which incorporates the virtual adversarial training and batch nuclear-norm maximization to make the fault diagnosis robust and discriminative. Experiments of transferring between three bearing datasets show that the proposed method is able to effectively learn a discriminative model given only a labeled faulty sample of each class in target domain. The research provides a feasible approach for knowledge transfer in fault diagnosis scenarios.
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2

Zhang, Yongchao, Zhaohui Ren, and Shihua Zhou. "A New Deep Convolutional Domain Adaptation Network for Bearing Fault Diagnosis under Different Working Conditions." Shock and Vibration 2020 (July 24, 2020): 1–14. http://dx.doi.org/10.1155/2020/8850976.

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Анотація:
Effective fault diagnosis methods can ensure the safe and reliable operation of the machines. In recent years, deep learning technology has been applied to diagnose various mechanical equipment faults. However, in real industries, the data distribution under different working conditions is often different, which leads to serious degradation of diagnostic performance. In order to solve the issue, this study proposes a new deep convolutional domain adaptation network (DCDAN) method for bearing fault diagnosis. This method implements cross-domain fault diagnosis by using the labeled source domain data and the unlabeled target domain data as training data. In DCDAN, firstly, a convolutional neural network is applied to extract features of source domain data and target domain data. Then, the domain distribution discrepancy is reduced through minimizing probability distribution distance of multiple kernel maximum mean discrepancies (MK-MMD) and maximizing the domain recognition error of domain classifier. Finally, the source domain classification error is minimized. Extensive experiments on two rolling bearing datasets verify that the proposed method can implement accurate cross-domain fault diagnosis under different working conditions. The study may provide a promising tool for bearing fault diagnosis under different working conditions.
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3

Meng, Yu, Jianping Xuan, Long Xu, and Jie Liu. "Dynamic Reweighted Domain Adaption for Cross-Domain Bearing Fault Diagnosis." Machines 10, no. 4 (March 30, 2022): 245. http://dx.doi.org/10.3390/machines10040245.

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Анотація:
Certain progress has been made in fault diagnosis under cross-domain scenarios recently. Most researchers have paid almost all their attention to promoting domain adaptation in a common space. However, several challenges that will cause negative transfer have been ignored. In this paper, a reweighting method is proposed to overcome this difficulty from two aspects. First, extracted features differ greatly from one another in promoting positive transfer, and measuring the difference is important. Measured by conditional entropy, the weight of adversarial losses for those well aligned features are reduced. Second, the balance between domain adaptation and class discrimination greatly influences the transferring task. Here, a dynamic weight strategy is adopted to compute the balance factor. Consideration is made from the perspective of maximum mean discrepancy and multiclass linear discriminant analysis. The first item is supposed to measure the degree of the domain adaptation between source and the target domain, and the second is supposed to show the classification performance of the classifier on the learned features in the current training epoch. Finally, extensive experiments on several bearing fault diagnosis datasets are conducted. The performance shows that our model has an obvious advantage compared with other common transferring algorithms.
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4

Chang, Hong-Chan, Ren-Ge Liu, Chen-Cheng Li, and Cheng-Chien Kuo. "Fault Diagnosis of Induction Motors under Limited Data for across Loading by Residual VGG-Based Siamese Network." Applied Sciences 14, no. 19 (October 4, 2024): 8949. http://dx.doi.org/10.3390/app14198949.

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Анотація:
This study proposes an improved few-shot learning model of the Siamese network residual Visual Geometry Group (VGG). This model combined with time–frequency domain transformation techniques effectively enhances the performance of across-load fault diagnosis for induction motors with limited data conditions. The proposed residual VGG-based Siamese network consists of two primary components: the feature extraction network, which is the residual VGG, and the merged similarity layer. First, the residual VGG architecture utilizes residual learning to boost learning efficiency and mitigate the degradation problem typically associated with deep neural networks. The employment of smaller convolutional kernels substantially reduces the number of model parameters, expedites model convergence, and curtails overfitting. Second, the merged similarity layer incorporates multiple distance metrics for similarity measurement to enhance classification performance. For cross-domain fault diagnosis in induction motors, we developed experimental models representing four common types of faults. We measured the vibration signals from both healthy and faulty models under varying loads. We then applied the proposed model to evaluate and compare its effectiveness in cross-domain fault diagnosis against conventional AI models. Experimental results indicate that when the imbalance ratio reached 20:1, the average accuracy of the proposed residual VGG-based Siamese network for fault diagnosis across different loads was 98%, closely matching the accuracy of balanced and sufficient datasets, and significantly surpassing the diagnostic performance of other models.
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Li, Dan, Yudong Xu, Yuxun Zhou, Chao Gou, and See-Kiong Ng. "Cross Domain Data Generation for Smart Building Fault Detection and Diagnosis." Mathematics 10, no. 21 (October 26, 2022): 3970. http://dx.doi.org/10.3390/math10213970.

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Анотація:
Benefiting extensively from the Internet of Things (IoT) and sensor network technologies, the modern smart building achieves thermal comfort. It prevents energy wastage by performing automatic Fault Detection and Diagnosis (FDD) to maintain the good condition of its air-conditioning systems. Often, real-time multi-sensor measurements are collected, and supervised learning algorithms are adopted to exploit the data for an effective FDD. A key issue with the supervised methods is their dependence on well-labeled fault data, which is difficult to obtain in many real-world scenarios despite the abundance of unlabelled sensor data. Intuitively, the problem can be greatly alleviated if some well-labeled fault data collected under a particular setting can be re-used and transferred to other cases where labeled fault data is challenging or costly. Bearing this idea, we proposed a novel Adversarial Cross domain Data Generation (ACDG) framework to impute missing fault data for building fault detection and diagnosis where labeled data is costly. Unlike traditional Transfer Learning (TL)-related applications that adapt models or features learned in the source domain to the target domain, ACDG essentially “generates” the unknown sensor data for the target setting (target domain). This is accomplished by capturing the data patterns and common knowledge from known counterparts in the other setting (source domain), the inter-domain knowledge, and the intra-domain relations. The proposed ACDG framework is tested with the real-world Air Handling Unit (AHU) fault dataset of the ASHRAE Research Project 1312. Extensive experimental results on the cross-domain AHU fault data showed the effectiveness of ACDG in supplementing the data for a missing fault category by exploiting the underlying commonalities between different domain settings.
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6

Wang, Yuanfei, Shihao Li, Feng Jia, and Jianjun Shen. "Multi-Domain Weighted Transfer Adversarial Network for the Cross-Domain Intelligent Fault Diagnosis of Bearings." Machines 10, no. 5 (April 29, 2022): 326. http://dx.doi.org/10.3390/machines10050326.

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Анотація:
Transfer learning is a topic that has attracted attention for the intelligent fault diagnosis of bearings since it addresses bearing datasets that have different distributions. However, the traditional intelligent fault diagnosis methods based on transfer learning have the following two shortcomings. (1) The multi-mode structure characteristics of bearing datasets are neglected. (2) Some local regions of the bearing signals may not be suitable for transfer due to signal fluctuation. Therefore, a multi-domain weighted adversarial transfer network is proposed for the cross-domain intelligent fault diagnosis of bearings. In the proposed method, multi-domain adversarial and attention weighting modules are designed to consider bearing multi-mode structure characteristics and solve the influence of local non-transferability regions of signals, respectively. Two diagnosis cases are used to verify the proposed method. The results show that the proposed method is able to extract domain invariant features for different cross-domain diagnosis cases, and thus improves the accuracy of fault identification.
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7

Zhang, Long, Hao Zhang, Qian Xiao, Lijuan Zhao, Yanqing Hu, Haoyang Liu, and Yu Qiao. "Numerical Model Driving Multi-Domain Information Transfer Method for Bearing Fault Diagnosis." Sensors 22, no. 24 (December 13, 2022): 9759. http://dx.doi.org/10.3390/s22249759.

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Анотація:
Given the complexity of the application scenarios of rolling bearing and the severe scarcity of fault samples, a solution to the issue of fault diagnosis under varying working conditions along with the absence of fault samples is required. A numerical model-driven cross-domain fault diagnosis method targeting variable working conditions is proposed based on the cross-Domain Nuisance Attribute Projection (cDNAP). Firstly, the simulation datasets consisting of multiple fault types under variable working conditions are constructed to solve the problem of incomplete fault samples. Secondly, the simulation datasets are expanded by means of generating adversarial network to ensure sufficient samples for subsequent model training. Finally, cDNAP is used to obtain the cross-domain simulation projection matrix, which eliminates the variance in the distribution of measured and simulated sample features under varying working conditions. The experimental results of cross-domain for variable working conditions show that the diagnostic accuracy reaches up to 99%. Compared with DANN, DSAN, and DAAN domain adversarial neural networks, the proposed method performs better in bearing fault diagnosis.
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8

Jang, Gye-Bong, and Sung-Bae Cho. "Cross-Domain Adaptation Using Domain Interpolation for Rotating Machinery Fault Diagnosis." IEEE Transactions on Instrumentation and Measurement 71 (2022): 1–17. http://dx.doi.org/10.1109/tim.2022.3204093.

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9

Shang, Qianming, Tianyao Jin, and Mingsheng Chen. "A New Cross-Domain Motor Fault Diagnosis Method Based on Bimodal Inputs." Journal of Marine Science and Engineering 12, no. 8 (August 1, 2024): 1304. http://dx.doi.org/10.3390/jmse12081304.

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Анотація:
Electric motors are indispensable electrical equipment in ships, with a wide range of applications. They can serve as auxiliary devices for propulsion, such as air compressors, anchor winches, and pumps, and are also used in propulsion systems; ensuring the safe and reliable operation of motors is crucial for ships. Existing deep learning methods typically target motors under a specific operating state and are susceptible to noise during feature extraction. To address these issues, this paper proposes a Resformer model based on bimodal input. First, vibration signals are transformed into time–frequency diagrams using continuous wavelet transform (CWT), and three-phase current signals are converted into Park vector modulus (PVM) signals through Park transformation. The time–frequency diagrams and PVM signals are then aligned in the time sequence to be used as bimodal input samples. The analysis of time–frequency images and PVM signals indicates that the same fault condition under different loads but at the same speed exhibits certain similarities. Therefore, data from the same fault condition under different loads but at the same speed are combined for cross-domain motor fault diagnosis. The proposed Resformer model combines the powerful spatial feature extraction capabilities of the Swin-t model with the excellent fine feature extraction and efficient training performance of the ResNet model. Experimental results show that the Resformer model can effectively diagnose cross-domain motor faults and maintains performance even under different noise conditions. Compared with single-modal models (VGG-11, ResNet, ResNeXt, and Swin-t), dual-modal models (MLP-Transformer and LSTM-Transformer), and other large models (Swin-s, Swin-b, and VGG-19), the Resformer model exhibits superior overall performance. This validates the method’s effectiveness and accuracy in the intelligent recognition of common cross-domain motor faults.
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Wang, Huaqing, Zhitao Xu, Xingwei Tong, and Liuyang Song. "Cross-Domain Open Set Fault Diagnosis Based on Weighted Domain Adaptation with Double Classifiers." Sensors 23, no. 4 (February 14, 2023): 2137. http://dx.doi.org/10.3390/s23042137.

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Анотація:
The application of transfer learning in fault diagnosis has been developed in recent years. It can use existing data to solve the problem of fault recognition under different working conditions. Due to the complexity of the equipment and the openness of the working environment in industrial production, the status of the equipment is changeable, and the collected signals can have new fault classes. Therefore, the open set recognition ability of the transfer learning method is an urgent research direction. The existing transfer learning model can have a severe negative transfer problem when solving the open set problem, resulting in the aliasing of samples in the feature space and the inability to separate the unknown classes. To solve this problem, we propose a Weighted Domain Adaptation with Double Classifiers (WDADC) method. Specifically, WDADC designs the weighting module based on Jensen–Shannon divergence, which can evaluate the similarity between each sample in the target domain and each class in the source domain. Based on this similarity, a weighted loss is constructed to promote the positive transfer between shared classes in the two domains to realize the recognition of shared classes and the separation of unknown classes. In addition, the structure of double classifiers in WDADC can mitigate the overfitting of the model by maximizing the discrepancy, which helps extract the domain-invariant and class-separable features of the samples when the discrepancy between the two domains is large. The model’s performance is verified in several fault datasets of rotating machinery. The results show that the method is effective in open set fault diagnosis and superior to the common domain adaptation methods.
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Більше джерел

Дисертації з теми "Cross-domain fault diagnosis"

1

Ainapure, Abhijeet Narhar. "Application and Performance Enhancement of Intelligent Cross-Domain Fault Diagnosis in Rotating Machinery." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1623164772153736.

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Fernandes, Montesuma Eduardo. "Multi-Source Domain Adaptation through Wasserstein Barycenters." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG045.

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Les systèmes d'apprentissage automatique fonctionnent sous l'hypothèse que les conditions d'entraînement et de test ne changent pas. Néanmoins, cette hypothèse est rarement vérifiée en pratique. En conséquence, le système est entraîné avec des données qui ne sont plus représentatives des données sur lesquelles il sera testé : la mesure de probabilité des données évolue entre les périodes d'entraînement et de test. Ce scénario est connu dans la littérature sous le nom de décalage de distribution entre deux domaines : une source et une cible. Une généralisation évidente de ce problème considère que les données d'entraînement présentent elles-mêmes plusieurs décalages intrinsèques. On parle, donc, d'adaptation de domaine à sources multiples (MSDA). Dans ce contexte, le transport optimal est un outil de mathématique utile. En particulier, qui sert pour comparer et manipuler des mesures de probabilité. Cette thèse étudie les contributions du transport optimal à l'adaptation de domaines à sources multiples. Nous le faisons à travers des barycentres de Wasserstein, un objet qui définit une moyenne pondérée, dans l'espace des mesures de probabilité, des multiples domaines en MSDA. Basé sur ce concept, nous proposons : (i) une nouvelle notion de barycentre lorsque les mesures en question sont étiquetées, (ii) un nouveau problème d'apprentissage de dictionnaire sur des mesures de probabilité empiriques et (iii) de nouveaux outils pour l'adaptation de domaines via le transport optimal de modèles de mélanges Gaussiens. Nos méthodes améliorent les performances de l'adaptation de domaines par rapport aux méthodes existantes utilisant le transport optimal sur des benchmarks d'images et de diagnostic de défauts inter-domaines. Notre travail ouvre une perspective de recherche intéressante sur l'apprentissage de l'enveloppe barycentrique de mesures de probabilité
Machine learning systems work under the assumption that training and test conditions are uniform, i.e., they do not change. However, this hypothesis is seldom met in practice. Hence, the system is trained with data that is no longer representative of the data it will be tested on. This case is represented by a shift in the probability measure generating the data. This scenario is known in the literature as distributional shift between two domains: a source, and a target. A straightforward generalization of this problem is when training data itself exhibit shifts on its own. In this case, one consider Multi Source Domain Adaptation (MSDA). In this context, optimal transport is an useful field of mathematics. Especially, optimal transport serves as a toolbox, for comparing and manipulating probability measures. This thesis studies the contributions of optimal transport to multi-source domain adaptation. We do so through Wasserstein barycenters, an object that defines a weighted average, in the space of probability measures, for the multiple domains in MSDA. Based on this concept, we propose: (i) a novel notion of barycenter, when the measures at hand are equipped with labels, (ii) a novel dictionary learning problem over empirical probability measures and (iii) new tools for domain adaptation through the optimal transport of Gaussian mixture models. Through our methods, we are able to improve domain adaptation performance in comparison with previous optimal transport-based methods on image, and cross-domain fault diagnosis benchmarks. Our work opens an interesting research direction, on learning the barycentric hull of probability measures
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Частини книг з теми "Cross-domain fault diagnosis"

1

Lu, Weikai, Jian Chen, Hao Zheng, Haoyi Fan, Eng Yee Wei, Xinrong Cao, and Deyang Zhang. "Domain Adversarial Interaction Network for Cross-Domain Fault Diagnosis." In Machine Learning for Cyber Security, 436–46. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-20099-1_37.

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Ping, Mingtian, Dechang Pi, Zhiwei Chen, and Junlong Wang. "Cross-Domain Bearing Fault Diagnosis Method Using Hierarchical Pseudo Labels." In Neural Information Processing, 32–43. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8076-5_3.

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3

Huang, Zhe, Qing Lan, Mingxuan Li, Zhihui Wen, and Wangpeng He. "A Multi-scale Feature Adaptation ConvNeXt for Cross-Domain Fault Diagnosis." In Communications in Computer and Information Science, 339–53. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-7007-6_24.

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4

Shao, Haidong, Jian Lin, Zhishan Min, Jingjie Luo, and Haoxuan Dou. "Scalable Metric Meta-learning for Cross-domain Fault Diagnosis of Planetary Gearbox Using Few Samples." In Lecture Notes in Electrical Engineering, 865–72. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6901-0_89.

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5

Zhang, Fan, Pei Lai, Qichen Wang, Tianrui Li, and Weihua Zhang. "TCRNN: A Cross-domain Knowledge Transfer Acoustic Bearing Fault Diagnosis Method for Data Unbalance Issue." In Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023), 921–33. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-49421-5_76.

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6

Qin, Ruoshi, and Jinsong Zhao. "Cross-domain Fault Diagnosis for Chemical Processes through Dynamic Adversarial Adaptation Network." In Computer Aided Chemical Engineering, 867–73. Elsevier, 2023. http://dx.doi.org/10.1016/b978-0-443-15274-0.50139-6.

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Тези доповідей конференцій з теми "Cross-domain fault diagnosis"

1

Zhao, Yue, Guorong Fan, Yuxing Cao, Yong Yang, Wenhua Gao, and Zengshou Dong. "A cross domain deep learning method for rolling bearing fault diagnosis." In 2024 43rd Chinese Control Conference (CCC), 4969–74. IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662230.

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2

Chen, Zhi, Yajie Ma, Bin Jiang, and Zehui Mao. "A Joint Adaptation Conditional Adversarial Network for Rolling Bearing Cross-domain Fault Diagnosis." In 2024 43rd Chinese Control Conference (CCC), 4906–11. IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10661382.

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3

Xie, Zongliang, and Jinglong Chen. "Multi-Scale Attention Convolution Subdomain Adaption Network for Cross-Domain Fault Diagnosis of Machine." In 2024 Prognostics and System Health Management Conference (PHM), 153–58. IEEE, 2024. http://dx.doi.org/10.1109/phm61473.2024.00037.

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4

Shen, Pengfei, Fengrong Bi, Daijie Tang, Xiao Yang, Meng Huang, Mingzhi Guo, and Xiaoyang Bi. "Cross-Domain Fault Diagnosis of Powertrain System using Sparse Representation." In WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2023. http://dx.doi.org/10.4271/2023-01-0420.

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Анотація:
<div class="section abstract"><div class="htmlview paragraph">Although excellent progress has been made recently in powertrain fault diagnosis based on vibration signals, most of them are based on the assumption that the fault features of the training and test data are drawn from the same probability distribution. Due to the limitation of the domain shift phenomenon, the performance of the current intelligent fault diagnosis methods is significantly reduced. Even many existing transfer learning methods have the problem of low generalization ability. Inspired by sparse representation theory, a novel cross-domain fault diagnosis method based on K-means singular value decomposition (K-SVD) and long short-term memory network (LSTM) is proposed in this study. First, K-SVD can convert source domain data into a sparse dictionary and sparse coefficient. The domain-invariant features are explored in the sparse dictionary, which contains redundant features. The sparse coefficients are input into the LSTM to obtain a primary classifier. Then, the sparse coefficients of the target domain are solved by using the sparse dictionary of the source domain. It is input into the primary classifier for fine-tuning training, and the final diagnostic classification model is obtained. The proposed method establishes knowledge transfer from the source domain to the target domain by exploring domain-invariant features in the sparse domain and bridging the distribution discrepancy. It is evaluated using powertrain operating data acquired on cross-speed, cross-load and cross-sensor working conditions. It is demonstrated that the proposed method has superior performance in dealing with data imbalance and different distributions. It offers a promising approach for industrial applications on cross-domain fault diagnosis.</div></div>
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5

Li, D., X. Nie, C. Wu, J. Song, L. Ma, and J. Yang. "Bearing cross-domain fault diagnosis based on domain adversarial network." In 13th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE 2023). Institution of Engineering and Technology, 2023. http://dx.doi.org/10.1049/icp.2023.1698.

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6

Yue, Fengyu, and Yong Wang. "Cross-Domain Fault Diagnosis via Meta-Learning-Based Domain Generalization." In 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE). IEEE, 2022. http://dx.doi.org/10.1109/case49997.2022.9926497.

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Forest, Florent, and Olga Fink. "Calibrated Self-Training for Cross-Domain Bearing Fault Diagnosis." In 33rd European Safety and Reliability Conference. Singapore: Research Publishing Services, 2023. http://dx.doi.org/10.3850/978-981-18-8071-1_p249-cd.

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8

Ding, Yifei, and Minping Jia. "Cross-Domain Fault Diagnosis for Rotating Machines with Multi-Scale Domain Adaptation." In 2022 Global Reliability and Prognostics and Health Management (PHM-Yantai). IEEE, 2022. http://dx.doi.org/10.1109/phm-yantai55411.2022.9941970.

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Cao, Yuxin, Yue Zhao, Lijun Li, Chenye Zhang, and Zengshou Dong. "Rolling bearing cross-domain fault diagnosis based on transfer learning domain generalization." In 2023 4th International Conference on Computer Engineering and Intelligent Control (ICCEIC). IEEE, 2023. http://dx.doi.org/10.1109/icceic60201.2023.10426717.

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Zhao, Chao, and Weiming Shen. "An Application-oriented Perspective of Domain Generalization for Cross-Domain Fault Diagnosis." In 2023 26th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2023. http://dx.doi.org/10.1109/cscwd57460.2023.10152676.

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