Bibliografías temáticas / Cross-domain fault diagnosis

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Literatura académica sobre el tema "Cross-domain fault diagnosis"

Autor: Grafiati
Publicado: 19 de octubre de 2024
Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "Cross-domain fault diagnosis"

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

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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 ex
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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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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
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Sun, Dong, Xudong Yang, and Hai Yang. "A Domain Adaptation Meta-Relation Network for Knowledge Transfer from Human-Induced Faults to Natural Faults in Bearing Fault Diagnosis." Sensors 25, no. 7 (2025): 2254. https://doi.org/10.3390/s25072254.

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Intelligent fault diagnosis of bearings is crucial to the safe operation and productivity of mechanical equipment, but it still faces the challenge of difficulty in acquiring real fault data in practical applications. Therefore, this paper proposes a domain adaptive meta-relation network (DAMRN) to achieve diagnostic knowledge transfer from laboratory-simulated faults (human-induced faults) to real scenario faults (natural faults) by fusing meta-learning and domain adaptation techniques. Specifically, firstly, through meta-task scenario training, DAMRN captures task-independent generic feature
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Yang, Dan, Tianyu Ma, and Zhipeng Li. "A multi-source domain adaption intelligent fault diagnosis method based on asymmetric adversarial training." Measurement Science and Technology 36, no. 3 (2025): 036123. https://doi.org/10.1088/1361-6501/adb2b1.

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Abstract To enhance the cross-domain diagnostic ability of the model, domain adaptation method is adopted. When using traditional domain adaption methods to extract domain invariant characteristics of axial flow fan faults, the characteristics of the source and target domains will be close to each other, thereby the distribution of trained source domain characteristics will be changed. When the fault characteristics of the source domain gather at the classification boundary, the trained model will incorrectly classify some target samples. In addition, single source domain adaptation can lead t
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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 (2024): 8949. http://dx.doi.org/10.3390/app14198949.

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

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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 a
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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 (2022): 3970. http://dx.doi.org/10.3390/math10213970.

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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 ab
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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 (2022): 326. http://dx.doi.org/10.3390/machines10050326.

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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 fau
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Zhang, Long, Hao Zhang, Qian Xiao, et al. "Numerical Model Driving Multi-Domain Information Transfer Method for Bearing Fault Diagnosis." Sensors 22, no. 24 (2022): 9759. http://dx.doi.org/10.3390/s22249759.

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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 si
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Liu, Zhuoxun, Chengqian Zhao, Zhe Yang, Jianyu Long, and Chuan Li. "Cross-domain fault diagnosis for rolling element bearings without fault data in target domain." IET Conference Proceedings 2025, no. 10 (2025): 236–40. https://doi.org/10.1049/icp.2025.2363.

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Tesis sobre el tema "Cross-domain fault diagnosis"

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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
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Capítulos de libros sobre el tema "Cross-domain fault diagnosis"

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Lu, Weikai, Jian Chen, Hao Zheng, et al. "Domain Adversarial Interaction Network for Cross-Domain Fault Diagnosis." In Machine Learning for Cyber Security. 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. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8076-5_3.

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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. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-7007-6_24.

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Wang, Rui, Weiguo Huang, Xiao Zhang, Mingkuan Shi, Chuancang Ding, and Zhongkui Zhu. "Cross-domain machinery fault diagnosis under unseen working conditions based on multiple auxiliary classifiers." In Equipment Intelligent Operation and Maintenance. CRC Press, 2025. https://doi.org/10.1201/9781003470076-82.

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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. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6901-0_89.

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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). Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-49421-5_76.

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

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Actas de conferencias sobre el tema "Cross-domain fault diagnosis"

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Zhong, Jiajun, and Peng Lin. "Dynamic Regulation Domain Adaptation Network for Cross Domain Bearing Fault Diagnosis." In 2024 7th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). IEEE, 2024. https://doi.org/10.1109/prai62207.2024.10827083.

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Liu, Zhengyu, Tong Sun, Juan Xu, Tong Wu, Yewei Wang, and Rui Xu. "CFNet: Cross-Domain Bearing Fault Diagnosis Under Different Operating Conditions." In 2024 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD). IEEE, 2024. https://doi.org/10.1109/icsmd64214.2024.10920617.

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Zhang, Jinyuan, Boyuan Yang, and Ruonan Liu. "Dynamic Confusion-Aware Correlation Network for Cross-Domain Fault Diagnosis." In IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2024. https://doi.org/10.1109/iecon55916.2024.10905878.

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Wang, Li, Yiping Gao, Xinyu Li, and Liang Gao. "Regularized Optimal Transport Enabled Few-Shot Cross-Domain Fault Diagnosis." In 2024 Global Reliability and Prognostics and Health Management Conference (PHM-Beijing). IEEE, 2024. https://doi.org/10.1109/phm-beijing63284.2024.10874716.

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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). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662230.

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Chen, Zixu, Wennian Yu, Qing Ni, and Jinchen Ji. "Learnable Topology Enhanced Heterogeneous Network for Unbalanced Cross-Domain Fault Diagnosis." In 2024 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC). IEEE, 2024. http://dx.doi.org/10.1109/sdpc62810.2024.10707709.

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Li, Jingde, Changqing Shen, Juanjuan Shi, Dong Wang, Weiguo Huang, and Zhongkui Zhu. "Adversarial Domain Bias Removal Network for Cross-condition Bearing Fault Diagnosis." In 2024 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD). IEEE, 2024. https://doi.org/10.1109/icsmd64214.2024.10920512.

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Zhu, Xiyao, Yurui Zhu, Xin Ma, Jinglin Zhou, and Dazi Li. "Encoder-Enhanced Graph Convolutional Network for Cross-Domain Mechanical Fault Diagnosis." In 2025 IEEE 14th Data Driven Control and Learning Systems (DDCLS). IEEE, 2025. https://doi.org/10.1109/ddcls66240.2025.11065744.

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Usman, Muhammad, Tenta Komatsu, Kyaw Myo Htun, Zhiqi Liu, and Aryel Beck. "Benchmarking Sensor Modalities with Few-shot Domain Adaptation for Cross-Domain Fault Diagnosis*." In 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE). IEEE, 2024. http://dx.doi.org/10.1109/case59546.2024.10711623.

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Liu, Qingke, Quan Zhang, Yaqi Yu, and Xiaowen Ma. "Adversarial Multi-Target Domain Adaptation with Multi-Scale Convolutional Neural Networks for Cross-Domain Fault Diagnosis." In 2025 5th International Conference on Sensors and Information Technology (ICSI). IEEE, 2025. https://doi.org/10.1109/icsi64877.2025.11009887.

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