{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T15:03:16Z","timestamp":1776351796617,"version":"3.51.2"},"reference-count":32,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["National Research Foundation of Korea"],"award-info":[{"award-number":["National Research Foundation of Korea"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents a novel method for fusing information from multiple sensor systems for bearing fault diagnosis. In the proposed method, a convolutional neural network is exploited to handle multiple signal sources simultaneously. The most important finding of this paper is that a deep neural network with wide structure can extract automatically and efficiently discriminant features from multiple sensor signals simultaneously. The feature fusion process is integrated into the deep neural network as a layer of that network. Compared to single sensor cases and other fusion techniques, the proposed method achieves superior performance in experiments with actual bearing data.<\/jats:p>","DOI":"10.3390\/s21010244","type":"journal-article","created":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T22:35:48Z","timestamp":1609540548000},"page":"244","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["A Deep Neural Network-Based Feature Fusion for Bearing Fault Diagnosis"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3930-7283","authenticated-orcid":false,"given":"Duy Tang","family":"Hoang","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, University of Ulsan, Ulsan 44610, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1096-1262","authenticated-orcid":false,"given":"Xuan Toa","family":"Tran","sequence":"additional","affiliation":[{"name":"NTT Hi-Tech Institute, Nguyen Tat Thanh University, 300A Nguyen Tat Thanh Street, Ho Chi Minh City 70000, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9616-6061","authenticated-orcid":false,"given":"Mien","family":"Van","sequence":"additional","affiliation":[{"name":"Centre for Intelligent and Autonomous Manufacturing Systems, and School of Electronics, Electrical Engineering and Computer Science, Queen\u2019s University Belfast, Belfast BT7 1NN, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9121-5442","authenticated-orcid":false,"given":"Hee Jun","family":"Kang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, University of Ulsan, Ulsan 44610, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106587","DOI":"10.1016\/j.ymssp.2019.106587","article-title":"Applications of machine learning to machine fault diagnosis: A review and roadmap","volume":"138","author":"Lei","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Piltan, F., Prosvirin, A.E., Jeong, I., Im, K., and Kim, J.M. (2019). Rolling-Element Bearing Fault Diagnosis Using Advanced Machine Learning-Based Observer. Appl. Sci., 9.","DOI":"10.3390\/app9245404"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1177\/0954406215573976","article-title":"Two-stage feature selection for bearing fault diagnosis based on dual-tree complex wavelet transform and empirical mode decomposition","volume":"230","author":"Van","year":"2016","journal-title":"Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"106861","DOI":"10.1016\/j.ymssp.2020.106861","article-title":"Multisensor data fusion for gearbox fault diagnosis using 2-D convolutional neural network and motor current signature analysis","volume":"144","author":"Azamfar","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"107050","DOI":"10.1016\/j.ymssp.2020.107050","article-title":"High-speed train wheel set bearing fault diagnosis and prognostics: A new prognostic model based on extendable useful life","volume":"146","author":"Xu","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1109\/TIE.2014.2327589","article-title":"Heterogeneous feature models and feature selection applied to bearing fault diagnosis","volume":"62","author":"Rauber","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_7","first-page":"1","article-title":"Research progress of the fractional Fourier transform in signal processing","volume":"49","author":"Tao","year":"2006","journal-title":"Sci. China Ser. F"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.renene.2019.06.094","article-title":"Vibration analysis for large-scale wind turbine blade bearing fault detection with an empirical wavelet thresholding method","volume":"146","author":"Liu","year":"2020","journal-title":"Renew. Energy"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1049\/iet-smt.2016.0121","article-title":"Adaptive fault identification of bearing using empirical mode decomposition\u2013principal component analysis-based average kurtosis technique","volume":"11","author":"Mohanty","year":"2017","journal-title":"IET Sci. Meas. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Thelaidjia, T., Moussaoui, A., and Chenikher, S. (2015, January 18\u201320). Bearing fault diagnosis based on independent component analysis and optimized support vector machine. Proceedings of the 2015 7th International Conference on Modelling, Identification and Control (ICMIC), Sousse, Tunisia.","DOI":"10.1109\/ICMIC.2015.7409362"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1049\/iet-smt.2014.0228","article-title":"Bearing-fault diagnosis using non-local means algorithm and empirical mode decomposition-based feature extraction and two-stage feature selection","volume":"9","author":"Van","year":"2015","journal-title":"IET Sci. Meas. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zuo, L., Zhang, L., Zhang, Z.H., Luo, X.L., and Liu, Y. (2020). A spiking neural network-based approach to bearing fault diagnosis. J. Manuf. Syst., in press.","DOI":"10.1016\/j.jmsy.2020.07.003"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Cui, M., Wang, Y., Lin, X., and Zhong, M. (2020). Fault Diagnosis of Rolling Bearings Based on an Improved Stack Autoencoder and Support Vector Machine. IEEE Sens. J.","DOI":"10.1109\/JSEN.2020.3030910"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"107253","DOI":"10.1016\/j.measurement.2019.107253","article-title":"Modelling of shaft unbalance: Modelling a multi discs rotor using K-Nearest Neighbor and Decision Tree Algorithms","volume":"151","author":"Gohari","year":"2020","journal-title":"Measurement"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1016\/j.acha.2019.06.004","article-title":"Universality of deep convolutional neural networks","volume":"48","author":"Zhou","year":"2020","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"ref_16","first-page":"1","article-title":"Sparse autoencoder","volume":"72","author":"Ng","year":"2011","journal-title":"CS294A Lect. Notes"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Norouzi, M., Ranjbar, M., and Mori, G. (2009, January 20\u201325). Stacks of convolutional restricted boltzmann machines for shift-invariant feature learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, Miami, FL, USA.","DOI":"10.1109\/CVPRW.2009.5206577"},{"key":"ref_18","unstructured":"Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2648","DOI":"10.1109\/TIM.2019.2928346","article-title":"An enhanced intelligent diagnosis method based on multi-sensor image fusion via improved deep learning network","volume":"69","author":"Wang","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1109\/TIM.2017.2669947","article-title":"Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network","volume":"66","author":"Chen","year":"2017","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_21","first-page":"54","article-title":"An approach for image fusion using PCA and genetic algorithm","volume":"145","author":"Kaur","year":"2016","journal-title":"Int. J. Comput. Appl."},{"key":"ref_22","unstructured":"Lohweg, V., and M\u00f6nks, U. (2020, November 07). Fuzzy-Pattern-Classifier Based Sensor Fusion for Machine Conditioning. Available online: https:\/\/www.intechopen.com\/books\/sensor-fusion-and-its-applications\/fuzzy-pattern-classifier-based-sensor-fusion-for-machine-conditioning."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.engappai.2016.10.017","article-title":"Dempster-Shafer evidence theory for multi-bearing faults diagnosis","volume":"57","author":"Hui","year":"2017","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Xie, X., Ke, Y., Hao, Y., Song, L., and Wang, H. (2017, January 10\u201312). Feature extraction method for roller bearing based on Dempster-Shafer evidence. Proceedings of the 2017 9th International Conference on Modelling, Identification and Control (ICMIC), Kunming, China.","DOI":"10.1109\/ICMIC.2017.8321554"},{"key":"ref_25","unstructured":"Loparo, K.A. (2013). Bearing Data Center, Case Western Reserve University."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1984","DOI":"10.1109\/TIFS.2016.2569061","article-title":"Discriminant correlation analysis: Real-time feature level fusion for multimodal biometric recognition","volume":"11","author":"Haghighat","year":"2016","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_27","unstructured":"Phung, S.L., and Bouzerdoum, A. (2009). Matlab Library for Convolutional Neural Networks, ICT Research Institute, Visual and Audio Signal Processing Laboratory, University of Wollongong. Tech. Rep."},{"key":"ref_28","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bol\u00f3s, V.J., and Ben\u00edtez, R. (2014). The wavelet scalogram in the study of time series. Advances in Differential Equations and Applications, Springer.","DOI":"10.1007\/978-3-319-06953-1_15"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5990","DOI":"10.1109\/TIE.2017.2774777","article-title":"A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method","volume":"65","author":"Wen","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_31","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_32","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/1\/244\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:06:16Z","timestamp":1760159176000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/1\/244"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,1]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["s21010244"],"URL":"https:\/\/doi.org\/10.3390\/s21010244","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,1]]}}}