{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T16:35:20Z","timestamp":1784392520319,"version":"3.55.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T00:00:00Z","timestamp":1675382400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T00:00:00Z","timestamp":1675382400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["No. 2020AAA0109300"],"award-info":[{"award-number":["No. 2020AAA0109300"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Sign Process Syst"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s11265-023-01845-z","type":"journal-article","created":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T09:05:05Z","timestamp":1675415105000},"page":"935-954","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Application of EMD Combined with Deep Learning and Knowledge Graph in Bearing Fault"],"prefix":"10.1007","volume":"95","author":[{"given":"Bowei","family":"Qi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanyuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhibo","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,2,3]]},"reference":[{"issue":"4","key":"1845_CR1","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1109\/TEC.2005.847955","volume":"20","author":"S Nandi","year":"2005","unstructured":"Nandi, S., Toliyat, H. A., & Li, X. (2005). Condition monitoring and fault diagnosis of electrical motors\u2014A review. IEEE transactions on energy conversion, 20(4), 719\u2013729.","journal-title":"IEEE transactions on energy conversion"},{"key":"1845_CR2","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1016\/j.rser.2015.11.032","volume":"56","author":"HDM de Azevedo","year":"2016","unstructured":"de Azevedo, H. D. M., Ara\u00fajo, A. M., & Bouchonneau, N. (2016). A review of wind turbine bearing condition monitoring: state of the art and challenges. Renewable and Sustainable Energy Reviews, 56, 368\u2013379.","journal-title":"Renewable and Sustainable Energy Reviews"},{"key":"1845_CR3","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1016\/j.triboint.2015.12.037","volume":"96","author":"A Rai","year":"2016","unstructured":"Rai, A., & Upadhyay, S. H. (2016). A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings. Tribology International, 96, 289\u2013306.","journal-title":"Tribology International"},{"issue":"14","key":"1845_CR4","doi-asserted-by":"publisher","first-page":"3059","DOI":"10.1093\/nar\/gkf436","volume":"30","author":"K Katoh","year":"2002","unstructured":"Katoh, K., Misawa, K., Kuma, K. I., & Miyata, T. (2002). MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic acids research, 30(14), 3059\u20133066.","journal-title":"Nucleic acids research"},{"issue":"6","key":"1845_CR5","doi-asserted-by":"publisher","first-page":"449","DOI":"10.3109\/03005364000000039","volume":"31","author":"KJ Blinowska","year":"1997","unstructured":"Blinowska, K. J., & Durka, P. J. (1997). Introduction to wavelet analysis. British journal of audiology, 31(6), 449\u2013459.","journal-title":"British journal of audiology"},{"key":"1845_CR6","unstructured":"Huang, N. E. (1998). The empirical mode decomposition method and the hilbert spectrum for non-stationary time series. Proc Roy Soc London 45AA, 703\u2013775."},{"key":"1845_CR7","doi-asserted-by":"crossref","unstructured":"Wang, H., Liu, H., Qing, T., Liu, W., & He, T. (2017, July). An automatic fault diagnosis method for aerospace rolling bearings based on ensemble empirical mode decomposition. In 2017 8th International Conference on Mechanical and Aerospace Engineering (ICMAE) (pp.\u00a0502\u2013506). IEEE.","DOI":"10.1109\/ICMAE.2017.8038697"},{"key":"1845_CR8","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1016\/j.ymssp.2017.08.038","volume":"101","author":"Y Zhao","year":"2018","unstructured":"Zhao, Y., Wang, D., Yi, C., Tsui, K. L., & Lin, J. (2018). Sparsity guided empirical wavelet transform for fault diagnosis of rolling element bearings. Mechanical systems and signal processing, 101, 292\u2013308.","journal-title":"Mechanical systems and signal processing"},{"key":"1845_CR9","doi-asserted-by":"crossref","unstructured":"Yan, X., Liu, Y., Ding, P., & Jia, M. (2020). Fault diagnosis of rolling-element bearing using multiscale pattern gradient spectrum entropy coupled with Laplacian score. Complexity, 2020.","DOI":"10.1155\/2020\/4032628"},{"key":"1845_CR10","doi-asserted-by":"publisher","first-page":"106609","DOI":"10.1016\/j.ymssp.2019.106609","volume":"139","author":"Q Hu","year":"2020","unstructured":"Hu, Q., Si, X. S., Zhang, Q. H., & Qin, A. S. (2020). A rotating machinery fault diagnosis method based on multi-scale dimensionless indicators and random forests. Mechanical systems and signal processing, 139, 106609.","journal-title":"Mechanical systems and signal processing"},{"key":"1845_CR11","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.knosys.2016.12.012","volume":"119","author":"H Shao","year":"2017","unstructured":"Shao, H., Jiang, H., Wang, F., & Zhao, H. (2017). An enhancement deep feature fusion method for rotating machinery fault diagnosis. Knowledge-Based Systems, 119, 200\u2013220.","journal-title":"Knowledge-Based Systems"},{"issue":"4","key":"1845_CR12","doi-asserted-by":"publisher","first-page":"4927","DOI":"10.1109\/JSEN.2020.3030910","volume":"21","author":"M Cui","year":"2020","unstructured":"Cui, M., Wang, Y., Lin, X., & Zhong, M. (2020). Fault diagnosis of rolling bearings based on an improved stack autoencoder and support vector machine. IEEE Sensors Journal, 21(4), 4927\u20134937.","journal-title":"IEEE Sensors Journal"},{"key":"1845_CR13","doi-asserted-by":"crossref","unstructured":"Ahmad, S., Styp-Rekowski, K., Nedelkoski, S., & Kao, O. (2020, December). Autoencoder-based condition monitoring and anomaly detection method for rotating machines. In 2020 IEEE International Conference on Big Data (Big Data) (pp.\u00a04093\u20134102). IEEE.","DOI":"10.1109\/BigData50022.2020.9378015"},{"issue":"2","key":"1845_CR14","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/j.cja.2019.07.011","volume":"33","author":"J Zhang","year":"2020","unstructured":"Zhang, J., Yi, S., Liang, G. U. O., Hongli, G. A. O., Xin, H. O. N. G., & Hongliang, S. O. N. G (2020). A new bearing fault diagnosis method based on modified convolutional neural networks. Chinese Journal of Aeronautics, 33(2), 439\u2013447.","journal-title":"Chinese Journal of Aeronautics"},{"key":"1845_CR15","doi-asserted-by":"crossref","unstructured":"Yuan, Z., Zhang, L., Duan, L., & Li, T. (2018, October). Intelligent fault diagnosis of rolling element bearings based on HHT and CNN. In 2018 Prognostics and System Health Management Conference (PHM-Chongqing) (pp.\u00a0292\u2013296). IEEE.","DOI":"10.1109\/PHM-Chongqing.2018.00056"},{"issue":"2","key":"1845_CR16","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1109\/TIM.2019.2902003","volume":"69","author":"G Xu","year":"2019","unstructured":"Xu, G., Liu, M., Jiang, Z., Shen, W., & Huang, C. (2019). Online fault diagnosis method based on transfer convolutional neural networks. IEEE Transactions on Instrumentation and Measurement, 69(2), 509\u2013520.","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"1845_CR17","unstructured":"Nian-Long, G. U., Pan, H., & Peng, H. E. (2017). Bearing fault diagnosis method based on EMD-CNNs. DEStech Transactions on Computer Science and Engineering."},{"key":"1845_CR18","doi-asserted-by":"publisher","first-page":"106515","DOI":"10.1016\/j.asoc.2020.106515","volume":"95","author":"Z Xu","year":"2020","unstructured":"Xu, Z., Li, C., & Yang, Y. (2020). Fault diagnosis of rolling bearing of wind turbines based on the variational mode decomposition and deep convolutional neural networks. Applied Soft Computing, 95, 106515.","journal-title":"Applied Soft Computing"},{"issue":"1","key":"1845_CR19","doi-asserted-by":"publisher","first-page":"59","DOI":"10.3390\/electronics10010059","volume":"10","author":"CC Chen","year":"2020","unstructured":"Chen, C. C., Liu, Z., Yang, G., Wu, C. C., & Ye, Q. (2020). An improved fault diagnosis using 1D-convolutional neural network model. Electronics, 10(1), 59.","journal-title":"Electronics"},{"key":"1845_CR20","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.ymssp.2015.04.021","volume":"64","author":"WA Smith","year":"2015","unstructured":"Smith, W. A., & Randall, R. B. (2015). Rolling element bearing diagnostics using the Case Western Reserve University data: a benchmark study. Mechanical systems and signal processing, 64, 100\u2013131.","journal-title":"Mechanical systems and signal processing"},{"issue":"6","key":"1845_CR21","doi-asserted-by":"publisher","first-page":"1693","DOI":"10.3390\/s20061693","volume":"20","author":"L Wan","year":"2020","unstructured":"Wan, L., Chen, Y., Li, H., & Li, C. (2020). Rolling-element bearing fault diagnosis using improved LeNet-5 network. Sensors (Basel, Switzerland), 20(6), 1693.","journal-title":"Sensors (Basel, Switzerland)"},{"issue":"2","key":"1845_CR22","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1007\/s00202-021-01309-2","volume":"104","author":"IH Ozcan","year":"2022","unstructured":"Ozcan, I. H., Devecioglu, O. C., Ince, T., Eren, L., & Askar, M. (2022). Enhanced bearing fault detection using multichannel, multilevel 1D CNN classifier. Electrical Engineering, 104(2), 435\u2013447.","journal-title":"Electrical Engineering"},{"key":"1845_CR23","doi-asserted-by":"publisher","first-page":"107435","DOI":"10.1016\/j.apacoust.2020.107435","volume":"168","author":"X Zhu","year":"2020","unstructured":"Zhu, X., Luo, X., Zhao, J., Hou, D., Han, Z., & Wang, Y. (2020). Research on deep feature learning and condition recognition method for bearing vibration. Applied Acoustics, 168, 107435.","journal-title":"Applied Acoustics"},{"issue":"9","key":"1845_CR24","doi-asserted-by":"publisher","first-page":"3245","DOI":"10.3390\/su10093245","volume":"10","author":"T Wu","year":"2018","unstructured":"Wu, T., Qi, G., Li, C., & Wang, M. (2018). A survey of techniques for constructing chinese knowledge graphs and their applications. Sustainability, 10(9), 3245.","journal-title":"Sustainability"},{"key":"1845_CR25","doi-asserted-by":"crossref","unstructured":"Su, L., Wang, Z., Ji, Y., & Guo, X. (2020, October). A survey based on knowledge graph in fault diagnosis, analysis and prediction: key technologies and challenges. In 2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE) (pp.\u00a0458\u2013462). IEEE.","DOI":"10.1109\/ICAICE51518.2020.00096"},{"issue":"4","key":"1845_CR26","doi-asserted-by":"publisher","first-page":"1693","DOI":"10.1007\/s10845-017-1351-1","volume":"30","author":"Q Zhou","year":"2019","unstructured":"Zhou, Q., Yan, P., Liu, H., & Xin, Y. (2019). A hybrid fault diagnosis method for mechanical components based on ontology and signal analysis. Journal of Intelligent Manufacturing, 30(4), 1693\u20131715.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"1845_CR27","unstructured":"Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences, 454(1971), 903\u2013995"},{"issue":"12","key":"1845_CR28","doi-asserted-by":"publisher","first-page":"168781401881718","DOI":"10.1177\/1687814018817184","volume":"10","author":"W Mao","year":"2018","unstructured":"Mao, W., He, J., Tang, J., & Li, Y. (2018). Predicting remaining useful life of rolling bearings based on deep feature representation and long short-term memory neural network. Advances in Mechanical Engineering, 10(12), 1687814018817184.","journal-title":"Advances in Mechanical Engineering"},{"key":"1845_CR29","doi-asserted-by":"publisher","first-page":"103182","DOI":"10.1016\/j.compind.2019.103182","volume":"115","author":"T Xia","year":"2020","unstructured":"Xia, T., Song, Y., Zheng, Y., Pan, E., & Xi, L. (2020). An ensemble framework based on convolutional bi-directional LSTM with multiple time windows for remaining useful life estimation. Computers in Industry, 115, 103182.","journal-title":"Computers in Industry"},{"key":"1845_CR30","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zheng, L., Wang, J., & Du, W. (2019). Research on novel bearing fault diagnosis method based on improved krill herd algorithm and kernel extreme learning machine. Complexity, 2019.","DOI":"10.1155\/2019\/4031795"},{"key":"1845_CR31","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017). Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.\u00a02881\u20132890).","DOI":"10.1109\/CVPR.2017.660"},{"key":"1845_CR32","doi-asserted-by":"publisher","first-page":"102174","DOI":"10.1016\/j.bspc.2020.102174","volume":"63","author":"X Zheng","year":"2021","unstructured":"Zheng, X., & Chen, W. (2021). An attention-based bi-LSTM method for visual object classification via EEG. Biomedical Signal Processing and Control, 63, 102174.","journal-title":"Biomedical Signal Processing and Control"},{"issue":"19","key":"1845_CR33","doi-asserted-by":"publisher","first-page":"e5746","DOI":"10.1002\/cpe.5746","volume":"32","author":"A Assi","year":"2020","unstructured":"Assi, A., Mcheick, H., & Dhifli, W. (2020). Data linking over RDF knowledge graphs: a survey. Concurrency and Computation: Practice and Experience, 32(19), e5746.","journal-title":"Concurrency and Computation: Practice and Experience"},{"key":"1845_CR34","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhang, J., Feng, J., & Chen, Z. (2014, June). Knowledge graph embedding by translating on hyperplanes. In Proceedings of the AAAI conference on artificial intelligence (Vol.\u00a028, No. 1).","DOI":"10.1609\/aaai.v28i1.8870"},{"issue":"5","key":"1845_CR35","doi-asserted-by":"publisher","first-page":"2280","DOI":"10.1016\/j.ymssp.2006.11.003","volume":"21","author":"Y Lei","year":"2007","unstructured":"Lei, Y., He, Z., Zi, Y., & Hu, Q. (2007). Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs. Mechanical systems and signal processing, 21(5), 2280\u20132294.","journal-title":"Mechanical systems and signal processing"},{"key":"1845_CR36","doi-asserted-by":"publisher","first-page":"131885","DOI":"10.1109\/ACCESS.2020.3007499","volume":"8","author":"G Xian","year":"2020","unstructured":"Xian, G. (2020). Parallel machine learning algorithm using fine-grained-mode spark on a mesos big data cloud computing software framework for mobile robotic intelligent fault recognition. Ieee Access : Practical Innovations, Open Solutions, 8, 131885\u2013131900.","journal-title":"Ieee Access : Practical Innovations, Open Solutions"},{"issue":"22","key":"1845_CR37","doi-asserted-by":"publisher","first-page":"4827","DOI":"10.3390\/s19224827","volume":"19","author":"H Liu","year":"2019","unstructured":"Liu, H., Yao, D., Yang, J., & Li, X. (2019). Lightweight convolutional neural network and its application in rolling bearing fault diagnosis under variable working conditions. Sensors (Basel, Switzerland), 19(22), 4827.","journal-title":"Sensors (Basel, Switzerland)"},{"key":"1845_CR38","doi-asserted-by":"crossref","unstructured":"Zhu, X., Zhao, J., Hou, D., & Han, Z. (2019). An SDP characteristic information fusion-based CNN vibration fault diagnosis method. Shock and Vibration, 2019.","DOI":"10.1155\/2019\/3926963"},{"key":"1845_CR39","unstructured":"Van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of machine learning research, 9(11)"}],"container-title":["Journal of Signal Processing Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11265-023-01845-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11265-023-01845-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11265-023-01845-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T06:12:14Z","timestamp":1696659134000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11265-023-01845-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,3]]},"references-count":39,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["1845"],"URL":"https:\/\/doi.org\/10.1007\/s11265-023-01845-z","relation":{},"ISSN":["1939-8018","1939-8115"],"issn-type":[{"value":"1939-8018","type":"print"},{"value":"1939-8115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,3]]},"assertion":[{"value":"13 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 November 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 February 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interests"}}]}}