{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T23:27:27Z","timestamp":1773962847155,"version":"3.50.1"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T00:00:00Z","timestamp":1736467200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T00:00:00Z","timestamp":1736467200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","award":["NRF-2022R1A2C2004457"],"award-info":[{"award-number":["NRF-2022R1A2C2004457"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100004358","name":"Samsung","doi-asserted-by":"publisher","award":["IO201210-07929-01"],"award-info":[{"award-number":["IO201210-07929-01"]}],"id":[{"id":"10.13039\/100004358","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002701","name":"Ministry of Education","doi-asserted-by":"publisher","award":["BK21 FOUR"],"award-info":[{"award-number":["BK21 FOUR"]}],"id":[{"id":"10.13039\/501100002701","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s10845-024-02557-2","type":"journal-article","created":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T04:07:23Z","timestamp":1736482043000},"page":"525-547","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Switch ON\/OFF learning of one-dimensional convolutional neural network and one-dimensional generative adversarial network for fault detection"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2497-3785","authenticated-orcid":false,"given":"Seunghwan","family":"Song","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kyuchang","family":"Chang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheolsoon","family":"Park","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7088-1478","authenticated-orcid":false,"given":"Jun-Geol","family":"Baek","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,10]]},"reference":[{"key":"2557_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113922","volume":"165","author":"ISA Abdelhalim","year":"2021","unstructured":"Abdelhalim, I. S. A., Mohamed, M. F., & Mahdy, Y. B. (2021). Data augmentation for skin lesion using self-attention based progressive generative adversarial network. Expert Systems with Applications, 165, 113922. https:\/\/doi.org\/10.1016\/j.eswa.2020.113922","journal-title":"Expert Systems with Applications"},{"key":"2557_CR2","doi-asserted-by":"publisher","unstructured":"Aggarwal, U., Popescu, A., & Hudelot, C. (2020). Active learning for imbalanced datasets. In Proceedings of the 2020 IEEE\/CVF winter conference on applications of computer vision (pp. 1428\u20131437). https:\/\/doi.org\/10.1109\/wacv45572.2020.9093475","DOI":"10.1109\/wacv45572.2020.9093475"},{"key":"2557_CR3","doi-asserted-by":"publisher","unstructured":"Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein generative adversarial networks. In Proceedings of the 2017 international conference on machine learning (pp. 214\u2013223). https:\/\/doi.org\/10.48550\/arXiv.1701.07875","DOI":"10.48550\/arXiv.1701.07875"},{"key":"2557_CR4","doi-asserted-by":"publisher","unstructured":"Batista, G. E., Carvalho, A. C., & Monard, M. C. (2000). Applying one-sided selection to unbalanced datasets. In Proceedings of the 2000 Mexican international conference on artificial intelligence (pp. 315\u2013325). https:\/\/doi.org\/10.1007\/10720076_29","DOI":"10.1007\/10720076_29"},{"key":"2557_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.comgeo.2022.101923","volume":"109","author":"K Buchin","year":"2023","unstructured":"Buchin, K., L\u00f6ffler, M., Ophelders, T., Popov, A., Urhausen, J., & Verbeek, K. (2023). Computing the Fr\u00e9chet distance between uncertain curves in one dimension. Computational Geometry, 109, 101923. https:\/\/doi.org\/10.1016\/j.comgeo.2022.101923","journal-title":"Computational Geometry"},{"issue":"11","key":"2557_CR6","doi-asserted-by":"publisher","first-page":"3880","DOI":"10.3390\/s21113880","volume":"21","author":"K Chang","year":"2021","unstructured":"Chang, K., Yoo, Y., & Baek, J. G. (2021). Anomaly detection using signal segmentation and one-class classification in diffusion process of semiconductor manufacturing. Sensors, 21(11), 3880. https:\/\/doi.org\/10.3390\/s21113880","journal-title":"Sensors"},{"key":"2557_CR7","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321\u2013357. https:\/\/doi.org\/10.1613\/jair.953","journal-title":"Journal of Artificial Intelligence Research"},{"issue":"6","key":"2557_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-024-10759-6","volume":"57","author":"W Chen","year":"2024","unstructured":"Chen, W., Yang, K., Yu, Z., Shi, Y., & Chen, C. L. (2024). A survey on imbalanced learning: Latest research, applications and future directions. Artificial Intelligence Review, 57(6), 1\u201351. https:\/\/doi.org\/10.1007\/s10462-024-10759-6","journal-title":"Artificial Intelligence Review"},{"key":"2557_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109535","volume":"253","author":"W Chen","year":"2022","unstructured":"Chen, W., Yang, K., Yu, Z., & Zhang, W. (2022). Double-kernel based class-specific broad learning system for multiclass imbalance learning. Knowledge-Based Systems, 253, 109535. https:\/\/doi.org\/10.1016\/j.knosys.2022.109535","journal-title":"Knowledge-Based Systems"},{"issue":"5","key":"2557_CR10","doi-asserted-by":"publisher","first-page":"2387","DOI":"10.1007\/s10845-023-02163-8","volume":"35","author":"J Chung","year":"2024","unstructured":"Chung, J., Shen, B., & Kong, Z. J. (2024). Anomaly detection in additive manufacturing processes using supervised classification with imbalanced sensor data based on generative adversarial network. Journal of Intelligent Manufacturing, 35(5), 2387\u20132406. https:\/\/doi.org\/10.1007\/s10845-023-02163-8","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"5","key":"2557_CR11","doi-asserted-by":"publisher","first-page":"2542","DOI":"10.3390\/s23052542","volume":"23","author":"C Deng","year":"2023","unstructured":"Deng, C., Deng, Z., Lu, S., He, M., Miao, J., & Peng, Y. (2023). Fault diagnosis method for imbalanced data based on multi-signal fusion and improved deep convolution generative adversarial network. Sensors, 23(5), 2542. https:\/\/doi.org\/10.3390\/s23052542","journal-title":"Sensors"},{"key":"2557_CR12","doi-asserted-by":"publisher","unstructured":"Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009). ImageNet: A large-scale hierarchical image database. In Proceedings of the 2009 IEEE conference on computer vision and pattern recognition (pp. 248\u2013255). https:\/\/doi.org\/10.1109\/cvpr.2009.5206848","DOI":"10.1109\/cvpr.2009.5206848"},{"key":"2557_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114060","volume":"166","author":"A Dogan","year":"2021","unstructured":"Dogan, A., & Birant, D. (2021). Machine learning and data mining in manufacturing. Expert Systems with Applications, 166, 114060. https:\/\/doi.org\/10.1016\/j.eswa.2020.114060","journal-title":"Expert Systems with Applications"},{"key":"2557_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2018.06.056","volume":"465","author":"G Douzas","year":"2018","unstructured":"Douzas, G., Bacao, F., & Last, F. (2018). Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE. Information Sciences, 465, 1\u201320. https:\/\/doi.org\/10.1016\/j.ins.2018.06.056","journal-title":"Information Sciences"},{"key":"2557_CR15","doi-asserted-by":"publisher","DOI":"10.21767\/2472-1956.100011","author":"T Elhassan","year":"2017","unstructured":"Elhassan, T., & Aljurf, M. (2017). Classification of imbalance data using tomek link (t-link) combined with random under-sampling (rus) as a data reduction method. Global Journal of Technology & Optimization. https:\/\/doi.org\/10.21767\/2472-1956.100011","journal-title":"Global Journal of Technology & Optimization"},{"issue":"3","key":"2557_CR16","doi-asserted-by":"publisher","first-page":"1530","DOI":"10.1109\/TASE.2022.3141426","volume":"19","author":"SKS Fan","year":"2022","unstructured":"Fan, S. K. S., Hsu, C. Y., Tsai, D. M., Chou, M. C., Jen, C. H., & Tsou, J. H. (2022). Key feature identification for monitoring wafer-to-wafer variation in semiconductor manufacturing. IEEE Transactions on Automation Science and Engineering, 19(3), 1530\u20131541. https:\/\/doi.org\/10.1109\/TASE.2022.3141426","journal-title":"IEEE Transactions on Automation Science and Engineering"},{"issue":"4","key":"2557_CR17","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1109\/TSMCC.2011.2161285","volume":"42","author":"M Galar","year":"2011","unstructured":"Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., & Herrera, F. (2011). A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(4), 463\u2013484. https:\/\/doi.org\/10.1109\/TSMCC.2011.2161285","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)"},{"issue":"4","key":"2557_CR18","first-page":"42","volume":"2","author":"V Ganganwar","year":"2012","unstructured":"Ganganwar, V. (2012). An overview of classification algorithms for imbalanced datasets. International Journal of Emerging Technology and Advanced Engineering, 2(4), 42\u201347.","journal-title":"International Journal of Emerging Technology and Advanced Engineering"},{"issue":"7","key":"2557_CR19","doi-asserted-by":"publisher","first-page":"4845","DOI":"10.1007\/s10994-022-06268-8","volume":"113","author":"K Ghosh","year":"2024","unstructured":"Ghosh, K., Bellinger, C., Corizzo, R., Branco, P., Krawczyk, B., & Japkowicz, N. (2024). The class imbalance problem in deep learning. Machine Learning, 113(7), 4845\u20134901. https:\/\/doi.org\/10.1007\/s10994-022-06268-8","journal-title":"Machine Learning"},{"key":"2557_CR20","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672\u20132680)."},{"key":"2557_CR21","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. C. (2017). Improved training of Wasserstein GANS. In Advances in neural information processing systems (pp. 5767\u20135777)."},{"key":"2557_CR22","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017). GANS trained by a two time-scale update rule converge to a local Nash equilibrium. In Advances in neural information processing systems (Vol. 30)."},{"key":"2557_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-020-01591-0","author":"CY Hsu","year":"2020","unstructured":"Hsu, C. Y., & Liu, W. C. (2020). Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing. Journal of Intelligent Manufacturing. https:\/\/doi.org\/10.1007\/s10845-020-01591-0","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"441","key":"2557_CR24","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1002\/9781118445112.stat05501","volume":"93","author":"Y Kakizawa","year":"1998","unstructured":"Kakizawa, Y., Shumway, R. H., & Taniguchi, M. (1998). Discrimination and clustering for multivariate time series. Journal of the American Statistical Association, 93(441), 328\u2013340. https:\/\/doi.org\/10.1002\/9781118445112.stat05501","journal-title":"Journal of the American Statistical Association"},{"key":"2557_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41586-023-05773-7","volume":"616","author":"KJ Kanarik","year":"2023","unstructured":"Kanarik, K. J., Osowiecki, W. T., Lu, Y., Talukder, D., Roschewsky, N., Park, S. N., Kamon, M., Fried, D. M., & Gottscho, R. A. (2023). Human\u2013machine collaboration for improving semiconductor process development. Nature, 616, 1\u20135. https:\/\/doi.org\/10.1038\/s41586-023-05773-7","journal-title":"Nature"},{"key":"2557_CR26","doi-asserted-by":"publisher","first-page":"117398","DOI":"10.1016\/j.eswa.2022.117398","volume":"202","author":"Z Kang","year":"2022","unstructured":"Kang, Z., Catal, C., & Tekinerdogan, B. (2022). Product failure detection for production lines using a data-driven model. Expert Systems with Applications, 202, 117398. https:\/\/doi.org\/10.1016\/j.eswa.2022.117398","journal-title":"Expert Systems with Applications"},{"key":"2557_CR27","doi-asserted-by":"publisher","unstructured":"Karras, T., Laine, S., & Aila, T. (2019). A style-based generator architecture for generative adversarial networks. In\u00a0Proceedings of the 2019 IEEE conference on computer vision and pattern recognition (pp. 4401\u20134410). https:\/\/doi.org\/10.1109\/cvpr.2019.00453","DOI":"10.1109\/cvpr.2019.00453"},{"key":"2557_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.105964","volume":"120","author":"J Kim","year":"2023","unstructured":"Kim, J., Kang, H., & Kang, P. (2023). Time-series anomaly detection with stacked transformer representations and 1D convolutional network. Engineering Applications of Artificial Intelligence, 120, 105964. https:\/\/doi.org\/10.1016\/j.engappai.2023.105964","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"2","key":"2557_CR29","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1109\/tsm.2022.3161512","volume":"35","author":"SH Kim","year":"2022","unstructured":"Kim, S. H., Kim, C. Y., Seol, D. H., Choi, J. E., & Hong, S. J. (2022). Machine learning-based process-level fault detection and part-level fault classification in semiconductor etch equipment. IEEE Transactions on Semiconductor Manufacturing, 35(2), 174\u2013185. https:\/\/doi.org\/10.1109\/tsm.2022.3161512","journal-title":"IEEE Transactions on Semiconductor Manufacturing"},{"key":"2557_CR30","doi-asserted-by":"publisher","unstructured":"Kiranyaz, S., Avci, O., Abdeljaber, O., Ince, T., Gabbouj, M., & Inman, D. J. (2019). 1D convolutional neural networks and applications: A survey. arXiv preprint. arXiv:1905.03554. https:\/\/doi.org\/10.1016\/j.ymssp.2020.107398","DOI":"10.1016\/j.ymssp.2020.107398"},{"key":"2557_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116094","volume":"189","author":"JU Ko","year":"2022","unstructured":"Ko, J. U., Na, K., Oh, J. S., Kim, J., & Youn, B. D. (2022). A new auto-encoder-based dynamic threshold to reduce false alarm rate for anomaly detection of steam turbines. Expert Systems with Applications, 189, 116094. https:\/\/doi.org\/10.1016\/j.eswa.2021.116094","journal-title":"Expert Systems with Applications"},{"issue":"7","key":"2557_CR32","doi-asserted-by":"publisher","first-page":"2011","DOI":"10.1080\/00207543.2014.946159","volume":"53","author":"N Kurniati","year":"2015","unstructured":"Kurniati, N., Yeh, R. H., & Wu, C. W. (2015). Designing a variables two-plan sampling system of type TNTVSS-(nT, nN; k) for controlling process fraction nonconforming with unilateral specification limit. International Journal of Production Research, 53(7), 2011\u20132025. https:\/\/doi.org\/10.1080\/00207543.2014.946159","journal-title":"International Journal of Production Research"},{"key":"2557_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115477","volume":"184","author":"S Lee","year":"2021","unstructured":"Lee, S., Chang, K., & Baek, J. G. (2021). Incremental learning using generative-rehearsal strategy for fault detection and classification. Expert Systems with Applications, 184, 115477. https:\/\/doi.org\/10.1016\/j.eswa.2021.115477","journal-title":"Expert Systems with Applications"},{"key":"2557_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2021.108139","volume":"163","author":"S Liu","year":"2022","unstructured":"Liu, S., Jiang, H., Wu, Z., & Li, X. (2022). Data synthesis using deep feature enhanced generative adversarial networks for rolling bearing imbalanced fault diagnosis. Mechanical Systems and Signal Processing, 163, 108139. https:\/\/doi.org\/10.1016\/j.ymssp.2021.108139","journal-title":"Mechanical Systems and Signal Processing"},{"key":"2557_CR35","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1016\/j.procs.2015.07.438","volume":"57","author":"RP Mahapatra","year":"2015","unstructured":"Mahapatra, R. P., & Chakraborty, P. S. (2015). Comparative analysis of nearest neighbor query processing techniques. Procedia Computer Science, 57, 1289\u20131298. https:\/\/doi.org\/10.1016\/j.procs.2015.07.438","journal-title":"Procedia Computer Science"},{"key":"2557_CR36","doi-asserted-by":"publisher","unstructured":"Mao, X., Li, Q., Xie, H., Lau, R. Y., Wang, Z., & Paul Smolley, S. (2017). Least squares generative adversarial networks. In Proceedings of the IEEE international conference on computer vision (pp. 2794\u20132802). https:\/\/doi.org\/10.1109\/iccv.2017.304","DOI":"10.1109\/iccv.2017.304"},{"issue":"2","key":"2557_CR37","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/s41060-019-00186-0","volume":"8","author":"S Maya","year":"2019","unstructured":"Maya, S., Ueno, K., & Nishikawa, T. (2019). dLSTM: A new approach for anomaly detection using deep learning with delayed prediction. International Journal of Data Science and Analytics, 8(2), 137\u2013164. https:\/\/doi.org\/10.1007\/s41060-019-00186-0","journal-title":"International Journal of Data Science and Analytics"},{"key":"2557_CR38","doi-asserted-by":"publisher","unstructured":"McKinney, W. (2010). Data structures for statistical computing in python. In Proceedings of the 9th Python in science conference (Vol. 445, pp. 51\u201356). https:\/\/doi.org\/10.25080\/majora-92bf1922-00a","DOI":"10.25080\/majora-92bf1922-00a"},{"key":"2557_CR39","doi-asserted-by":"publisher","first-page":"101280","DOI":"10.1016\/j.iot.2024.101280","volume":"27","author":"T Nguyen-Da","year":"2024","unstructured":"Nguyen-Da, T., Nguyen-Thanh, P., & Cho, M. Y. (2024). Real-time AIoT anomaly detection for industrial diesel generator based an efficient deep learning CNN-LSTM in industry 4.0. Internet of Things, 27, 101280. https:\/\/doi.org\/10.1016\/j.iot.2024.101280","journal-title":"Internet of Things"},{"key":"2557_CR40","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., ... Chintala, S. (2019). Pytorch: An imperative style, high-performance deep learning library. In Advances in neural information processing systems (Vol. 32)."},{"key":"2557_CR41","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. The Journal of Machine Learning Research, 12, 2825\u20132830.","journal-title":"The Journal of Machine Learning Research"},{"key":"2557_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106542","volume":"124","author":"Z Ren","year":"2023","unstructured":"Ren, Z., Gao, D., Zhu, Y., Ni, Q., Yan, K., & Hong, J. (2023). Generative adversarial networks driven by multi-domain information for improving the quality of generated samples in fault diagnosis. Engineering Applications of Artificial Intelligence, 124, 106542. https:\/\/doi.org\/10.1016\/j.engappai.2023.106542","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"2557_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105540","volume":"117","author":"C Shen","year":"2023","unstructured":"Shen, C., Zhang, H., Meng, S., & Li, C. (2023). Augmented data driven self-attention deep learning method for imbalanced fault diagnosis of the HVAC chiller. Engineering Applications of Artificial Intelligence, 117, 105540. https:\/\/doi.org\/10.1016\/j.engappai.2022.105540","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"2","key":"2557_CR44","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1109\/tsm.2022.3146266","volume":"35","author":"PC Shen","year":"2022","unstructured":"Shen, P. C., & Lee, C. Y. (2022). Wafer bin map recognition with autoencoder-based data augmentation in semiconductor assembly process. IEEE Transactions on Semiconductor Manufacturing, 35(2), 198\u2013209. https:\/\/doi.org\/10.1109\/tsm.2022.3146266","journal-title":"IEEE Transactions on Semiconductor Manufacturing"},{"key":"2557_CR45","doi-asserted-by":"publisher","unstructured":"Smith, K. E., & Smith, A. O. (2020). Conditional GAN for timeseries generation. arXiv preprint. arXiv:2006.16477. https:\/\/doi.org\/10.48550\/arXiv.2006.16477","DOI":"10.48550\/arXiv.2006.16477"},{"key":"2557_CR46","doi-asserted-by":"publisher","unstructured":"Sohn, Y., Ryu, S., Ahn, J., & Kim, S. (2022). Diversifying the role of MI in semiconductor manufacturing through new technologies and innovations. In Metrology, inspection, and process control XXXVI (Vol. 12053, pp. 308\u2013313). https:\/\/doi.org\/10.1117\/12.2612710","DOI":"10.1117\/12.2612710"},{"issue":"8","key":"2557_CR47","doi-asserted-by":"publisher","first-page":"2441","DOI":"10.1007\/s10845-021-01806-y","volume":"33","author":"S Sun","year":"2022","unstructured":"Sun, S., Hu, X., & Liu, Y. (2022). An imbalanced data learning method for tool breakage detection based on generative adversarial networks. Journal of Intelligent Manufacturing, 33(8), 2441\u20132455. https:\/\/doi.org\/10.1007\/s10845-021-01806-y","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"3","key":"2557_CR48","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1109\/tsm.2019.2925361","volume":"32","author":"J Wang","year":"2019","unstructured":"Wang, J., Yang, Z., Zhang, J., Zhang, Q., & Chien, W. T. K. (2019). AdaBalGAN: An improved generative adversarial network with imbalanced learning for wafer defective pattern recognition. IEEE Transactions on Semiconductor Manufacturing, 32(3), 310\u2013319. https:\/\/doi.org\/10.1109\/tsm.2019.2925361","journal-title":"IEEE Transactions on Semiconductor Manufacturing"},{"key":"2557_CR49","doi-asserted-by":"publisher","unstructured":"Wei, X., Gong, B., Liu, Z., Lu, W., & Wang, L. (2018). Improving the improved training of Wazsserstein GANS: A consistency term and its dual effect. arXiv preprint. arXiv:1803.01541. https:\/\/doi.org\/10.48550\/arXiv.1803.01541","DOI":"10.48550\/arXiv.1803.01541"},{"key":"2557_CR50","doi-asserted-by":"publisher","unstructured":"Wen, T., & Keyes, R. (2019). Time series anomaly detection using convolutional neural networks and transfer learning. arXiv preprint. arXiv:1905.13628. https:\/\/doi.org\/10.48550\/arXiv.1905.13628","DOI":"10.48550\/arXiv.1905.13628"},{"key":"2557_CR51","doi-asserted-by":"publisher","unstructured":"Weng, L. (2019). From GAN to WGAN. arXiv preprint. arXiv:1904.08994. https:\/\/doi.org\/10.48550\/arXiv.1904.08994","DOI":"10.48550\/arXiv.1904.08994"},{"issue":"3\u20134","key":"2557_CR52","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1002\/(sici)1099-128x(199905\/08)13:3\/4<379::aid-cem556>3.0.co;2-n","volume":"13","author":"BM Wise","year":"1999","unstructured":"Wise, B. M., Gallagher, N. B., Butler, S. W., White, D. D., Jr., & Barna, G. G. (1999). A comparison of principal component analysis, multiway principal component analysis, trilinear decomposition and parallel factor analysis for fault detection in a semiconductor etch process. Journal of Chemometrics: A Journal of the Chemometrics Society, 13(3\u20134), 379\u2013396. https:\/\/doi.org\/10.1002\/(sici)1099-128x(199905\/08)13:3\/4%3c379::aid-cem556%3e3.0.co;2-n","journal-title":"Journal of Chemometrics: A Journal of the Chemometrics Society"},{"key":"2557_CR53","doi-asserted-by":"publisher","unstructured":"Xu, J., Wu, H., Wang J., & Long, M. (2021). Anomaly transformer: Time series anomaly detection with association discrepancy. arXiv preprint. arXiv:2110.02642. https:\/\/doi.org\/10.48550\/arXiv.2110.02642","DOI":"10.48550\/arXiv.2110.02642"},{"key":"2557_CR54","unstructured":"Yoon, J., Jarrett, D., & Van der Schaar, M. (2019). Time-series generative adversarial networks. In Advances in neural information processing systems (Vol. 32)."},{"issue":"11","key":"2557_CR55","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/ac0a0c","volume":"32","author":"Y Yu","year":"2021","unstructured":"Yu, Y., Zhao, J., Tang, T., Wang, J., Chen, M., Wu, J., & Wang, L. (2021). Wasserstein distance-based asymmetric adversarial domain adaptation in intelligent bearing fault diagnosis. Measurement Science and Technology, 32(11), 115019. https:\/\/doi.org\/10.1088\/1361-6501\/ac0a0c","journal-title":"Measurement Science and Technology"},{"key":"2557_CR56","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105735","volume":"119","author":"L Zhang","year":"2023","unstructured":"Zhang, L., Fan, Q., Lin, J., Zhang, Z., Yan, X., & Li, C. (2023). A nearly end-to-end deep learning approach to fault diagnosis of wind turbine gearboxes under nonstationary conditions. Engineering Applications of Artificial Intelligence, 119, 105735. https:\/\/doi.org\/10.1016\/j.engappai.2022.105735","journal-title":"Engineering Applications of Artificial Intelligence"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-024-02557-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-024-02557-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-024-02557-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T09:35:16Z","timestamp":1769852116000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-024-02557-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,10]]},"references-count":56,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["2557"],"URL":"https:\/\/doi.org\/10.1007\/s10845-024-02557-2","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,10]]},"assertion":[{"value":"2 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}