{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T06:56:26Z","timestamp":1760597786891},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319700953"},{"type":"electronic","value":"9783319700960"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-3-319-70096-0_11","type":"book-chapter","created":{"date-parts":[[2017,10,25]],"date-time":"2017-10-25T01:33:18Z","timestamp":1508895198000},"page":"101-109","source":"Crossref","is-referenced-by-count":4,"title":["An End-to-End Approach for Bearing Fault Diagnosis Based on a Deep Convolution Neural Network"],"prefix":"10.1007","author":[{"given":"Liang","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxuan","family":"Zhuang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinghua","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianming","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,10,26]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/S0098-1354(02)00160-6","volume":"27","author":"V Venkatsubramanian","year":"2003","unstructured":"Venkatsubramanian, V., Rengaswamy, R., Yin, K., et al.: A review of process fault detection and diagnosis Part I: quantitative model-based methods. Comput. Chem. Eng. 27, 293\u2013311 (2003)","journal-title":"Comput. Chem. Eng."},{"key":"11_CR2","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.neucom.2015.01.016","volume":"157","author":"Z Su","year":"2015","unstructured":"Su, Z., Tang, B., Liu, Z., et al.: Multi-fault diagnosis for rotating machinery based on orthogonal supervised linear local tangent space alignment and least square support vector machine. Neurocomputing 157, 208\u2013222 (2015)","journal-title":"Neurocomputing"},{"key":"11_CR3","first-page":"1","volume":"2015","author":"Z Chen","year":"2015","unstructured":"Chen, Z., Li, C., Sanchez, R.: Gearbox fault identification and classification with convolutional neural networks. Shock Vibr. 2015, 1\u201310 (2015)","journal-title":"Shock Vibr."},{"key":"11_CR4","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.neucom.2015.06.008","volume":"168","author":"C Li","year":"2015","unstructured":"Li, C., Sanchez, R., Zurita, G., et al.: Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis. Neurocomputing 168, 119\u2013127 (2015)","journal-title":"Neurocomputing"},{"key":"11_CR5","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/j.measurement.2012.08.007","volume":"46","author":"P Li","year":"2013","unstructured":"Li, P., Kong, F., He, Q., et al.: Multiscale slope feature extraction for rotating machinery fault diagnosis using wavelet analysis. Meas. J. Int. Meas. Confederation 46, 497\u2013505 (2013)","journal-title":"Meas. J. Int. Meas. Confederation"},{"key":"11_CR6","first-page":"47","volume":"5","author":"Z Ye","year":"2011","unstructured":"Ye, Z., Yang, C.G., Zhang, J., et al.: Fault diagnosis of railway rolling bearing based on wavelet analysis and FCM. Int. J. Digit. Content Technol Appl. 5, 47\u201358 (2011)","journal-title":"Int. J. Digit. Content Technol Appl."},{"key":"11_CR7","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1016\/j.asoc.2010.04.012","volume":"11","author":"R Eslamloueyan","year":"2011","unstructured":"Eslamloueyan, R.: Designing a hierarchical neural network based on fuzzy clustering for fault diagnosis of the Tennessee-Eastman process. Appl. Soft Comput. J. 11, 1407\u20131415 (2011)","journal-title":"Appl. Soft Comput. J."},{"key":"11_CR8","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1016\/j.measurement.2013.09.019","volume":"47","author":"K Zhu","year":"2014","unstructured":"Zhu, K., Song, X., Xue, D.: A roller bearing fault diagnosis method based on hierarchical entropy and support vector machine with particle swarm optimization algorithm. Measurement 47, 669\u2013675 (2014)","journal-title":"Measurement"},{"key":"11_CR9","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y Lecun","year":"2015","unstructured":"Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436\u2013444 (2015)","journal-title":"Nature"},{"key":"11_CR10","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.ymssp.2015.10.025","volume":"72\u201373","author":"F Jia","year":"2016","unstructured":"Jia, F., Lei, Y., Lin, J., et al.: Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mech. Syst. Signal Process. 72\u201373, 303\u2013315 (2016)","journal-title":"Mech. Syst. Signal Process."},{"key":"11_CR11","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.ymssp.2015.11.014","volume":"72\u201373","author":"M Gan","year":"2016","unstructured":"Gan, M., Wang, C., Zhu, C.: Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings. Mech. Syst. Signal Process. 72\u201373, 92\u2013104 (2016)","journal-title":"Mech. Syst. Signal Process."},{"key":"11_CR12","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.measurement.2016.07.054","volume":"93","author":"X Guo","year":"2016","unstructured":"Guo, X., Chen, L., Shen, C.: Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis. Measurement 93, 490\u2013502 (2016)","journal-title":"Measurement"},{"key":"11_CR13","doi-asserted-by":"crossref","first-page":"2435","DOI":"10.1109\/TAES.2011.6034643","volume":"47","author":"A Barua","year":"2011","unstructured":"Barua, A., Khorasani, K.: Hierarchical fault diagnosis and fuzzy rule-based reasoning for satellites formation flight. IEEE Trans. Aerosp. Electron. Syst. 47, 2435\u20132456 (2011)","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"11_CR14","doi-asserted-by":"crossref","first-page":"2152","DOI":"10.1080\/00207160.2012.710325","volume":"89","author":"S Zhou","year":"2012","unstructured":"Zhou, S., Lin, L., Xu, J.M.: Conditional fault diagnosis of hierarchical hypercubes. Int. J. Comput. Math. 89, 2152\u20132164 (2012)","journal-title":"Int. J. Comput. Math."},{"unstructured":"Gu, Z.J., Wang, C.: A hierarchical model of network fault diagnosis. In: International Conference on Convergence Computer Technology, pp. 128\u2013131. IEEE Computer Society, Washington (2012)","key":"11_CR15"},{"key":"11_CR16","doi-asserted-by":"crossref","first-page":"3529","DOI":"10.1109\/TIE.2012.2213560","volume":"60","author":"B Hu","year":"2013","unstructured":"Hu, B., She, J., Yokoyama, R.: Hierarchical fault diagnosis for power systems based on equivalent-input-disturbance approach. IEEE Trans. Industr. Electron. 60, 3529\u20133538 (2013)","journal-title":"IEEE Trans. Industr. Electron."},{"unstructured":"Wu, Y., Schuster, M., Chen, Z., Le, Q.V., Norouzi, M., et al.: Google\u2019s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation (2016)","key":"11_CR17"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-70096-0_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,10,25]],"date-time":"2017-10-25T01:37:20Z","timestamp":1508895440000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-70096-0_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319700953","9783319700960"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-70096-0_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2017]]}}}