{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:06:40Z","timestamp":1787029600149,"version":"build-2736575974"},"reference-count":38,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T00:00:00Z","timestamp":1695945600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005047","name":"Natural Science Foundation of Liaoning Province","doi-asserted-by":"publisher","award":["2022-KF-12-11"],"award-info":[{"award-number":["2022-KF-12-11"]}],"id":[{"id":"10.13039\/501100005047","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>During industrial production activities, industrial products serve as critical resources whose performance is subject to various external factors and usage conditions. To ensure uninterrupted production processes and to guarantee the safety of the production personnel, a real-time analysis of the industrial product quality and subsequent decision making are essential. Conventional detection methods have inherent limitations in meeting the real-time demands of processing large volumes of data and achieving high response speeds. For instance, the regular inspection and maintenance of cars can be time-consuming and labor-intensive if performed manually. Furthermore, monitoring the damage situation of bearings in real time through a manual inspection may lead to delays and may hinder production efficiency. Therefore, this paper presents online machine-learning-based methods to address these two practical problems and simulates them on various datasets to meet the requirements of efficiency and speed. Prior to being fed into the network for training, the data undergo identity parsing to transform them into easily identifiable streaming data. The training process demonstrates that online machine learning ensures timely model updates as small batches of data are sent to the network. The test results indicate that the online learning method exhibits highly stable and effective performance, optimizing the training process.<\/jats:p>","DOI":"10.3390\/s23198167","type":"journal-article","created":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T07:42:08Z","timestamp":1695973328000},"page":"8167","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Industrial Product Quality Analysis Based on Online Machine Learning"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-0388-8797","authenticated-orcid":false,"given":"Yiming","family":"Yin","sequence":"first","affiliation":[{"name":"School of Physics, Liaoning University, Shenyang 110036, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Wan","sequence":"additional","affiliation":[{"name":"College of Information, Liaoning University, Shenyang 110036, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5565-2352","authenticated-orcid":false,"given":"Panfeng","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Physics, Liaoning University, Shenyang 110036, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Physics, Liaoning University, Shenyang 110036, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Physics, Liaoning University, Shenyang 110036, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Song","sequence":"additional","affiliation":[{"name":"School of Physics, Liaoning University, Shenyang 110036, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S78","DOI":"10.1080\/15389588.2019.1597348","article-title":"Relationship between frontal car-to-car test result and vehicle crash compatibility evaluation in mobile progressive deformable barrier test","volume":"20","author":"Watanabe","year":"2019","journal-title":"Traffic Inj. Prev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4623","DOI":"10.1007\/s00521-020-05291-x","article-title":"A combined deep learning method for internet car evaluation","volume":"33","author":"Li","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"105681","DOI":"10.1016\/j.aap.2020.105681","article-title":"An integrated architecture for intelligence evaluation of automated vehicles","volume":"145","author":"Huang","year":"2020","journal-title":"Accid. Anal. Prev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2845","DOI":"10.1109\/TITS.2017.2766172","article-title":"Accelerated Evaluation of Automated Vehicles Using Piecewise Mixture Models","volume":"19","author":"Huang","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1177\/09544070221080362","article-title":"Correlation between objective and subjective tests for vehicle ride comfort evaluations","volume":"237","author":"Boke","year":"2023","journal-title":"Proc. Inst. Mech. Eng. Part D-J. Automob. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tong, Q.H., Yang, Z.L., Chai, G.O., Wang, Y.J., Qi, Z.H., Wang, F., and Yin, K.C. (2022). Driving state evaluation of intelligent connected vehicles based on centralized multi-source vehicle road collaborative information fusion. Environ. Dev. Sustain.","DOI":"10.1007\/s10668-021-01957-1"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5938","DOI":"10.1109\/TCYB.2022.3219142","article-title":"Quantitative Evaluation Methodology for Chassis-Domain Dynamics Performance of Automated Vehicles","volume":"53","author":"Cheng","year":"2022","journal-title":"IEEE Trans. Cybern."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3999","DOI":"10.1080\/00423114.2021.1986223","article-title":"On dynamic stability evaluation methods for long combination vehicles","volume":"60","author":"Zhu","year":"2022","journal-title":"Veh. Syst. Dyn."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.mechmachtheory.2018.10.016","article-title":"Static structural behaviour of wire bearings under axial load: Comparison with conventional bearings and study of design and operational parameters","volume":"132","author":"Martin","year":"2019","journal-title":"Mech. Mach. Theory"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"109826","DOI":"10.1016\/j.ymssp.2022.109826","article-title":"A comprehensive study on dynamic performance of ball bearing considering bearing deformations and ball-inner raceway separation","volume":"185","author":"Wang","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, W., Peng, G.L., Li, C.H., Chen, Y.H., and Zhang, Z.J. (2017). A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals. Sensors, 17.","DOI":"10.20944\/preprints201701.0132.v1"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yoo, Y., Jo, H., and Ban, S.W. (2023). Lite and Efficient Deep Learning Model for Bearing Fault Diagnosis Using the CWRU Dataset. Sensors, 23.","DOI":"10.3390\/s23063157"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6111","DOI":"10.1007\/s00521-019-04097-w","article-title":"A transfer convolutional neural network for fault diagnosis based on ResNet-50","volume":"32","author":"Wen","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"164807","DOI":"10.1109\/ACCESS.2020.3022840","article-title":"A Generic Intelligent Bearing Fault Diagnosis System Using Convolutional Neural Networks with Transfer Learning","volume":"8","author":"Lu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ullah, Z., Lodhi, B.A., and Hur, J. (2020). Detection and Identification of Demagnetization and Bearing Faults in PMSM Using Transfer Learning-Based VGG. Energies, 13.","DOI":"10.3390\/en13153834"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.ymssp.2017.06.022","article-title":"A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load","volume":"100","author":"Zhang","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_17","first-page":"4125","article-title":"TCDA: Truthful Combinatorial Double Auctions for Mobile Edge Computing in Industrial Internet of Things","volume":"21","author":"Ma","year":"2022","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6684","DOI":"10.1109\/TCYB.2020.3041212","article-title":"An Adaptive Localized Decision Variable Analysis Approach to Large-Scale Multiobjective and Many-Objective Optimization","volume":"52","author":"Ma","year":"2022","journal-title":"IEEE Trans. Cybern."},{"key":"ref_19","first-page":"3117","article-title":"A Survey of Algorithms and Analysis for Adaptive Online Learning","volume":"18","author":"McMahan","year":"2017","journal-title":"J. Mach. Learn. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1007\/s13042-016-0544-9","article-title":"Timeliness online regularized extreme learning machine","volume":"9","author":"Luo","year":"2018","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.neucom.2014.03.076","article-title":"Parallel online sequential extreme learning machine based on MapReduce","volume":"149","author":"Wang","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1007\/s11063-021-10435-8","article-title":"Augmented Online Sequential Quaternion Extreme Learning Machine","volume":"53","author":"Zhu","year":"2021","journal-title":"Neural Process. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"12698","DOI":"10.1109\/TCYB.2021.3086501","article-title":"Learning to Optimize: Reference Vector Reinforcement Learning Adaption to Constrained Many-Objective Optimization of Industrial Copper Burdening System","volume":"52","author":"Ma","year":"2022","journal-title":"IEEE Trans. Cybern."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"6723","DOI":"10.1109\/TSMC.2020.2963943","article-title":"Enhancing Learning Efficiency of Brain Storm Optimization via Orthogonal Learning Design","volume":"51","author":"Ma","year":"2021","journal-title":"IEEE Trans. Syst. Man Cybern.-Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1145\/3200692","article-title":"Combination Forecasting Reversion Strategy for Online Portfolio Selection","volume":"9","author":"Huang","year":"2018","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Alahyari, A., Pozo, D., and Farrokhifar, M. (2021). Online Learning Algorithms for the Real-Time Set-Point Tracking Problem. Appl. Sci., 11.","DOI":"10.3390\/app11146620"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1338","DOI":"10.1109\/TSP.2018.2890368","article-title":"Online Learning with Inexact Proximal Online Gradient Descent Algorithms","volume":"67","author":"Dixit","year":"2019","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jian, L., Gao, F.H., Ren, P., Song, Y.Q., and Luo, S.H. (2018). A Noise-Resilient Online Learning Algorithm for Scene Classification. Remote Sens., 10.","DOI":"10.3390\/rs10111836"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2550","DOI":"10.1109\/TMC.2020.2983924","article-title":"Distributed Online Learning of Cooperative Caching in Edge Cloud","volume":"20","author":"Lyu","year":"2021","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"12671","DOI":"10.1007\/s00521-020-04718-9","article-title":"A Randomized Block-Coordinate Adam online learning optimization algorithm","volume":"32","author":"Zhou","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"118965","DOI":"10.1016\/j.ins.2023.118965","article-title":"Adaptive proximal SGD based on new estimating sequences for sparser ERM","volume":"638","author":"Zhang","year":"2023","journal-title":"Inf. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1268","DOI":"10.1109\/TCOMM.2018.2880482","article-title":"User Preference Learning-Based Edge Caching for Fog Radio Access Network","volume":"67","author":"Jiang","year":"2019","journal-title":"IEEE Trans. Commun."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1049\/rpg2.12275","article-title":"Followed the Regularized Leader (FTRL) prediction model based photovoltaic array reconfiguration for mitigation of mismatch losses in partial shading condition","volume":"16","author":"Gao","year":"2022","journal-title":"IET Renew. Power Gener."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.tcs.2017.11.021","article-title":"Scale-free online learning","volume":"716","author":"Orabona","year":"2018","journal-title":"Theor. Comput. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5136","DOI":"10.1109\/TCYB.2020.3027642","article-title":"Online Reconstruction of Complex Networks from Streaming Data","volume":"52","author":"Wu","year":"2022","journal-title":"IEEE Trans. Cybern."},{"key":"ref_36","first-page":"4945","article-title":"River: Machine learning for streaming data in Python","volume":"22","author":"Montiel","year":"2021","journal-title":"J. Mach. Learn. Res."},{"key":"ref_37","unstructured":"Bohanec, M. (2022, October 20). Car Evaluation. UCI Machine Learning Repository. Available online: https:\/\/archive.ics.uci.edu\/dataset\/19\/car+evaluation."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1885","DOI":"10.1109\/TFUZZ.2020.2988845","article-title":"Online Fuzzy Cognitive Map Learning","volume":"29","author":"Wu","year":"2021","journal-title":"IEEE Trans. Fuzzy Syst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/19\/8167\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:01:50Z","timestamp":1760130110000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/19\/8167"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,29]]},"references-count":38,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["s23198167"],"URL":"https:\/\/doi.org\/10.3390\/s23198167","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,29]]}}}