{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T04:50:53Z","timestamp":1747198253309,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":18,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811961342"},{"type":"electronic","value":"9789811961359"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-19-6135-9_10","type":"book-chapter","created":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T14:04:46Z","timestamp":1666274686000},"page":"123-137","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Multi-channel Fusion Method Based on Tensor for Rolling Bearing Fault Diagnosis"],"prefix":"10.1007","author":[{"given":"Huiming","family":"Jiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Song","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunfei","family":"Shao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"An","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,21]]},"reference":[{"key":"10_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2020.107249","volume":"208","author":"NP Li","year":"2020","unstructured":"Li, N.P., Gebraeel, N., Lei, Y.G., et al.: Remaining useful life prediction based on a multi-sensor data fusion model. Reliab. Eng. Syst. Saf. 208, 107249 (2020)","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.inffus.2013.10.002","volume":"18","author":"MS Safizadeh","year":"2014","unstructured":"Safizadeh, M.S., Latifi, S.K.: Using multi-sensor data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell. Inf. Fusion 18, 1\u20138 (2014)","journal-title":"Inf. Fusion"},{"key":"10_CR3","doi-asserted-by":"publisher","first-page":"716","DOI":"10.1016\/j.ijleo.2018.09.017","volume":"176","author":"XW Zhang","year":"2019","unstructured":"Zhang, X.W., Li, H.S.: Research on transformer fault diagnosis method and calculation model by using fuzzy data fusion in multi-sensor detection system. Optik 176, 716\u2013723 (2019)","journal-title":"Optik"},{"issue":"04","key":"10_CR4","first-page":"172","volume":"39","author":"DC Zhu","year":"2020","unstructured":"Zhu, D.C., Zhang, Y.X., Pan, Y.Y., et al.: Fault diagnosis for rolling element bearings based on multi-sensor signals and CNN. J. Vibr. Shock 39(04), 172\u2013178 (2020)","journal-title":"J. Vibr. Shock"},{"key":"10_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2020.107802","volume":"159","author":"SJ Hao","year":"2020","unstructured":"Hao, S.J., Ge, F.X., Li, Y.M., et al.: Multisensor bearing fault diagnosis based on one-dimensional convolutional long short-term memory networks. Measurement 159, 107802 (2020)","journal-title":"Measurement"},{"key":"10_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2020.106861","volume":"144","author":"M Azamfar","year":"2020","unstructured":"Azamfar, M., Singh, J., Bravo-Imaz, I., et al.: Multisensor data fusion for gearbox fault diagnosis using 2-D convolutional neural network and motor current signature analysis. Mech. Syst. Signal Process. 144, 106861 (2020)","journal-title":"Mech. Syst. Signal Process."},{"key":"10_CR7","doi-asserted-by":"publisher","first-page":"762","DOI":"10.1016\/j.ymssp.2019.05.016","volume":"132","author":"HS Zhao","year":"2019","unstructured":"Zhao, H.S., Zhang, W.: Fault diagnosis method for rolling bearings based on segment tensor rank-(L, L, 1) decomposition. Mech. Syst. Signal Process. 132, 762\u2013775 (2019)","journal-title":"Mech. Syst. Signal Process."},{"key":"10_CR8","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1177\/1475921719843453","volume":"19","author":"M R\u00e9billat","year":"2019","unstructured":"R\u00e9billat, M., Mechbal, N.: Damage localization in geometrically complex aeronautic structures using canonical polyadic decomposition of Lamb wave difference signal tensors. Struct. Health Monit. 19, 305\u2013321 (2019)","journal-title":"Struct. Health Monit."},{"issue":"12","key":"10_CR9","first-page":"253","volume":"41","author":"Y Li","year":"2020","unstructured":"Li, Y., Gong, X.L., Zhao, Q.H.: Hyperspectral image classification based on tensor-based radial basis kernel function and support vector machine. Chin. J. Sci. Inst. 41(12), 253\u2013262 (2020)","journal-title":"Chin. J. Sci. Inst."},{"issue":"3","key":"10_CR10","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1137\/07070111X","volume":"51","author":"TG Kolda","year":"2009","unstructured":"Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455\u2013500 (2009)","journal-title":"SIAM Rev."},{"issue":"13","key":"10_CR11","doi-asserted-by":"publisher","first-page":"3551","DOI":"10.1109\/TSP.2017.2690524","volume":"65","author":"ND Sidiropoulos","year":"2017","unstructured":"Sidiropoulos, N.D., de Lathauwer, L., Fu, X., et al.: Tensor decomposition for signal processing and machine learning. IEEE Trans. Signal Process. 65(13), 3551\u20133582 (2017)","journal-title":"IEEE Trans. Signal Process."},{"issue":"12","key":"10_CR12","doi-asserted-by":"publisher","first-page":"50","DOI":"10.3901\/JME.2000.12.050","volume":"55","author":"CF Hu","year":"2019","unstructured":"Hu, C.F., Wang, Y.X.: Research on multi-channel signal denoising method for multiple faults diagnosis of rolling element bearings based on tensor factorization. J. Mech. Eng. 55(12), 50\u201357 (2019)","journal-title":"J. Mech. Eng."},{"key":"10_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2019.107271","volume":"166","author":"LL Feng","year":"2020","unstructured":"Feng, L.L., Liu, Y.P., Chen, L.X., et al.: Robust block tensor principal component analysis. Signal Process. 166, 107271 (2020)","journal-title":"Signal Process."},{"issue":"04","key":"10_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3901\/JME.2021.04.001","volume":"57","author":"YG Lei","year":"2021","unstructured":"Lei, Y.G., Xu, X.F., Cai, X., et al.: Research on data quality assurance for health condition monitoring of machinery. J. Mech. Eng. 57(04), 1\u20139 (2021)","journal-title":"J. Mech. Eng."},{"issue":"3","key":"10_CR15","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/BF02288367","volume":"1","author":"C Eckart","year":"1936","unstructured":"Eckart, C., Young, G.: The approximation of one matrix by another of lower rank. Psychometrika 1(3), 211\u2013218 (1936)","journal-title":"Psychometrika"},{"issue":"5","key":"10_CR16","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1002\/cem.801","volume":"17","author":"R Bro","year":"2003","unstructured":"Bro, R., Kiers, H.A.L.: A new efficient method for determining the number of components in PARAFAC models. J. Chemom. 17(5), 274\u2013286 (2003)","journal-title":"J. Chemom."},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Liu, K.F., So, H.C., Costa, J.P., et al.: Core consistency diagnostic aided by reconstruction error for accurate enumeration of the number of components in parafac models. In: IEEE International Conference on Acoustics, pp. 6635\u20136639 (2013)","DOI":"10.1109\/ICASSP.2013.6638945"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Javed, K., Gouriveau, R., Zerhouni, N.: A feature extraction procedure based on trigonometric functions and cumulative descriptors to enhance prognostics modeling. In:2013 IEEE Conference on IEEE Prognostics and Health Management (PHM), pp. 1\u20137 (2013)","DOI":"10.1109\/ICPHM.2013.6621413"}],"container-title":["Communications in Computer and Information Science","Neural Computing for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-6135-9_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T14:06:14Z","timestamp":1666274774000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-6135-9_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811961342","9789811961359"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-6135-9_10","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"21 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NCAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Computing for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jinan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 July 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 July 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ncaa2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/dl2link.com\/ncaa2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"205","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"77","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"38% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.09","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.68","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}