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There are some problems in the rolling process, such as insufficient ability of abnormal detection and evaluation, low accuracy of process monitoring, and fault diagnosis. To improve the accuracy of quality-related fault diagnosis, this paper proposes a quality-related process monitoring and diagnosis method for hot-rolled strip based on weighted statistical feature KPLS. Firstly, the process-monitoring and diagnosis model of strip thickness and quality based on the KPLS method is introduced. Then, considering that the KPLS diagnosis method ignores the contribution of process variables to quality, it is easy to misjudge the root cause of quality in the diagnosis process. Based on the rolling mechanism model, the influence weight of strip thickness is constructed. By weighing the statistical data features, a quality diagnosis framework of series structure data fusion is constructed. Finally, the method is applied to the 1580 mm hot-rolling process for industrial verification. The verification results show that the proposed method has higher diagnostic accuracy than PLS, KPLS, and other methods. The results show that the diagnostic model based on weighted statistical feature KPLS has a diagnostic accuracy of more than 96% for strip thickness and quality-related faults.<\/jats:p>","DOI":"10.3390\/s23136038","type":"journal-article","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T01:14:12Z","timestamp":1688087652000},"page":"6038","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Quality-Related Process Monitoring and Diagnosis of Hot-Rolled Strip Based on Weighted Statistical Feature KPLS"],"prefix":"10.3390","volume":"23","author":[{"given":"Hesong","family":"Guo","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China"},{"name":"National Engineering Research Center for Equipment and Technology of Cold Rolled Strip, Yanshan University, Qinhuangdao 066004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianliang","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China"},{"name":"National Engineering Research Center for Equipment and Technology of Cold Rolled Strip, Yanshan University, Qinhuangdao 066004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junhui","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China"},{"name":"National Engineering Research Center for Equipment and Technology of Cold Rolled Strip, Yanshan University, Qinhuangdao 066004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China"},{"name":"National Engineering Research Center for Equipment and Technology of Cold Rolled Strip, Yanshan University, Qinhuangdao 066004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"110628","DOI":"10.1016\/j.measurement.2021.110628","article-title":"Data-driven identification model for associated fault propagation path","volume":"188","author":"Liu","year":"2022","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1108\/AA-10-2018-0174","article-title":"A survey on data-driven process monitoring and diagnostic methods for variation reduction in multi-station assembly systems","volume":"39","author":"Liu","year":"2019","journal-title":"Assem. Autom."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1360\/N092017-00368","article-title":"\u201cFlow\u201d, flow network and dissipative structure\u2014Understanding of the physical system of manufacturing process of manufacturing type","volume":"48","author":"Yin","year":"2018","journal-title":"Sci. Sin. Technol."},{"key":"ref_4","first-page":"38","article-title":"Research status and prospect of intelligent modeling, fault diagnosis and cooperative robust control for whole rolling process quality","volume":"46","author":"Hua","year":"2022","journal-title":"Metall. Ind. Autom."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"810876","DOI":"10.3389\/fbioe.2021.810876","article-title":"Intelligent detection of steel defects based on improved split attention networks","volume":"9","author":"Hao","year":"2022","journal-title":"Front. Bioeng. Biotechnol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"105308","DOI":"10.1016\/j.conengprac.2022.105308","article-title":"A quality-related distributed fault detection method for large-scale sequential processes","volume":"127","author":"Zhang","year":"2022","journal-title":"Control Eng. Pract."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"104528","DOI":"10.1016\/j.chemolab.2022.104528","article-title":"Quality-related fault monitoring for multi-phase batch process based on multiway weighted elastic network","volume":"223","author":"Yao","year":"2022","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"104140","DOI":"10.1016\/j.chemolab.2020.104140","article-title":"Quality relevant over-complete independent component analysis based monitoring for non-linear and non-Gaussian batch process","volume":"205","author":"Peng","year":"2020","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"9454","DOI":"10.1109\/TCYB.2021.3060766","article-title":"A single-side neural network-aided canonical correlation analysis with applications to fault diagnosis","volume":"52","author":"Chen","year":"2022","journal-title":"IEEE Trans. Cybern."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1016\/S0019-0578(07)60174-8","article-title":"Graphical enhancement to support PCA-based process monitoring and fault diagnosis","volume":"43","author":"Ralston","year":"2004","journal-title":"ISA Trans."},{"key":"ref_11","first-page":"398","article-title":"Quality-related fault detection approach based on orthogonal signal correction and modified PLS","volume":"11","author":"Wang","year":"2015","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.jprocont.2018.08.009","article-title":"Parallel quality-related dynamic principal component regression method for chemical process monitoring","volume":"73","author":"Tao","year":"2019","journal-title":"J. Process Control"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3505712","DOI":"10.1109\/TIM.2023.3238692","article-title":"A general quality-related nonlinear process monitoring approach based on input-output kernel PLS","volume":"72","author":"Kong","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yan, S., Lv, L., and Yan, X. (2022). Neural representations for quality-related kernel learning and fault detection. Soft Comput.","DOI":"10.1007\/s00500-022-07022-x"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"18904","DOI":"10.1021\/acsomega.2c01892","article-title":"Nonlinear dynamic process monitoring based on ensemble kernel canonical variate analysis and bayesian inference","volume":"7","author":"Wang","year":"2022","journal-title":"ACS Omega"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.isatra.2020.05.029","article-title":"Incipient fault detection for nonlinear processes based on dynamic multi-block probability related kernel principal component analysis","volume":"105","author":"Cai","year":"2020","journal-title":"ISA Trans."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"132733","DOI":"10.1109\/ACCESS.2021.3115351","article-title":"Quality-related process monitoring based on improved kernel principal component regression","volume":"9","author":"Qi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/s10723-023-09670-1","article-title":"Quality-related fault detection based on approximate kernel partial least squares method","volume":"21","author":"Liu","year":"2023","journal-title":"J. Grid Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.isatra.2016.10.015","article-title":"A nonlinear quality-related fault detection approach based on modified kernel partial least squares","volume":"66","author":"Jiao","year":"2017","journal-title":"ISA Trans."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.cherd.2015.12.015","article-title":"Quality-related fault detection approach based on dynamic kernel partial least squares","volume":"106","author":"Jia","year":"2016","journal-title":"Chem. Eng. Res. Des."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"78343","DOI":"10.1109\/ACCESS.2020.2989917","article-title":"Reliable fault detection and diagnosis of large-scale nonlinear uncertain systems using interval reduced kernel PLS","volume":"8","author":"Fezai","year":"2020","journal-title":"IEEE Access"},{"key":"ref_22","first-page":"683","article-title":"Quality-related fault detection based on efficient kernel PLS","volume":"40","author":"Kong","year":"2023","journal-title":"Control. Theory Appl."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ye, Z., Wu, P., He, Y., and Pan, H. (2023). SSAE-KPLS: A quality-related process monitoring via integrating stacked sparse autoencoder with kernel partial least squares. Can. J. Chem. Eng., 24886.","DOI":"10.1002\/cjce.24886"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1316","DOI":"10.1109\/TIE.2019.2898576","article-title":"A novel hierarchical detection and isolation framework for quality-related multiple faults in large-scale processes","volume":"67","author":"Ma","year":"2020","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.isatra.2020.09.006","article-title":"KPLS-KSER based approach for quality-related monitoring of nonlinear process","volume":"108","author":"Jiao","year":"2021","journal-title":"ISA Trans."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.compchemeng.2018.04.009","article-title":"Deep convolutional neural network model based chemical process fault diagnosis","volume":"115","author":"Wu","year":"2018","journal-title":"Comput. Chem. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"106625","DOI":"10.1016\/j.ymssp.2020.106625","article-title":"Application of neural network algorithm in fault diagnosis of mechanical intelligence","volume":"141","author":"Xu","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1177\/1461348417744302","article-title":"Fault diagnosis of bearing based on the kernel principal component analysis and optimized k-nearest neighbour model","volume":"36","author":"Dong","year":"2017","journal-title":"J. Low Freq. Noise Vib. Act. Control."},{"key":"ref_29","first-page":"16","article-title":"Research status and prospect of quality modeling and abnormal traceability in whole hot strip rolling processes","volume":"46","author":"Ma","year":"2022","journal-title":"Metall. Ind. Autom."},{"key":"ref_30","first-page":"97","article-title":"Kernel partial least squares regression in reproducing kernel hilbert space","volume":"2","author":"Rosipal","year":"2002","journal-title":"J. Mach. Learn. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.chemolab.2005.03.003","article-title":"A novel multivariate regression approach based on kernel partial least squares with orthogonal signal correction","volume":"79","author":"Kim","year":"2005","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, Q., Peng, K., and Dong, J. (2022). Modeling and monitoring for laminar cooling process of hot steel strip rolling with time\u2013space nature. Processes, 10.","DOI":"10.3390\/pr10030589"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhao, Q., Liu, X., and Sun, X. (2022). Analysis of mechanical parameters of asymmetrical rolling dealing with three region percentages in deformation zones. Materials, 15.","DOI":"10.3390\/ma15031219"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1560","DOI":"10.1007\/s11771-017-3561-2","article-title":"Adaptive threading strategy based on rolling characteristics analysis in hot strip rolling","volume":"24","author":"Peng","year":"2017","journal-title":"J. Cent. South Univ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1007\/s00170-017-0188-2","article-title":"Modeling friction coefficient for roll force calculation during hot strip rolling","volume":"92","author":"Li","year":"2017","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3524711","DOI":"10.1109\/TIM.2022.3208652","article-title":"A novel three-stage quality oriented data-driven nonlinear industrial process monitoring strategy","volume":"71","author":"Ma","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/6038\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:03:48Z","timestamp":1760126628000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/6038"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,29]]},"references-count":36,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["s23136038"],"URL":"https:\/\/doi.org\/10.3390\/s23136038","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,29]]}}}