{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T23:10:06Z","timestamp":1750979406782,"version":"3.41.0"},"publisher-location":"Singapore","reference-count":29,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811071782"},{"type":"electronic","value":"9789811071799"}],"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\/978-981-10-7179-9_21","type":"book-chapter","created":{"date-parts":[[2017,11,8]],"date-time":"2017-11-08T04:53:32Z","timestamp":1510116812000},"page":"274-285","source":"Crossref","is-referenced-by-count":1,"title":["Fault Diagnosis in Aluminium Electrolysis Using a Joint Method Based on Kernel Principal Component Analysis and Support Vector Machines"],"prefix":"10.1007","author":[{"given":"Kaibo","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gaofeng","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongting","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sihai","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,11,9]]},"reference":[{"key":"21_CR1","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1016\/j.asoc.2017.08.024","volume":"61","author":"C He","year":"2017","unstructured":"He, C., Tian, Y., Jin, Y.C., Zhang, X.Y., Pan, L.Q.: A radial space division based evolutionary algorithm for many-objective optimization. Appl. Soft. Comput. 61, 603\u2013621 (2017)","journal-title":"Appl. Soft. Comput."},{"key":"21_CR2","doi-asserted-by":"crossref","unstructured":"He, Y., Du, C.Y., Li, C.B., et al.: Sensor fault diagnosis of superconducting fault current limiter with saturated iron core based on SVM. IEEE Trans. Appl. Supercond. 24(5), 5602805(5 pp.) (2014)","DOI":"10.1109\/TASC.2014.2352391"},{"issue":"2","key":"21_CR3","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1109\/72.991427","volume":"13","author":"CW Hsu","year":"2002","unstructured":"Hsu, C.W., Lin, C.J.: A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Netw. 13(2), 415\u2013425 (2002)","journal-title":"IEEE Trans. Neural Netw."},{"key":"21_CR4","doi-asserted-by":"crossref","unstructured":"Guo, S.H., Zhou, K.B., Cao, B., Yang, C.H.: Combination weights and TOPSIS method for performance evaluation of aluminum electrolysis. In: 2015 Chinese Automation Congress (CAC), Wuhan, Hubei, China, 27\u201329 November 2015, pp. 1\u20136 (2015)","DOI":"10.1109\/CAC.2015.7382459"},{"issue":"2","key":"21_CR5","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1109\/97.991133","volume":"9","author":"KI Kim","year":"2002","unstructured":"Kim, K.I., Jung, K., Kim, H.J.: Face recognition using kernel principal component analysis. IEEE Signal Process. Lett. 9(2), 40\u201342 (2002)","journal-title":"IEEE Signal Process. Lett."},{"issue":"12","key":"21_CR6","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1049\/el:20000780","volume":"36","author":"KI Kim","year":"2000","unstructured":"Kim, K.I., Jung, K., Park, S.H., et al.: Texture classification with kernel principal component analysis. Electron. Lett. 36(12), 1021\u20131022 (2000)","journal-title":"Electron. Lett."},{"issue":"5","key":"21_CR7","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1109\/MCS.2002.1035214","volume":"22","author":"T Kourti","year":"2002","unstructured":"Kourti, T.: Process analysis and abnormal situation detection: from theory to practice. IEEE Control Syst. 22(5), 10\u201325 (2002)","journal-title":"IEEE Control Syst."},{"issue":"14","key":"21_CR8","doi-asserted-by":"crossref","first-page":"2824","DOI":"10.3923\/itj.2013.2824.2830","volume":"12","author":"J Li","year":"2013","unstructured":"Li, J., Guan, W., Zhou, P.: Optimal control strategy research on aluminium electrolysis fault diagnosis system. Inf. Technol. J. 12(14), 2824\u20132830 (2013)","journal-title":"Inf. Technol. J."},{"key":"21_CR9","doi-asserted-by":"crossref","unstructured":"Li, J., Qiao, F., Guo, T.: Neural network fault prediction and its application. In: 8th World Congress on Intelligent Control and Automation (WCICA), Shandong, Jinan, China, 6\u20137 July 2010, pp. 740\u2013743 (2010)","DOI":"10.1109\/WCICA.2010.5554056"},{"key":"21_CR10","doi-asserted-by":"crossref","unstructured":"Li, J., Wu, H., Pian, J.: The application of the equipment fault diagnosis based on modified Elman neural network. In: International Conference on Electronic and Mechanical Engineering and Information Technology (EMEIT), Harbin, Heilongjiang, China, 12\u201314 August 2011, pp. 4135\u20134137 (2011)","DOI":"10.1109\/EMEIT.2011.6023961"},{"issue":"2","key":"21_CR11","doi-asserted-by":"crossref","first-page":"1198","DOI":"10.1109\/TDEI.2015.005277","volume":"23","author":"J Li","year":"2016","unstructured":"Li, J., Zhang, Q., Wang, K., et al.: Optimal dissolved gas ratios selected by genetic algorithm for power transformer fault diagnosis based on support vector machine. IEEE Trans. Dielectr. Electr. Insul. 23(2), 1198\u20131206 (2016)","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"21_CR12","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1016\/j.rser.2014.11.021","volume":"43","author":"B Lin","year":"2015","unstructured":"Lin, B., Xu, L.: Energy conservation of electrolytic aluminium industry in China. Renew. Sustain. Energy Rev. 43, 676\u2013686 (2015)","journal-title":"Renew. Sustain. Energy Rev."},{"issue":"4","key":"21_CR13","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/j.conengprac.2010.12.005","volume":"19","author":"NAA Majid","year":"2011","unstructured":"Majid, N.A.A., Taylor, M.P., Chen, J.J.J., et al.: Aluminium process fault detection by multiway principal component analysis. Control Eng. Pract. 19(4), 367\u2013379 (2011)","journal-title":"Control Eng. Pract."},{"issue":"11","key":"21_CR14","doi-asserted-by":"crossref","first-page":"2457","DOI":"10.1016\/j.compchemeng.2011.03.001","volume":"35","author":"NAA Majid","year":"2011","unstructured":"Majid, N.A.A., Taylor, M.P., Chen, J.J.J., et al.: Multivariate statistical monitoring of the aluminium smelting process. Comput. Chem. Eng. 35(11), 2457\u20132468 (2011)","journal-title":"Comput. Chem. Eng."},{"key":"21_CR15","doi-asserted-by":"crossref","unstructured":"Majid, N.A.A., Young, B.R., Taylor, M.P., et al.: K-means clustering pre-analysis for fault diagnosis in an aluminium smelting process. In: Proceedings of the 2012 4th Conference on Data Mining and Optimization (DMO), Piscataway, NJ, USA, 2\u20134 September 2012, pp. 43\u201346 (2012)","DOI":"10.1109\/DMO.2012.6329796"},{"issue":"3","key":"21_CR16","first-page":"1","volume":"24","author":"L Pan","year":"2017","unstructured":"Pan, L., He, C., Tian, Y., et al.: A region division based diversity maintaining approach for many-objective optimization. Integr. Comput. Aided Eng. 24(3), 1\u201318 (2017)","journal-title":"Integr. Comput. Aided Eng."},{"issue":"10","key":"21_CR17","doi-asserted-by":"crossref","first-page":"2313","DOI":"10.1109\/TIM.2016.2575318","volume":"65","author":"L Ren","year":"2016","unstructured":"Ren, L., Lv, W., Jiang, S., et al.: Fault diagnosis using a joint model based on sparse representation and SVM. IEEE Trans. Instrum. Meas. 65(10), 2313\u20132320 (2016)","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"3","key":"21_CR18","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1109\/TSMCC.2004.843228","volume":"35","author":"B Ribeiro","year":"2005","unstructured":"Ribeiro, B.: Support vector machines for quality monitoring in a plastic injection molding process. IEEE Trans. Syst. Man Cybern. Part C 35(3), 401\u2013410 (2005)","journal-title":"IEEE Trans. Syst. Man Cybern. Part C"},{"key":"21_CR19","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4757-3264-1","volume-title":"The Nature of Statistical Learning Theory","author":"V Vapnik","year":"2000","unstructured":"Vapnik, V.: The Nature of Statistical Learning Theory, 2nd edn. Springer, New York (2000)","edition":"2"},{"issue":"3","key":"21_CR20","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/S0098-1354(02)00162-X","volume":"27","author":"V Venkatasubramanian","year":"2003","unstructured":"Venkatasubramanian, V., Rengaswamy, R., Kavuri, S.N., et al.: A review of process fault detection and diagnosis: Part III: process history based methods. Comput. Chem. Eng. 27(3), 327\u2013346 (2003)","journal-title":"Comput. Chem. Eng."},{"key":"21_CR21","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1023\/A:1015533928104","volume":"32","author":"H Vogt","year":"2002","unstructured":"Vogt, H., Thonstad, J.: The voltage of alumina reduction cells prior to the anode effect. J. Appl. Electrochem. 32, 241\u2013249 (2002)","journal-title":"J. Appl. Electrochem."},{"key":"21_CR22","doi-asserted-by":"crossref","unstructured":"Xia, M., Kong, F., Hu, F.: An approach for bearing fault diagnosis based on PCA and multiple classifier fusion. In: IEEE Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China, 20\u201322 August 2011, pp. 321\u2013325 (2011)","DOI":"10.1109\/ITAIC.2011.6030215"},{"issue":"4","key":"21_CR23","doi-asserted-by":"crossref","first-page":"2595","DOI":"10.1109\/TIE.2016.2515057","volume":"63","author":"J Yi","year":"2016","unstructured":"Yi, J., Huang, D., Fu, S., et al.: Optimized relative transformation matrix using bacterial foraging algorithm for process fault detection. IEEE Trans. Ind. Electron. 63(4), 2595\u20132605 (2016)","journal-title":"IEEE Trans. Ind. Electron."},{"issue":"11","key":"21_CR24","doi-asserted-by":"crossref","first-page":"6356","DOI":"10.1109\/TIE.2014.2312885","volume":"61","author":"S Yin","year":"2014","unstructured":"Yin, S., Gao, H., Kaynak, O.: Data-driven control and process monitoring for industrial applications-Part I. IEEE Trans. Ind. Electron. 61(11), 6356\u20136359 (2014)","journal-title":"IEEE Trans. Ind. Electron."},{"issue":"1","key":"21_CR25","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/TIE.2014.2328316","volume":"62","author":"S Yin","year":"2015","unstructured":"Yin, S., Gao, H., Kaynak, O.: Data-driven control and process monitoring for industrial applications-Part II. IEEE Trans. Ind. Electron. 62(1), 583\u2013586 (2015)","journal-title":"IEEE Trans. Ind. Electron."},{"issue":"1","key":"21_CR26","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1109\/TIE.2014.2319216","volume":"62","author":"D You","year":"2015","unstructured":"You, D., Gao, X., Katayama, S.: WPD-PCA-based laser welding process monitoring and defects diagnosis by using FNN and SVM. IEEE Trans. Ind. Electron. 62(1), 628\u2013636 (2015)","journal-title":"IEEE Trans. Ind. Electron."},{"issue":"20","key":"21_CR27","doi-asserted-by":"crossref","first-page":"4403","DOI":"10.1021\/ie000141+","volume":"40","author":"HH Yue","year":"2001","unstructured":"Yue, H.H., Qin, S.J.: Reconstruction-based fault identification using a combined index. Ind. Eng. Chem. Res. 40(20), 4403\u20134414 (2001)","journal-title":"Ind. Eng. Chem. Res."},{"key":"21_CR28","doi-asserted-by":"crossref","unstructured":"Zhou, K., Lin, Z., Yu, D., et al.: Cell resistance slope combined with LVQ neural network for prediction of anode effect. In: Sixth International Conference on Intelligent Control and Information Processing (ICICIP), Wuhan, Hubei, China, 26\u201328 November 2015, pp. 47\u201351 (2015)","DOI":"10.1109\/ICICIP.2015.7388142"},{"key":"21_CR29","doi-asserted-by":"crossref","unstructured":"Zhou, K., Yu, D., Lin, Z., et al.: Anode effect prediction of aluminium electrolysis using GRNN. In: Chinese Automation Congress (CAC), Wuhan, Hubei, China, 27\u201329 November 2015, pp. 853\u2013858 (2015)","DOI":"10.1109\/CAC.2015.7382617"}],"container-title":["Communications in Computer and Information Science","Bio-inspired Computing: Theories and Applications"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-10-7179-9_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T22:34:34Z","timestamp":1750977274000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-10-7179-9_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9789811071782","9789811071799"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-981-10-7179-9_21","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2017]]}}}