{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T20:00:13Z","timestamp":1777060813151,"version":"3.51.4"},"reference-count":42,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T00:00:00Z","timestamp":1695254400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFE0116300"],"award-info":[{"award-number":["2021YFE0116300"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Prebaked carbon anodes are a critical consumable in the aluminum electrolysis industry. Prebaked carbon anode paste is the intermediate product of the prebaked carbon anode, and its quality significantly impacts the prebaked carbon anode. Therefore, inspecting the quality of the prebaked carbon anode paste is essential. Currently, the quality inspection of the paste still relies on laboratory analysis or manual experience. A laboratory inspection cannot obtain results in real time, while manual inspection poses potential risks. To address these issues, an online intelligent inspection method for prebaked carbon anode paste based on an anomaly detection algorithm was proposed. Firstly, we acquired the temperature of the paste and the power of the kneading motor. Secondly, we transformed these time-series data into images using the Gramian Angular Field (GAF) technique and joined them to create the paste anomaly detection dataset. Thirdly, we trained a matched anomaly detection model based on the PatchCore algorithm. Finally, we compared two advanced models: HaloAE and TSRD. PatchCore performs best on our dataset with an AUC-ROC score of 0.9943, followed by HaloAE (0.9906) and TSRD (0.9811). Our proposed method enables on-time intelligent inspection of prebaked carbon anode paste quality. This eliminates the need for manual inspection, reduces labor requirements, and ensures worker safety.<\/jats:p>","DOI":"10.3390\/systems11090484","type":"journal-article","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T04:53:05Z","timestamp":1695271985000},"page":"484","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Intelligent Online Inspection of the Paste Quality of Prebaked Carbon Anodes Using an Anomaly Detection Algorithm"],"prefix":"10.3390","volume":"11","author":[{"given":"Laiyi","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory for Manufacturing Systems Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingzong","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Manufacturing Systems Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wentao","family":"Yong","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Manufacturing Systems Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Manufacturing Systems Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7316-0174","authenticated-orcid":false,"given":"Maolin","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Manufacturing Systems Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1757-4276","authenticated-orcid":false,"given":"Pingyu","family":"Jiang","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Manufacturing Systems Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,21]]},"reference":[{"key":"ref_1","unstructured":"Stephen, J.L. (2011). Light Metals 2011, Springer."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"106716","DOI":"10.1016\/j.est.2023.106716","article-title":"Carbon-based materials as anode materials for lithium-ion batteries and lithium-ion capacitors: A review","volume":"61","author":"Yuan","year":"2023","journal-title":"J. Energy Storage"},{"key":"ref_3","unstructured":"Perez, S.P., Doval-Gandoy, J., Ferro, A., and Silvestre, F. (2005, January 2\u20136). Quality improvement for anode paste used in electrolytic production of aluminium. Proceedings of the Conference Record of the 2005 Industry Applications Conference, Hong Kong, China."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"17","DOI":"10.4028\/www.scientific.net\/AMR.409.17","article-title":"Influence of Mixing Parameters on the Density and Compaction Behavior of Carbon Anodes Used in Aluminum Production","volume":"409","author":"Azari","year":"2011","journal-title":"Adv. Mater. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3787","DOI":"10.1109\/JSEN.2022.3230361","article-title":"LSTM-Autoencoder-Based Anomaly Detection for Indoor Air Quality Time-Series Data","volume":"23","author":"Wei","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chadha, G.S., Islam, I., Schwung, A., and Ding, S.X. (2021). Deep Convolutional Clustering-Based Time Series Anomaly Detection. Sensors, 21.","DOI":"10.3390\/s21165488"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lin, S.Y., Clarke, R., Birke, R., Schonborn, S., Trigoni, N., and Roberts, S. (2020, January 4\u20138). Anomaly Detection for Time Series Using Vaelstm Hybrid Model. Proceedings of the 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9053558"},{"key":"ref_8","first-page":"703","article-title":"MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks","volume":"Volume 11730","author":"Li","year":"2019","journal-title":"Proceedings of the Artificial Neural Networks and Machine Learning\u2014ICANN 2019: Text and Time Series"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Niu, Z., Yu, K., and Wu, X. (2020). LSTM-Based VAE-GAN for Time-Series Anomaly Detection. Sensors, 20.","DOI":"10.3390\/s20133738"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bashar, M.A., and Nayak, R. (2020, January 1\u20134). TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networks. Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia.","DOI":"10.1109\/SSCI47803.2020.9308512"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Geiger, A., Liu, D., Alnegheimish, S., Cuesta-Infante, A., and Veeramachaneni, K. (2020, January 10\u201313). TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks. Proceedings of the IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA.","DOI":"10.1109\/BigData50022.2020.9378139"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhan, J., Wang, S.Q., Ma, X.D., Wu, C.K., Yang, C.Q., Zeng, D.T., and Wang, S.L. (2022, January 23\u201327). STGAT-MAD: Spatial-temporal graph attention network for multivariate time series anomaly detection. Proceedings of the 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore.","DOI":"10.1109\/ICASSP43922.2022.9747274"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"110791","DOI":"10.1016\/j.measurement.2022.110791","article-title":"Variational transformer-based anomaly detection approach for multivariate time series","volume":"191","author":"Wang","year":"2022","journal-title":"Measurement"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Jain, S., Seal, A., Ojha, A., Yazidi, A., Bures, J., Tacheci, I., and Krejcar, O. (2021). A deep CNN model for anomaly detection and localization in wireless capsule endoscopy images. Comput. Biol. Med., 137.","DOI":"10.1016\/j.compbiomed.2021.104789"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"107007","DOI":"10.1016\/j.compag.2022.107007","article-title":"Joint optimization of autoencoder and Self-Supervised Classifier: Anomaly detection of strawberries using hyperspectral imaging","volume":"198","author":"Liu","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Mathian, E., Liu, H.W., Fernandez-Cuesta, L., Samaras, D., Foll, M., and Chen, L. (2022). HaloAE: An HaloNet based Local Transformer Auto-Encoder for Anomaly Detection and Localization. arXiv.","DOI":"10.5220\/0011865900003417"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Deng, H., and Li, X. (2022, January 18\u201324). Anomaly Detection via Reverse Distillation from One-Class Embedding. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00951"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Roth, K., Pemula, L., Zepeda, J., Sch Olkopf, B., Brox, T., and Gehler, P. (2022, January 18\u201324). Towards Total Recall in Industrial Anomaly Detection. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01392"},{"key":"ref_19","unstructured":"Bergman, L., Cohen, N., and Hoshen, Y. (2020). Deep Nearest Neighbor Anomaly Detection. arXiv."},{"key":"ref_20","unstructured":"Cohen, N., and Hoshen, Y. (2021). Sub-Image Anomaly Detection with Deep Pyramid Correspondences. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3232","DOI":"10.1109\/ACCESS.2023.3234745","article-title":"SA-PatchCore: Anomaly Detection in Dataset with Co-Occurrence Relationships Using Self-Attention","volume":"11","author":"Ishida","year":"2023","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1007\/978-3-319-59050-9_12","article-title":"Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery","volume":"Volume 10265","author":"Schlegl","year":"2017","journal-title":"Proceedings of the Information Processing in Medical Imaging\u2014IPMI 2017"},{"key":"ref_23","unstructured":"Zenati, H., Foo, C.S., Lecouat, B., Manek, G., and Chandrasekhar, V.R. (2018). Efficient GAN-Based Anomaly Detection. arXiv."},{"key":"ref_24","first-page":"622","article-title":"GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training","volume":"Volume 11363","author":"Akcay","year":"2018","journal-title":"Proceedings of the Computer Vision\u2014ACCV 2018"},{"key":"ref_25","unstructured":"Dhariwal, P., and Nichol, A. (2021, January 6\u201314). Diffusion Models Beat GANs on Image Synthesis. Proceedings of the Advances in Neural Information Processing Systems, virtual."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wyatt, J., Leach, A., Schmon, S.M., and Willcocks, C.G. (2022, January 19\u201320). AnoDDPM: Anomaly Detection with Denoising Diffusion Probabilistic Models using Simplex Noise. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), New Orleans, LA, USA.","DOI":"10.1109\/CVPRW56347.2022.00080"},{"key":"ref_27","first-page":"35","article-title":"Diffusion Models for Medical Anomaly Detection","volume":"Volume 13438","author":"Wolleb","year":"2022","journal-title":"Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2022"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yang, C., Yang, C., Chen, Z., and Lo, N. (2019, January 14\u201316). Multivariate Time Series Data Transformation for Convolutional Neural Network. Proceedings of the 2019 IEEE\/SICE International Symposium on System Integration (SII), Paris, France.","DOI":"10.1109\/SII.2019.8700425"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"122121","DOI":"10.1016\/j.fuel.2021.122121","article-title":"Determination of alcohols-diesel oil by near infrared spectroscopy based on gramian angular field image coding and deep learning","volume":"309","author":"Liu","year":"2022","journal-title":"Fuel"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"103390","DOI":"10.1016\/j.autcon.2020.103390","article-title":"Detecting excessive load-carrying tasks using a deep learning network with a Gramian Angular Field","volume":"120","author":"Lee","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1016\/j.ins.2022.11.027","article-title":"A Gramian angular field-based data-driven approach for multiregion and multisource renewable scenario generation","volume":"619","author":"Wu","year":"2023","journal-title":"Inf. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ma, K., Zhan, C.A., and Yang, F. (2022). Multi-classification of arrhythmias using ResNet with CBAM on CWGAN-GP augmented ECG Gramian Angular Summation Field. Biomed. Signal Process. Control, 77.","DOI":"10.1016\/j.bspc.2022.103684"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"113680","DOI":"10.1016\/j.eswa.2020.113680","article-title":"Forecasting with time series imaging","volume":"160","author":"Li","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_34","unstructured":"Xu, L., Zheng, L., Li, W., Chen, Z., Song, W., Deng, Y., Chang, Y., Xiao, J., and Yuan, B. (2021). NVAE-GAN Based Approach for Unsupervised Time Series Anomaly Detection. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"112161","DOI":"10.1016\/j.nucengdes.2023.112161","article-title":"Attention-based time series analysis for data-driven anomaly detection in nuclear power plants","volume":"404","author":"Dong","year":"2023","journal-title":"Nucl. Eng. Des."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Oh, S., Oh, S., Um, T., Kim, J., and Jung, Y. (2022). Methods of Pre-Clustering and Generating Time Series Images for Detecting Anomalies in Electric Power Usage Data. Electronics, 11.","DOI":"10.3390\/electronics11203315"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3727","DOI":"10.1007\/s11063-022-10783-z","article-title":"Time Series Classification Based on Image Transformation Using Feature Fusion Strategy","volume":"54","author":"Jiang","year":"2022","journal-title":"Neural Process. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5990","DOI":"10.1109\/TIE.2017.2774777","article-title":"A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method","volume":"65","author":"Wen","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1343","DOI":"10.1109\/JSEN.2022.3225189","article-title":"Fault Diagnosis of Conventional Circuit Breaker Accessories Based on Grayscale Image of Current Signal and Improved ZFNet-DRN","volume":"23","author":"Sun","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"105786","DOI":"10.1016\/j.engappai.2022.105786","article-title":"From time-series to 2D images for building occupancy prediction using deep transfer learning","volume":"119","author":"Sayed","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"105775","DOI":"10.1016\/j.engappai.2022.105775","article-title":"An innovative deep anomaly detection of building energy consumption using energy time-series images","volume":"119","author":"Copiaco","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5902","DOI":"10.1109\/TII.2022.3201011","article-title":"Multisensor-Driven Motor Fault Diagnosis Method Based on Visual Features","volume":"19","author":"Tang","year":"2023","journal-title":"IEEE Trans. Ind. Inform."}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/11\/9\/484\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:54:22Z","timestamp":1760129662000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/11\/9\/484"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,21]]},"references-count":42,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["systems11090484"],"URL":"https:\/\/doi.org\/10.3390\/systems11090484","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,21]]}}}