{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T05:16:09Z","timestamp":1776316569138,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T00:00:00Z","timestamp":1732233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62073213"],"award-info":[{"award-number":["62073213"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["431"],"award-info":[{"award-number":["431"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["232102220022"],"award-info":[{"award-number":["232102220022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022BS022"],"award-info":[{"award-number":["2022BS022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Key Disciplines Construction Project of Computer Science and Technology in Henan Province","award":["62073213"],"award-info":[{"award-number":["62073213"]}]},{"name":"the Key Disciplines Construction Project of Computer Science and Technology in Henan Province","award":["431"],"award-info":[{"award-number":["431"]}]},{"name":"the Key Disciplines Construction Project of Computer Science and Technology in Henan Province","award":["232102220022"],"award-info":[{"award-number":["232102220022"]}]},{"name":"the Key Disciplines Construction Project of Computer Science and Technology in Henan Province","award":["2022BS022"],"award-info":[{"award-number":["2022BS022"]}]},{"DOI":"10.13039\/501100017700","name":"Henan Province Science and Technology Research Project","doi-asserted-by":"publisher","award":["62073213"],"award-info":[{"award-number":["62073213"]}],"id":[{"id":"10.13039\/501100017700","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017700","name":"Henan Province Science and Technology Research Project","doi-asserted-by":"publisher","award":["431"],"award-info":[{"award-number":["431"]}],"id":[{"id":"10.13039\/501100017700","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017700","name":"Henan Province Science and Technology Research Project","doi-asserted-by":"publisher","award":["232102220022"],"award-info":[{"award-number":["232102220022"]}],"id":[{"id":"10.13039\/501100017700","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017700","name":"Henan Province Science and Technology Research Project","doi-asserted-by":"publisher","award":["2022BS022"],"award-info":[{"award-number":["2022BS022"]}],"id":[{"id":"10.13039\/501100017700","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Doctoral Research Start-up Fund of Henan Finance University","award":["62073213"],"award-info":[{"award-number":["62073213"]}]},{"name":"Doctoral Research Start-up Fund of Henan Finance University","award":["431"],"award-info":[{"award-number":["431"]}]},{"name":"Doctoral Research Start-up Fund of Henan Finance University","award":["232102220022"],"award-info":[{"award-number":["232102220022"]}]},{"name":"Doctoral Research Start-up Fund of Henan Finance University","award":["2022BS022"],"award-info":[{"award-number":["2022BS022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>A convolutional neural network can extract features from high-dimensional data, but the convolution operation has a high time complexity and requires a large amount of computation. For equipment with a high sampling frequency, fault diagnosis methods based on convolutional neural networks cannot meet the requirements of online fault diagnosis. To solve this problem, this study proposes a fault diagnosis method for multi-source heterogeneous information fusion based on two-level transfer learning. This method aims to fully utilize multi-source heterogeneous information and external domain data, construct a two-level transfer mechanism to fuse multi-source heterogeneous information, avoid convolutional operations, and achieve real-time fault diagnosis. Its main work is to build a feature extraction network model of screenshots, design a mechanism for transfer from the feature extraction model using screenshots to the deep learning model using one-dimensional sequence signals, and complete the transfer from a convolutional neural network to a deep neural network. After two-level transfer, the fault diagnosis model not only integrates the characteristics of one-dimensional sequence signals and screenshots but also avoids convolution operations and has a low time complexity. The effectiveness of the proposed method is verified using a gearbox dataset and a bearing dataset.<\/jats:p>","DOI":"10.3390\/e26121007","type":"journal-article","created":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T06:41:48Z","timestamp":1732257708000},"page":"1007","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Real-Time Fault Diagnosis Method for Multi-Source Heterogeneous Information Fusion Based on Two-Level Transfer Learning"],"prefix":"10.3390","volume":"26","author":[{"given":"Danmin","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer and Artificial Intelligence, Henan Finance University, Zhengzhou 450046, China"},{"name":"Zhengzhou Key Laboratory of Financial Big Data Intelligent Application Technology, Zhengzhou 450046, China"}]},{"given":"Zhiqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Logistic Engineering, Shanghai Maritime University, Shanghai 201306, China"}]},{"given":"Funa","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Logistic Engineering, Shanghai Maritime University, Shanghai 201306, China"}]},{"given":"Chaoge","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Logistic Engineering, Shanghai Maritime University, Shanghai 201306, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"14246","DOI":"10.1007\/s10489-022-03344-3","article-title":"Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: A systematic literature review","volume":"52","author":"Fernandes","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"106139","DOI":"10.1016\/j.engappai.2023.106139","article-title":"Multi-fault diagnosis of Industrial Rotating Machines using Data-driven approach: A review of two decades of research","volume":"123","author":"Gawde","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1080\/21642583.2020.1723143","article-title":"Weak fault diagnosis of rolling bearing based on FRFT and DBN","volume":"8","author":"He","year":"2020","journal-title":"Syst. Sci. Control Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2617","DOI":"10.1109\/TIE.2020.2972461","article-title":"Distribution-invariant deep belief network for intelligent fault diagnosis of machines under new working conditions","volume":"68","author":"Xing","year":"2020","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1007\/s10462-021-09993-z","article-title":"A novel fault diagnosis method based on CNN and LSTM and its application in fault diagnosis for complex systems","volume":"55","author":"Huang","year":"2022","journal-title":"Artif. Intell. Rev."},{"key":"ref_6","first-page":"341","article-title":"Research on the big data fusion: Issues and challenges","volume":"3","author":"Meng","year":"2016","journal-title":"J. Comput. Res. Dev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"101981","DOI":"10.1016\/j.simpat.2019.101981","article-title":"Mechanical Fault Diagnosis and Prediction in IoT Based on Multisource Sensing Data Fusion","volume":"102","author":"Huang","year":"2020","journal-title":"Simul. Model. Pract. Theory"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4569","DOI":"10.1109\/TII.2018.2883357","article-title":"Multiple Classifiers and Data Fusion for Robust Diagnosis of Gearbox Mixed Faults","volume":"15","author":"Jagath","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, H., Huang, J., and Ji, S. (2019). Bearing Fault Diagnosis with a Feature Fusion Method Based on an Ensemble Convolutional Neural Network and Deep Neural Network. Sensors, 19.","DOI":"10.3390\/s19092034"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3500412","DOI":"10.1109\/TIM.2021.3132051","article-title":"A Multisensor Information Fusion Method for High-Reliability Fault Diagnosis of Rotating Machinery","volume":"71","author":"Huo","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"9501712","DOI":"10.1109\/TIM.2022.3225910","article-title":"A Novel Ensemble Learning-Based Multisensor Information Fusion Method for Rolling Bearing Fault Diagnosis","volume":"72","author":"Tong","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_12","first-page":"3503315","article-title":"Multi-Information Fusion Fault Diagnosis of Bogie Bearing Under Small Samples via Unsupervised Representation Alignment Deep Q-Learning","volume":"72","author":"Zhu","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1109\/TII.2022.3179011","article-title":"Enhanced Discriminate Feature Learning Deep Residual CNN for Multitask Bearing Fault Diagnosis With Information Fusion","volume":"19","author":"Niu","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Huo, D., Kang, Y., Wang, B., Feng, G., Zhang, J., and Zhang, H. (2022). Gear Fault Diagnosis Method Based on Multi-Sensor Information Fusion and VGG. Entropy, 24.","DOI":"10.3390\/e24111618"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2451008:1","DOI":"10.1142\/S021800142451008X","article-title":"Rolling Bearing Composite Fault Diagnosis Method Based on Convolutional Neural Network","volume":"38","author":"Chen","year":"2024","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"125214","DOI":"10.1016\/j.eswa.2024.125214","article-title":"MRCFN: A multi-sensor residual convolutional fusion network for intelligent fault diagnosis of bearings in noisy and small sample scenarios","volume":"259","author":"Ye","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1109\/TII.2023.3256400","article-title":"Cross-Modal Fusion Convolutional Neural Networks With Online Soft-Label Training Strategy for Mechanical Fault Diagnosis","volume":"20","author":"Xu","year":"2024","journal-title":"IEEE Trans. Ind. Informatics"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s12530-023-09523-y","article-title":"Enhancing fault detection and diagnosis systems for a chemical process: A study on convolutional neural networks and transfer learning","volume":"15","author":"Silva","year":"2024","journal-title":"Evol. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A Survey on Transfer Learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s40537-016-0043-6","article-title":"A Survey of Transfer Learning","volume":"3","author":"Weiss","year":"2016","journal-title":"J. Big Data"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"111158","DOI":"10.1016\/j.knosys.2023.111158","article-title":"Fault diagnosis in rotating machines based on transfer learning: Literature review","volume":"283","author":"Iqbal","year":"2024","journal-title":"Knowl. Based Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.55730\/1300-0632.4033","article-title":"Cognitive digital modelling for hyperspectral image classification using transfer learning model","volume":"31","author":"Shabaz","year":"2023","journal-title":"Turkish J. Electr. Eng. Comput. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Han, L., Gladkoff, S., Erofeev, G., Sorokina, I., Galiano, B., and Nenadic, G. (2024). Neural machine translation of clinical text: An empirical investigation into multilingual pre-trained language models and transfer-learning. Front. Digit. Health, 6.","DOI":"10.3389\/fdgth.2024.1211564"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"110511","DOI":"10.1016\/j.asoc.2023.110511","article-title":"Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning","volume":"144","author":"Ghassemi","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"102659","DOI":"10.1016\/j.simpat.2022.102659","article-title":"A fault diagnosis method of bearings based on deep transfer learning. Simul","volume":"122","author":"Huang","year":"2023","journal-title":"Model. Pract. Theory"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"108890","DOI":"10.1016\/j.ress.2022.108890","article-title":"Deep imbalanced domain adaptation for transfer learning fault diagnosis of bearings under multiple working conditions","volume":"230","author":"Ding","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"107150","DOI":"10.1016\/j.asoc.2021.107150","article-title":"Deep transfer learning with limited data for machinery fault diagnosis","volume":"103","author":"Han","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"110739","DOI":"10.1016\/j.asoc.2023.110739","article-title":"A class-level matching unsupervised transfer learning network for rolling bearing fault diagnosis under various working conditions","volume":"146","author":"Huo","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"110065","DOI":"10.1016\/j.knosys.2022.110065","article-title":"A novel deep transfer learning method with inter-domain decision discrepancy minimization for intelligent fault diagnosis","volume":"259","author":"Su","year":"2023","journal-title":"Knowl. Based Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"108236","DOI":"10.1016\/j.engappai.2024.108236","article-title":"Multi-modal data cross-domain fusion network for gearbox fault diagnosis under variable operating conditions","volume":"133","author":"Zhang","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_31","first-page":"3046277","article-title":"Unsupervised Cross-domain Fault Diagnosis Using Feature Representation Alignment Networks for Rotating Machinery","volume":"26","author":"Chen","year":"2020","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"109171","DOI":"10.1016\/j.ress.2023.109171","article-title":"Cross-domain augmentation diagnosis: An adversarial domain-augmented. generalization method for fault diagnosis under unseen working conditions","volume":"234","author":"Li","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"109186","DOI":"10.1016\/j.ress.2023.109186","article-title":"Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing","volume":"234","author":"Zhang","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"42373","DOI":"10.1109\/ACCESS.2019.2907131","article-title":"A Deep Learning Method for Bearing Fault Diagnosis Based on Time-Frequency Image","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2446","DOI":"10.1109\/TII.2018.2864759","article-title":"Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning","volume":"15","author":"Shao","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"055402","DOI":"10.1088\/1361-6501\/ab0793","article-title":"A Novel Bearing Fault Diagnosis Method Based on 2D Image Representation and Transfer Learning-Convolutional Neural Network","volume":"30","author":"Ma","year":"2019","journal-title":"Meas. Sci. Technol."},{"key":"ref_37","unstructured":"(2023, May 01). Complexity Analysis of Convolutional Neural Network. Available online: https:\/\/zhuanlan.zhihu.com\/p\/31575074."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"He, K., and Sun, J. (2015, January 7\u201312). Convolutional Neural Networks at Constrained Time Cost. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299173"},{"key":"ref_39","unstructured":"(2023, June 03). Case Western Reserve University Bearing Data Center Website. Available online: https:\/\/engineering.case.edu\/bearingdatacenter\/welcome."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/26\/12\/1007\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:37:32Z","timestamp":1760114252000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/26\/12\/1007"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,22]]},"references-count":39,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["e26121007"],"URL":"https:\/\/doi.org\/10.3390\/e26121007","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,22]]}}}