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Artículos de revistas 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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1

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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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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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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

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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12

Zhang, Hongpeng, Xinran Wang, Cunyou Zhang, et al. "Dynamic Condition Adversarial Adaptation for Fault Diagnosis of Wind Turbine Gearbox." Sensors 23, no. 23 (2023): 9368. http://dx.doi.org/10.3390/s23239368.

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While deep learning has found widespread utility in gearbox fault diagnosis, its direct application to wind turbine gearboxes encounters significant hurdles. Disparities in data distribution across a spectrum of operating conditions for wind turbines result in a marked decrease in diagnostic accuracy. In response, this study introduces a tailored dynamic conditional adversarial domain adaptation model for fault diagnosis in wind turbine gearboxes amidst cross-condition scenarios. The model adeptly adjusts the importance of aligning marginal and conditional distributions using distance metric f

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13

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 (2024): 1304. http://dx.doi.org/10.3390/jmse12081304.

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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–

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14

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 (2023): 2137. http://dx.doi.org/10.3390/s23042137.

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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 solv

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15

Bai, Jie, Xuan Liu, Bingjie Dou, et al. "A Fault Diagnosis Method for Pumped Storage Unit Stator Based on Improved STFT-SVDD Hybrid Algorithm." Processes 12, no. 10 (2024): 2126. http://dx.doi.org/10.3390/pr12102126.

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Stator faults are one of the common issues in pumped storage generators, significantly impacting their performance and safety. To ensure the safe and stable operation of pumped storage generators, a stator fault diagnosis method based on an improved short-time Fourier transform (STFT)-support vector data description (SVDD) hybrid algorithm is proposed. This method establishes a fault model for inter-turn short circuits in the stator windings of pumped storage generators and analyzes the electrical and magnetic states associated with such faults. Based on the three-phase current signals observe

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16

Liu, Guokai, Weiming Shen, Liang Gao, and Andrew Kusiak. "Automated broad transfer learning for cross-domain fault diagnosis." Journal of Manufacturing Systems 66 (February 2023): 27–41. http://dx.doi.org/10.1016/j.jmsy.2022.11.003.

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17

Liu, Siyuan, Jinying Huang, Peiyu Han, Zhenfang Fan, and Jiancheng Ma. "Cross-Domain Fault Diagnosis of Rotating Machinery Under Time-Varying Rotational Speed and Asymmetric Domain Label Condition." Sensors 25, no. 9 (2025): 2818. https://doi.org/10.3390/s25092818.

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In practical engineering, the asymmetric problem of the domain label space is inevitable owing to the prior fault information of the target domain being difficult to completely obtain. This implies that the target domain may include unseen fault classes or lack certain fault classes found in the source domain. To maintain diagnostic performance and knowledge generalization across different speeds, cross-domain intelligent fault diagnosis (IFD) models are widely researched. However, the rigid requirement for consistent domain label spaces hinders the IFD model from identifying private fault pat

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18

Zhang, Chao, Peng Du, Dingyu Zhou, Zhijie Dong, Shilie He, and Zhenwei Zhou. "Fault Diagnosis of Low-Noise Amplifier Circuit Based on Fusion Domain Adaptation Method." Actuators 13, no. 9 (2024): 379. http://dx.doi.org/10.3390/act13090379.

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The Low-Noise Amplifier (LNA) is a critical component of Radio Frequency (RF) receivers. Therefore, the accuracy of LNA fault diagnosis significantly impacts the overall performance of the entire RF receiver. Traditional LNA fault diagnosis is typically conducted under fixed conditions, but varying factors in practical applications often alter the circuit’s parameters and reduce diagnostic accuracy. To address the issue of decreased fault diagnosis accuracy under varying external or internal conditions, a fusion domain adaptation method based on Convolutional Neural Networks (CNNs), referred t

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19

Wu, Miaoling, Jun Zhang, Peidong Xu, et al. "Bearing Fault Diagnosis for Cross-Condition Scenarios Under Data Scarcity Based on Transformer Transfer Learning Network." Electronics 14, no. 3 (2025): 515. https://doi.org/10.3390/electronics14030515.

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Motor-bearing fault diagnosis is critical for industrial equipment reliability, yet traditional data-driven methods require extensive labeled data, which are often scarce in real-world applications. To address this challenge, we propose a Transformer transfer learning network (TTLN) for accurate fault diagnosis under cross-condition scenarios, particularly when target domain data are limited. First, we develop a Transformer-based fault diagnosis model that captures long-range dependencies in sequential data through self-attention, achieving high accuracy under single operating conditions. Seco

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20

Chen, Zhuyun, Guolin He, Jipu Li, Yixiao Liao, Konstantinos Gryllias, and Weihua Li. "Domain Adversarial Transfer Network for Cross-Domain Fault Diagnosis of Rotary Machinery." IEEE Transactions on Instrumentation and Measurement 69, no. 11 (2020): 8702–12. http://dx.doi.org/10.1109/tim.2020.2995441.

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21

Liu, Fuqiang, Wenlong Deng, Chaoqun Duan, Yi Qin, Jun Luo, and Huayan Pu. "Duplex adversarial domain discriminative network for cross-domain partial transfer fault diagnosis." Knowledge-Based Systems 279 (November 2023): 110960. http://dx.doi.org/10.1016/j.knosys.2023.110960.

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22

Liu, Fuzheng, Faye Zhang, Xiangyi Geng, et al. "Structural discrepancy and domain adversarial fusion network for cross-domain fault diagnosis." Advanced Engineering Informatics 58 (October 2023): 102217. http://dx.doi.org/10.1016/j.aei.2023.102217.

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23

Zhou, Hongdi, Tao Huang, Xixing Li, and Fei Zhong. "Cross-domain intelligent fault diagnosis of rolling bearing based on distance metric transfer learning." Advances in Mechanical Engineering 14, no. 11 (2022): 168781322211357. http://dx.doi.org/10.1177/16878132221135740.

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Rolling bearings are present ubiquitously in mechanical equipment, timely fault diagnosis has great significance in guaranteeing the safety of mechanical operation. In real world industrial applications, the distribution of training dataset (source domain) and testing dataset (target domain) is often different and varies with operating environment, which may lead to performance degradation. In this study, a cross-domain fault diagnosis of rolling bearing method based on distance metric transfer learning (DMTL) and wavelet packet decomposition (WPD) is proposed. The Mahalanobis distance is adop

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24

Qin, Y. X., Y. Hong, J. Y. Long, Z. Yang, Y. W. Huang, and C. Li. "Attitude data-based deep transfer capsule network for intelligent fault diagnosis of delta 3D printers." Journal of Physics: Conference Series 2184, no. 1 (2022): 012017. http://dx.doi.org/10.1088/1742-6596/2184/1/012017.

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Abstract In order to improve the quality of printed products and promote the application of 3D printing, it is necessary to carry out health monitoring and fault diagnosis for 3D printers. In this paper, an attitude data-based deep transfer capsule network is proposed for intelligent fault diagnosis of delta 3D printers. Based on the forward kinematic analysis, the attitude data change of the moving platform can reflect the fault information of the printers. To extract fault features from the attitude data with rich directional pose information and complete the cross-domain diagnosis task effe

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25

Feiyan Fan, Feiyan Fan, Jiazhen Hou Feiyan Fan, and Tanghuai Fan Jiazhen Hou. "Fault Diagnosis under Varying Working Conditions with Domain Adversarial Capsule Networks." 電腦學刊 33, no. 3 (2022): 135–46. http://dx.doi.org/10.53106/199115992022063303011.

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<p>Most existing studies that develop fault diagnosis methods focus on performance under steady operation while overlooking adaptability under varying working conditions. This results in the low generalization of the fault diagnosis methods. In this study, a novel deep transfer learning architecture is proposed for fault diagnosis under varying working conditions. A modified capsule network is developed by combining the domain adversarial framework and classical capsule network to simultaneously recognize the machinery fault and working conditions. The novelty of the proposed architectur

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26

Zou, Yingyong, Wenzhuo Zhao, Tao Liu, Xingkui Zhang, and Yaochen Shi. "Research on High-Speed Train Bearing Fault Diagnosis Method Based on Domain-Adversarial Transfer Learning." Applied Sciences 14, no. 19 (2024): 8666. http://dx.doi.org/10.3390/app14198666.

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Traditional bearing fault diagnosis methods struggle to effectively extract distinctive, domain-invariable characterizations from one-dimensional vibration signals of high-speed train (HST) bearings under variable load conditions. A deep migration fault diagnosis method based on the combination of a domain-adversarial network and signal reconstruction unit (CRU) is proposed for this purpose. The feature extraction module, which includes a one-dimensional convolutional (Cov1d) layer, a normalization layer, a ReLU activation function, and a max-pooling layer, is integrated with the CRU to form a

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27

Zhao, Chao, and Weiming Shen. "Dual adversarial network for cross-domain open set fault diagnosis." Reliability Engineering & System Safety 221 (May 2022): 108358. http://dx.doi.org/10.1016/j.ress.2022.108358.

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28

Zheng, Huailiang, Rixin Wang, Yuantao Yang, et al. "Cross-Domain Fault Diagnosis Using Knowledge Transfer Strategy: A Review." IEEE Access 7 (2019): 129260–90. http://dx.doi.org/10.1109/access.2019.2939876.

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29

Chao, Ko-Chieh, Chuan-Bi Chou, and Ching-Hung Lee. "Online Domain Adaptation for Rolling Bearings Fault Diagnosis with Imbalanced Cross-Domain Data." Sensors 22, no. 12 (2022): 4540. http://dx.doi.org/10.3390/s22124540.

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Traditional machine learning methods rely on the training data and target data having the same feature space and data distribution. The performance may be unacceptable if there is a difference in data distribution between the training and target data, which is called cross-domain learning problem. In recent years, many domain adaptation methods have been proposed to solve this kind of problems and make much progress. However, existing domain adaptation approaches have a common assumption that the number of the data in source domain (labeled data) and target domain (unlabeled data) is matched.

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30

Xie, Fengyun, Gang Li, Qiuyang Fan, Qian Xiao, and Shengtong Zhou. "Optimizing and Analyzing Performance of Motor Fault Diagnosis Algorithms for Autonomous Vehicles via Cross-Domain Data Fusion." Processes 11, no. 10 (2023): 2862. http://dx.doi.org/10.3390/pr11102862.

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Electric motors play a pivotal role in the functioning of autonomous vehicles, necessitating accurate fault diagnosis to ensure vehicle safety and reliability. In this paper, a novel motor fault diagnosis approach grounded in vibration signals to enhance fault detection performance is presented. The method involves capturing vibration signals from the motor across various operational states and frequencies using vibration sensors. Subsequently, the signals undergo transformation into frequency domain representations through fast Fourier transform. This includes normalizing and concatenating th

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31

Kim, Taeyun, and Jangbom Chai. "Pre-Processing Method to Improve Cross-Domain Fault Diagnosis for Bearing." Sensors 21, no. 15 (2021): 4970. http://dx.doi.org/10.3390/s21154970.

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Models trained with one system fail to identify other systems accurately because of domain shifts. To perform domain adaptation, numerous studies have been conducted in many fields and have successfully aligned different domains into one domain. The domain shift problem is caused by the difference of distributions between two domains, which is solved by reducing this difference. Source domain data are labeled and used for training the models to extract the features while the target domain data are unlabeled or partially labeled and only used for aligning. Bearings play important roles in rotat

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32

Zhang, Yizong, Shaobo Li, Ansi Zhang, Chuanjiang Li, and Ling Qiu. "A Novel Bearing Fault Diagnosis Method Based on Few-Shot Transfer Learning across Different Datasets." Entropy 24, no. 9 (2022): 1295. http://dx.doi.org/10.3390/e24091295.

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At present, the success of most intelligent fault diagnosis methods is heavily dependent on large datasets of artificial simulation faults (ASF), which have not been widely used in practice because it is often costly to obtain a large number of samples in reality. Fortunately, various faults can be easily simulated in the laboratory, and these simulated faults contain a lot of fault diagnosis knowledge. In this study, based on a Siamese network framework, we propose a bearing fault diagnosis based on few-shot transfer learning across different datasets (cross-machine), using the knowledge of A

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33

Fu, Bo, Li Xu, Yi Quan, Chaoshun Li, Xilin Zhao, and Yuxiang Zhu. "A cross domain processing deep transfer learning network for rotating machinery fault diagnosis." Measurement Science and Technology 36, no. 4 (2025): 046132. https://doi.org/10.1088/1361-6501/adc324.

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Abstract In the field of intelligent fault diagnosis of mechanical equipment, existing cross-domain diagnostic models based on transfer learning (TL) do not utilise the commonality information between the two domains in the data processing stage, which leads to the loss of transferable features that are essential for the cross-domain fault diagnostic task. To address this issue, this paper proposes a cross-domain processing deep TL network model (CDPDTLN), which consists of a cross-domain data processing (CDP) module, a feature extraction module and a domain-adaptive diagnostic module. In the

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34

Zhang, Yongchao, Zhaohui Ren, Ke Feng, Kun Yu, Michael Beer, and Zheng Liu. "Universal source-free domain adaptation method for cross-domain fault diagnosis of machines." Mechanical Systems and Signal Processing 191 (May 2023): 110159. http://dx.doi.org/10.1016/j.ymssp.2023.110159.

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35

Wang, Yu, Jie Gao, Wei Wang, Xu Yang, and Jinsong Du. "Curriculum learning-based domain generalization for cross-domain fault diagnosis with category shift." Mechanical Systems and Signal Processing 212 (April 2024): 111295. http://dx.doi.org/10.1016/j.ymssp.2024.111295.

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36

Shang, Zhiwu, Xiaolong Du, Cailu Pan, Fei Liu, and Ziyu Wang. "Joint domain transfer elasticity metric network for cross-domain small sample fault diagnosis." Neurocomputing 650 (October 2025): 130936. https://doi.org/10.1016/j.neucom.2025.130936.

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37

Zheng, Huailiang, Yuantao Yang, Jiancheng Yin, Yuqing Li, Rixin Wang, and Minqiang Xu. "Deep Domain Generalization Combining A Priori Diagnosis Knowledge Toward Cross-Domain Fault Diagnosis of Rolling Bearing." IEEE Transactions on Instrumentation and Measurement 70 (2021): 1–11. http://dx.doi.org/10.1109/tim.2020.3016068.

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38

Zhao, Juanru, Mei Yuan, Yiwen Cui, and Jin Cui. "A Cross-Machine Intelligent Fault Diagnosis Method with Small and Imbalanced Data Based on the ResFCN Deep Transfer Learning Model." Sensors 25, no. 4 (2025): 1189. https://doi.org/10.3390/s25041189.

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Intelligent fault diagnosis (IFD) for mechanical equipment based on small and imbalanced datasets has been widely studied in recent years, with transfer learning emerging as one of the most promising approaches. Existing transfer learning-based IFD methods typically use data from different operating conditions of the same equipment as the source and target domains for the transfer learning process. However, in practice, it is often challenging to find identical equipment to obtain source domain data when diagnosing faults in the target equipment. These strict assumptions pose significant limit

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39

Yan, Tao, Jianchun Guo, Yuan Zhou, Lixia Zhu, Bo Fang, and Jiawei Xiang. "Numerical Simulation Data-Aided Domain-Adaptive Generalization Method for Fault Diagnosis." Sensors 25, no. 11 (2025): 3482. https://doi.org/10.3390/s25113482.

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In order to deal with the cross-domain distribution offset problem in mechanical fault diagnosis under different operating conditions. Domain-adaptive (DA) methods, such as domain adversarial neural networks (DANNs), maximum mean discrepancy (MMD), and correlation alignment (CORAL), have been advanced in recent years, producing notable outcomes. However, these techniques rely on the accessibility of target data, restricting their use in real-time fault diagnosis applications. To address this issue, effectively extracting fault features in the source domain and generalizing them to unseen targe

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40

Liu, Xiaorong, Zhonghan Chen, Dongfeng Hu, and Liansong Zong. "A Novel Framework Based on Complementary Views for Fault Diagnosis with Cross-Attention Mechanisms." Electronics 14, no. 5 (2025): 886. https://doi.org/10.3390/electronics14050886.

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Bearing fault diagnosis is critical for the reliability and safety of rotating machinery in industrial applications. Traditional fault diagnosis methods and single-view deep learning models often fail to capture the complex, multi-dimensional nature of vibration signals, limiting their effectiveness in accurately identifying faults under varying conditions. To address this, we propose a novel multi-view framework that leverages complementary views—time, frequency, and wavelet domains—of vibration signals for robust fault diagnosis. Our framework integrates a cross-attention mechanism (CAM) tha

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41

Xu, Shu, Jian Ma, and Dengwei Song. "Open-set Federated Adversarial Domain Adaptation Based Cross-domain Fault Diagnosis." Measurement Science and Technology, July 13, 2023. http://dx.doi.org/10.1088/1361-6501/ace734.

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Abstract Data-driven fault diagnosis techniques utilizing deep learning have achieved widespread success. However, their diagnostic capability and application possibility are significantly reduced in real-world scenarios where fault modes are not fully covered and labels are lacking. Owing to potential conflicts of interest and legal risks, industrial equipment fault data usually exist in the form of isolated islands, making it difficult to carry out large-scale centralized model training. This paper proposes open-set federated adversarial domain adaptation (OS-FADA) to achieve collaborative e

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42

Jia, Feng, Yuanfei Wang, Jianjun Shen, Lifei Hao, and Zhaoyu Jiang. "Stepwise feature norm network with adaptive weighting for open set cross-domain intelligent fault diagnosis of bearings." Measurement Science and Technology, February 9, 2024. http://dx.doi.org/10.1088/1361-6501/ad282f.

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Abstract Cross-domain fault diagnosis of bearings has attracted significant attention. However, traditional cross-domain diagnostic methods have the following shortcomings: (1) When the trained model is applied to a new scenario, it leads to severe degradation of the model and a reduction in its generalisation ability. (2) The accuracy of the open-set fault diagnosis is affected by additional faults in the target domain data. To overcome these shortcomings, a stepwise feature norm network with adaptive weighting (SFNAW) is proposed for cross-domain open-set fault diagnosis. In SFNAW, two weigh

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43

Wang, Cheng, Bingyou Cheng, and Lili Deng. "An Adaptive Thresholding Approach for Open Set Fault Diagnosis." Measurement Science and Technology, November 22, 2024. http://dx.doi.org/10.1088/1361-6501/ad9625.

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Abstract Cross-domain fault diagnosis using deep learning plays a critical role in ensuring the reliability and safety of mechanical systems. However, real-world industrial scenarios often involve unknown fault classes, which introduce significant challenges beyond environmental differences between training and testing phases. These unknown fault classes, which do not appear in the training data, create a cross-domain open set fault diagnosis problem where the target domain includes both known and unknown fault types with distinct distribution characteristics. Traditional domain adaptation met

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Li, Can, Guangbin Wang, Shubiao Zhao, Zhixian Zhong, and Ying Lv. "Cross-domain manifold structure preservation for transferable and cross-machine fault diagnosis." Journal of Vibroengineering, August 22, 2024. http://dx.doi.org/10.21595/jve.2024.24067.

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To address the decline or failure in the autonomous learning capability of traditional transfer learning methods when training and test samples come from different machines, resulting in low cross-machine fault diagnosis rates, we propose a cross-domain manifold structure preservation (CDMSP) method for diagnosing rolling bearing faults across machines. The CDMSP method can induce the manifold space projection matrices of the source and target domains more effectively. This method maps high-dimensional features into a low-dimensional manifold, preserving non-linear relationships and aligning d

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45

Jia, Feng, Xiang Xu, Yuanfei Wang, and Jianjun Shen. "A novel source-free domain adaptation network for intelligent diagnosis of bearings under unknown faults." Measurement Science and Technology, March 28, 2025. https://doi.org/10.1088/1361-6501/adc6a6.

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Abstract Domain adaptation has significantly advanced the field of cross-domain intelligent fault diagnosis of bearings. However, there are still some issues that hinder the progress of cross-domain fault diagnosis as follows. The dataset may not be shared due to privacy issues in some industrial scenarios, and unknown faults included in the dataset may lead to low diagnosis accuracy. To address these issues, this paper proposes a source-free domain adaptation network considering unknown faults (SDANU) for intelligent diagnosis of bearings. To address privacy issues, the source domain model is

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Mao, Xiaodong. "Cross domain fault diagnosis method based on MLP-mixer network." Journal of Measurements in Engineering, October 30, 2023. http://dx.doi.org/10.21595/jme.2023.23460.

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The quality of rolling bearings determines the safety of mechanical equipment operation, and bearings with more precise structures are prone to damage due to excessive operation. Therefore, cross domain fault diagnosis of bearings has become a research hotspot. To better improve the accuracy of bearing cross domain fault diagnosis, this study proposes two models. One is a cross domain feature extraction model constructed using a mixed attention mechanism, which recognizes and extracts high-level features of bearing faults through channel attention and spatial attention mechanisms. The other is

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yefeng, zhang, Tang Hesheng, and yan ren. "A Three-Stage Cross Domain Intelligent Fault Diagnosis Method for Multiple New Faults." Measurement Science and Technology, November 8, 2024. http://dx.doi.org/10.1088/1361-6501/ad903f.

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Abstract Due to the randomness and complexity of mechanical faults, the new fault modes usually occur unexpectedly in the actual scenarios. In response to the challenges, a three-stage crossing domain intelligent fault diagnosis method is presented in article. Firstly, the partial domain alignment is achieved based on improved target weighted mechanism, and the outlier identifier is constructed to automatically separate the new fault classes. Then, the unsupervised learning model with silhouette coefficient are built to determine the number of new fault categories. Lastly, the simulation signa

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Wang, Pei, Jie Liu, Jianzhong Zhou, Ran Duan, and Wei Jiang. "Cross-domain fault diagnosis of rotating machinery based on graph feature extraction." Measurement Science and Technology, November 9, 2022. http://dx.doi.org/10.1088/1361-6501/aca16f.

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Abstract Transfer learning can realize the cross-domain fault diagnosis of rotating machinery, where the model trained on plenty of labeled samples collected in one working condition can be transferred to insufficient samples collected in target working condition. Currently, the data features cannot be completely extracted by existing methods when the data distribution gap of the samples collected in different working conditions is quite large. In order to fully extract the data features of rotating machinery to achieve cross-domain fault diagnosis, this paper investigated a cross-domain fault

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Liao, Yixiao, Ruyi Huang, Jipu Li, Zhuyun Chen, and Weihua Li. "Dynamic Distribution Adaptation Based Transfer Network for Cross Domain Bearing Fault Diagnosis." Chinese Journal of Mechanical Engineering 34, no. 1 (2021). http://dx.doi.org/10.1186/s10033-021-00566-3.

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AbstractIn machinery fault diagnosis, labeled data are always difficult or even impossible to obtain. Transfer learning can leverage related fault diagnosis knowledge from fully labeled source domain to enhance the fault diagnosis performance in sparsely labeled or unlabeled target domain, which has been widely used for cross domain fault diagnosis. However, existing methods focus on either marginal distribution adaptation (MDA) or conditional distribution adaptation (CDA). In practice, marginal and conditional distributions discrepancies both have significant but different influences on the d

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Gong, Fengjin, Ping Ma, Nini Wang, Hongli Zhang, Cong Wang, and Xinkai Li. "Cross-device fault diagnosis of rolling bearings using domain generalization and dynamic model." Journal of Vibration and Control, June 1, 2024. http://dx.doi.org/10.1177/10775463241256253.

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Over the past few years, intelligent fault diagnosis technology has been widely applied and has achieved good results. However, these methods cannot effectively diagnose faults across devices. Existing transfer learning methods such as domain generalization (DG) can solve this problem, but these methods rely on multiple source domains to train the network, which limits their practical application. In response to this issue, this study proposes a simulate data-driven method for cross-device fault diagnosis method and a DG network named Adversarial Domain-Invariant Feature Exploration (ADIFEX).

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