Abadi, M., Ashish, A., Paul, B., Eugene, B., Zhifeng, C., Craig, C. & **aoqiang, Z. (2015). TensorFlow: Large-scale machine learning on heterogeneous systems.
Abati, D., Porrello, A., Calderara, S. & Cucchiara, R. (2019). Latent space autoregression for novelty detection. In Conference on computer vision and pattern recognition (pp. 481–490). https://doi.org/10.1109/CVPR.2019.00057
Abdulaal, A., Liu, Z. & Lancewicki, T. (2021). Practical approach to asynchronous multivariate time series anomaly detection and localization. In KDD (pp. 2485–2494). https://doi.org/10.1145/3447548.3467174
Aggarwal, C. C. (2017). Outlier analysis. Springer, New York.https://doi.org/10.1007/978-3-319-47578-3
Article
Google Scholar
Ahmad, S., Enshaei, N., Naderkhani, F. & Awasthi, A. (2020). Integrated deep learning and statistical process control for online monitoring of manufacturing processes. In International conference on prognostics and health management (pp. 1–6). https://doi.org/10.1109/ICPHM49022.2020.9187046
Ai, M., **e, Y., Ding, S. X., Tang, Z., & Gui, W. (2023). Domain knowledge distillation and supervised contrastive learning for industrial process monitoring. IEEE Transactions on Industrial Electronics, 70(9), 9452–9462. https://doi.org/10.1109/TIE.2022.3206696
Article
Google Scholar
Alauddin, M., Khan, F., Imtiaz, S., & Ahmed, S. (2018). A bibliometric review and analysis of data-driven fault detection and diagnosis methods for process systems. Industrial & Engineering Chemistry Research, 57(32), 10719–10735. https://doi.org/10.1021/acs.iecr.8b00936
Article
Google Scholar
Bergman, L., & Hoshen, Y. (2020). Classification-based anomaly detection for general data. In International conference on learning representations. ar**v:2005.02359
Bergmann, P., Fauser, M., Sattlegger, D., & Steger, C. (2019). MVTEC ad—A comprehensive real-world dataset for unsupervised anomaly detection. Conference on computer vision and pattern recognition (pp. 9592–9600). https://doi.org/10.1007/s11263-020-01400-4
Biegel, T., Jourdan, N., Hernandez, C., Cviko, A., & Metternich, J. (2022). Deep learning for multivariate statistical in-process control in discrete manufacturing: A case study in a sheet-metal forming process. Procedia CIRP, 107, 422–427. https://doi.org/10.1016/j.procir.2022.05.002
Article
Google Scholar
Biegel, T., Jourdan, N., Madreiter, T., Kohl, L., Fahle, S., Ansari, F., & Metternich, J. (2022). Combining process monitoring with text mining for anomaly detection in discrete manufacturing. SSRN. https://doi.org/10.2139/ssrn.4073942
Article
Google Scholar
Bradley, A. P. (1997). The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognition, 30, 1145–1159. https://doi.org/10.1016/S0031-3203(96)00142-2
Article
Google Scholar
Campos, G. O., Zimek, A., Sander, J., Campello, R. J. G. B., Micenková, B., Schubert, E., & Houle, M. E. (2016). On the evaluation of unsupervised outlier detection: Measures, datasets, and an empirical study. Data Mining and Knowledge Discovery, 30(4), 891–927. https://doi.org/10.1007/s10618-015-0444-8
Article
Google Scholar
Carrara, F., Amato, G., Brombin, L., Falchi, F. & Gennaro, C. (2020). Combining gans and autoencoders for efficient anomaly detection. In International conference on pattern recognition (pp. 3939–3946). ar**v:2011.08102
Chalapathy, R., & Chawla, S. (2019). Deep learning for anomaly detection: A survey. ar**v preprint: ar**v:1901.03407.
Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 1–58. https://doi.org/10.1145/1541880.1541882
Article
Google Scholar
Chen, T., Kornblith, S., Norouzi, M. & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. In International conference on machine learning (pp. 1597–1607). ar**v:2002.05709
Cheng, F., He, Q. P., & Zhao, J. (2019). A novel process monitoring approach based on variational recurrent autoencoder. Computers & Chemical Engineering, 129, 106515. https://doi.org/10.1016/j.compchemeng.2019.106515
Article
Google Scholar
Cheng, J., & Vasconcelos, N. (2021). Learning deep classifiers consistent with fine-grained novelty detection. In Conference on computer vision and pattern recognition (pp. 1664–1673).
Dai, E., & Chen, J. (2022). Graph-augmented normalizing flows for anomaly detection of multiple time series. In International conference on learning representations. ar**v:2202.07857
Dehaene, D., Frigo, O., Combrexelle, S. & Eline, P. (2020). Iterative energy-based projection on a normal data manifold for anomaly localization. In International conference on learning representations. ar**v:2002.03734
Ding, X., Li, Y., Belatreche, A., & Maguire, L. P. (2014). An experimental evaluation of novelty detection methods. Neurocomputing, 135, 313–327. https://doi.org/10.1016/j.neucom.2013.12.002
Article
Google Scholar
Doersch, C., Gupta, A., & Efros, A. A. (2015). Unsupervised visual representation learning by context prediction. International conference on computer vision (pp. 1422–1430). https://doi.org/10.1109/ICCV.2015.167
Ermolov, A., Siarohin, A., Sangineto, E. & Sebe, N. (2021). Whitening for self-supervised representation learning. In International conference on machine learning (pp. 3015–3024). ar**v:2007.06346
Fawcett, T. (2006). An introduction to roc analysis. Pattern Recognition Letters, 27(8), 861–874. https://doi.org/10.1016/j.patrec.2005.10.010
Article
Google Scholar
Ferrer, A. (2007). Multivariate statistical process control based on principal component analysis (MSPC-PCA): Some reflections and a case study in an autobody assembly process. Quality Engineering, 19(4), 311–325. https://doi.org/10.1080/08982110701621304
Article
Google Scholar
Ferrer, A. (2014). Latent structures-based multivariate statistical process control: A paradigm shift. Quality Engineering, 26(1), 72–91. https://doi.org/10.1080/08982112.2013.846093
Article
Google Scholar
Fu, Y., & Xue, F. (2022). Mad: Self-supervised masked anomaly detection task for multivariate time series. International Joint Conference on Neural Networks. https://doi.org/10.1109/IJCNN55064.2022.9892218
Article
Google Scholar
Gao, R. X., & Yan, R. (2011). Continuous wavelet transform. In Wavelets: Theory and applications for manufacturing. Springer. https://doi.org/10.1007/978-1-4419-1545-0_3
Book
Google Scholar
Ge, Z., Song, Z., & Gao, F. (2013). Review of recent research on data-based process monitoring. Industrial & Engineering Chemistry Research, 52(10), 3543–3562. https://doi.org/10.1021/ie302069q
Article
Google Scholar
Gidaris, S., Singh, P. & Komodakis, N. (2018). Unsupervised representation learning by predicting image rotations. In International conference on learning representations. ar**v:1803.07728
Golan, I., & El-Yaniv, R. (2018). Deep anomaly detection using geometric transformations. Conference on neural information processing systems. ar**v:1805.10917
Goyal, S., Raghunathan, A., Jain, M., Simhadri, H.V. & Jain, P. (2020). Drocc: Deep robust one-class classification. In International conference on machine learning. ar**v:2002.12718
Grasso, M., Colosimo, B. M., Semeraro, Q., & Pacella, M. (2015). A comparison study of distribution-free multivariate SPC methods for multimode data. Quality and Reliability Engineering International, 31(1), 75–96. https://doi.org/10.1002/qre.1708
Article
Google Scholar
Grossmann, A., & Morlet, J. (1984). Decomposition of hardy functions into square integrable wavelets of constant shape. SIAM Journal on Mathematical Analysis, 15(4), 723–736. https://doi.org/10.1137/0515056
Article
Google Scholar
Gu, X., Akoglu, L. & Rinaldo, A. (2019). Statistical analysis of nearest neighbor methods for anomaly detection. In Conference on neural information processing systems. ar**v:1907.03813
Gupta, M., Gao, J., Aggarwal, C. C., & Han, J. (2014). Outlier detection for temporal data: A survey. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/TKDE.2013.184
Article
Google Scholar
Hahn, T., & Mechefske, C. K. (2021). Self-supervised learning for tool wear monitoring with a disentangled-variational-autoencoder. International Journal of Hydromechatronics. https://doi.org/10.1504/IJHM.2021.10035377
Article
Google Scholar
He, K., Zhang, X., Ren, S. & Sun, J. (2015). Deep residual learning for image recognition. In Conference on computer vision and pattern recognition (pp. 770–778). https://doi.org/10.1109/CVPR.2016.90
Hendrycks, D., Mazeika, M. & Dietterich, T. (2019). Deep anomaly detection with outlier exposure. In International conference on learning representations. ar**v:1812.04606
Hendrycks, D., Mazeika, M., Kadavath, S. & Song, D. (2019). Using self-supervised learning can improve model robustness and uncertainty. In Conference on neural information processing systems. ar**v:1906.12340
Hotelling, H. (1947). Multivariate quality control, illustrated by the air testing of sample bombsights. Techniques of statistical analysis (pp. 111–184).
Hu, W., Wang, M., Qin, Q., Ma, J. & Liu, B. (2020). Hrn: A holistic approach to one class learning. In Conference on neural information processing systems (pp. 19111–19124).
Hübner, H. B., Duarte, M. A. V., & Da Silva, R. B. (2020). Automatic grinding burn recognition based on time-frequency analysis and convolutional neural networks. The International Journal of Advanced Manufacturing Technology, 110(7–8), 1833–1849. https://doi.org/10.1007/s00170-020-05902-w
Article
Google Scholar
Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. International Conference on Machine Learning,37,448-456. ar**v:1502.03167
Jackson, J. E. (1991). A user’s guide to principal components. New YorkJohn Wiley. https://doi.org/10.1002/0471725331
Article
Google Scholar
**g, L., & Tian, Y. (2020). Self-supervised visual feature learning with deep neural networks: A survey. IEEE transactions on pattern analysis and machine intelligence,4037-4058. ar**v:1902.06162
Kingma, D.P., & Ba, J. (2015). Adam: A method for stochastic optimization. In International conference on learning representations. ar**v:1412.6980
Kong, D., & Yan, X. (2020). Industrial process deep feature representation by regularization strategy autoencoders for process monitoring. Measurement Science and Technology, 31(2), 025104. https://doi.org/10.1088/1361-6501/ab48c7
Article
Google Scholar
Kourti, T., & MacGregor, J. F. (1996). Multivariate SPC methods for process and product monitoring. Journal of Quality Technology, 28(4), 409–428. https://doi.org/10.1080/00224065.1996.11979699
Article
Google Scholar
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Conference on neural information processing systems (Vol. 25, pp. 1097–1105).
Kumagai, A., Iwata, T. & Fujiwara, Y. (2019). Transfer anomaly detection by inferring latent domain representations. In Conference on neural information processing systems.
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324. https://doi.org/10.1109/5.726791
Article
Google Scholar
Li, C.-L., Sohn, K., Yoon, J. & Pfister, T. (2021). Cutpaste: Self-supervised learning for anomaly detection and localization. In Conference on computer vision and pattern recognition (pp. 9664–9674).
Li, D., Lu, J., Zhang, T., & Ding, J. (2023). Self-supervised learning and multisource heterogeneous information fusion based quality anomaly detection for heavy-plate shape. In IEEE transactions on automation science and engineering. https://doi.org/10.1109/TASE.2023.3265649
Article
Google Scholar
Li, S., Luo, J., & Hu, Y. (2022). Toward interpretable process monitoring: Slow feature analysis-aided autoencoder for spatiotemporal process feature learning. IEEE Transactions on Instrumentation and Measurement, 71, 1–11. https://doi.org/10.1109/TIM.2021.3127284
Article
Google Scholar
Li, W., Zhang, C., Tsung, F., & Mei, Y. (2020). Nonparametric monitoring of multivariate data via KNN learning. International Journal of Production Research. https://doi.org/10.1080/00207543.2020.1812750
Article
Google Scholar
Li, Z., Zhao, Y., Han, J., Su, Y., Jiao, R., Wen, X. & Pei, D. (2021). Multivariate time series anomaly detection and interpretation using hierarchical inter-metric and temporal embedding. In KDD (pp. 3220–3230). https://doi.org/10.1145/3447548.3467075
Liao, Y., Ragai, I., Huang, Z., & Kerner, S. (2021). Manufacturing process monitoring using time-frequency representation and transfer learning of deep neural networks. Journal of Manufacturing Processes, 68, 231–248. https://doi.org/10.1016/j.jmapro.2021.05.046
Article
Google Scholar
Lindemann, B., Fesenmayr, F., Jazdi, N., & Weyrich, M. (2019). Anomaly detection in discrete manufacturing using self-learning approaches. Procedia CIRP, 79, 313–318. https://doi.org/10.1016/j.procir.2019.02.073
Article
Google Scholar
Lindemann, B., Jazdi, N., & Weyrich, M. (2020). Anomaly detection and prediction in discrete manufacturing based on cooperative LSTM networks. In IEEE international conference on automation science and engineering (pp. 1003–1010). https://doi.org/10.1109/CASE48305.2020.9216855
Liu, C., Wang, K., Wang, Y., & Yuan, X. (2022). Learning deep multimanifold structure feature representation for quality prediction with an industrial application. IEEE Transactions on Industrial Informatics, 18(9), 5849–5858. https://doi.org/10.1109/TII.2021.3130411
Article
Google Scholar
Liu, F.T., Ting, K.M. & Zhou, Z,-H. (2008). Isolation forest. In IEEE international conference on data mining (pp. 413–422). https://doi.org/10.1109/ICDM.2008.17
Liznerski, P., Ruff, L., Vandermeulen, R.A., Franks, B.J., Kloft, M. & Müller, K.-R. (2021). Explainable deep one-class classification. In International conference on learning representations. ar**v:2007.01760
Lorenti, L., de Rossi, G., Annoni, A., Rigutto, S., & Susto, G. A. (2022). Cuad-mo: Continuos unsupervised anomaly detection on machining operations. In IEEE conference on control technology and applications. https://doi.org/10.1109/CCTA49430.2022.9966138
Article
Google Scholar
Loshchilov, I., & Hutter, F. (2017). SGDR: Stochastic gradient descent with warm restarts. In International conference on learning representations. https://doi.org/10.48550/ARXIV.1608.03983
Lu, S., Dong, H., & Yu, H. (2023). Abnormal condition detection method of industrial processes based on cascaded bagging-PCA and CNN classification network. In IEEE transactions on industrial informatics. https://doi.org/10.1109/TII.2023.3242811
Article
Google Scholar
MacGregor, J. F. (1997). Using on-line process data to improve quality: Challenges for statisticians. International Statistical Review, 65(3), 309–323. https://doi.org/10.1111/j.1751-5823.1997.tb00311.x
Article
Google Scholar
Montgomery, D.C. (2009). Introduction to Statistical Quality Control Introduction to statistical quality control (6th ed.). Wiley.
Noroozi, M., & Favaro, P. (2016). Unsupervised learning of visual representations by solving jigsaw puzzles. European conference on computer vision (pp. 69–84). ar**v:1603.09246
Noroozi, M., Vinjimoor, A., Favaro, P. & Pirsiavash, H. (2018). Boosting self-supervised learning via knowledge transfer. In Conference on computer vision and pattern recognition (pp. 9359–9367). ar**v:1805.00385
Oshida, T., Murakoshi, T., Zhou, L., Ojima, H., Kaneko, K., Onuki, T., & Shimizu, J. (2023). Development and implementation of real-time anomaly detection on tool wear based on stacked LSTM encoder-decoder model. International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-023-11497-9
Article
Google Scholar
Pang, G., Shen, C., Cao, L., & van den Hengel, A. (2021). Deep learning for anomaly detection: A review. ACM Computing Surveys, 54(2), 1–38. https://doi.org/10.1145/3439950
Article
Google Scholar
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830. ar**v:1201.0490.
Google Scholar
Proteau, A., Zemouri, R., Tahan, A., & Thomas, M. (2020). Dimension reduction and 2d-visualization for early change of state detection in a machining process with a variational autoencoder approach. The International Journal of Advanced Manufacturing Technology, 111(11–12), 3597–3611. https://doi.org/10.1007/s00170-020-06338-y
Article
Google Scholar
Qin, S. J. (2003). Statistical process monitoring: Basics and beyond. Journal of Chemometrics, 17(8–9), 480–502. https://doi.org/10.1002/cem.800
Article
Google Scholar
Qin, S. J. (2012). Survey on data-driven industrial process monitoring and diagnosis. Annual Reviews in Control, 36(2), 220–234. https://doi.org/10.1016/j.arcontrol.2012.09.004
Article
Google Scholar
Qin, S. J., & Chiang, L. H. (2019). Advances and opportunities in machine learning for process data analytics. Computers & Chemical Engineering, 126, 465–473. https://doi.org/10.1016/j.compchemeng.2019.04.003
Article
Google Scholar
Qiu, C., Pfrommer, T., Kloft, M., Mandt, S. & Rudolph, M. (2021). Neural transformation learning for deep anomaly detection beyond images. In International conference on machine learning (pp. 8703–8714). ar**v:2103.16440
Qiu, P., & **e, X. (2021). Transparent sequential learning for statistical process control of serially correlated data. Technometrics. https://doi.org/10.1080/00401706.2021.1929493
Article
Google Scholar
Roth, K., Pemula, L., Zepeda, J., Schölkopf, B., Brox, T. & Gehler, P. (2022). Towards total recall in industrial anomaly detection. In Conference on computer vision and pattern recognition. ar**v:2106.08265
Ruff, L., Kauffmann, J. R., Vandermeulen, R. A., Montavon, G., Samek, W., Kloft, M., & Müller, K.-R. (2021). A unifying review of deep and shallow anomaly detection. Proceedings of the IEEE, 109(5), 756–795. https://doi.org/10.1109/JPROC.2021.3052449
Article
Google Scholar
Ruff, L., Vandermeulen, R.A., Görnitz, N., Binder, A., Müller, E., Müller, K.-R. & Kloft, M. (2020). Deep semi-supervised anomaly detection. In International conference on learning representations. ar**v:1906.02694
Ruff, L., Vandermeulen, R.A., Görnitz, N., Deecke, L., Siddiqui, S.A., Binder, A. & Kloft, M. (2018). Deep one-class classification. In International conference on machine learning (pp. 4398–4402).
Schölkopf, B., Williamson, R.C., Smola, A.J., Shawe-Taylor, J. & Platt, J.C. (1999). Support vector method for novelty detection. In Conference on neural information processing systems (pp. 582–588).
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2020). Grad-cam: Visual explanations from deep networks via gradient-based localization. International Journal of Computer Vision, 128(2), 336–359. https://doi.org/10.1007/s11263-019-01228-7
Article
Google Scholar
Shen, L., Li, Z., & Kwok, J. T. (2020). Timeseries anomaly detection using temporal hierarchichal one-class network. Conference on Neural Information Processing Systems, 33, 13016–13026.
Google Scholar
Shen, L., Yu, Z., Ma, Q., & Kwok, J. T. (2021). Time series anomaly detection with multiresolution ensemble decoding. AAAI Conference on Artificial Intelligence, 35(11), 9567–9575. https://doi.org/10.1609/aaai.v35i11.17152
Article
Google Scholar
Shenkar, T., & Wolf, L. (2022). Anomaly detection for tabular data with internal contrastive learning. In International conference on learning representations.
Sohn, K., Li, C-.L., Yoon, J., **, M. & Pfister, T. (2021). Learning and evaluating representations for deep one-class classification. In International conference on learning representations.
Sun, S., Liu, Y., Hu, X., & Zhang, W. (2023). A semisupervised autoencoder-based method for anomaly detection in cutting tools. Journal of Manufacturing Processes, 93, 315–327. https://doi.org/10.1016/j.jmapro.2023.03.043
Article
Google Scholar
Tack, J., Mo, S., Jeong, J. & Shin, J. (2020). Csi: Novelty detection via contrastive learning on distributionally shifted instances. In Conference on neural information processing systems (Vol. 33, pp. 11839–11852). ar**v:2007.08176
Tang, P., Peng, K., Dong, J., Zhang, K., & Zhao, S. (2020). Monitoring of nonlinear processes with multiple operating modes through a novel Gaussian mixture variational autoencoder model. IEEE Access, 8, 114487–114500. https://doi.org/10.1109/ACCESS.2020.3003095
Article
Google Scholar
Tax, D. M., & Duin, R. P. (2004). Support vector data description. Machine Learning, 54(1), 45–66. https://doi.org/10.1023/B:MACH.0000008084.60811.49
Article
Google Scholar
Tnani, M.-A., Feil, M., & Diepold, K. (2022). Smart data collection system for brownfield CNC milling machines: A new benchmark dataset for data-driven machine monitoring. Procedia CIRP, 107, 131–136. https://doi.org/10.1016/j.procir.2022.04.022
Article
Google Scholar
Tran, M.-Q., Liu, M.-K., & Tran, Q.-V. (2020). Milling chatter detection using scalogram and deep convolutional neural network. The International Journal of Advanced Manufacturing Technology, 107(3–4), 1505–1516. https://doi.org/10.1007/s00170-019-04807-7
Article
Google Scholar
Tran, T., & Lundgren, J. (2020). Drill fault diagnosis based on the scalogram and mel spectrogram of sound signals using artificial intelligence. IEEE Access. https://doi.org/10.1109/ACCESS.2020.3036769
Article
Google Scholar
Wang, R., Liu, C., Mou, X., Gao, K., Guo, X., Liu, P. & Liu, X. (2023). Deep contrastive one-class time series anomaly detection. In SIAM international conference on data mining (pp. 694–702). ar**v:2207.01472
Wang, Y., Si, Y., Huang, B., & Lou, Z. (2018). Survey on the theoretical research and engineering applications of multivariate statistics process monitoring algorithms: 2008–2017. Canadian Journal of Chemical Engineering, 96(10), 2073–2085. https://doi.org/10.1002/cjce.23249
Article
Google Scholar
Woodall, W. H. (2000). Controversies and contradictions in statistical process control. Journal of Quality Technology, 32(4), 341–350. https://doi.org/10.1080/00224065.2000.11980013
Article
Google Scholar
Woodall, W. H. (2017). Bridging the gap between theory and practice in basic statistical process monitoring. Quality Engineering, 29(1), 2–15. https://doi.org/10.1080/08982112.2016.1210449
Article
Google Scholar
Woodall, W. H., & Montgomery, D. C. (1999). Research issues and ideas in statistical process control. Journal of Quality Technology, 31(4), 376–386. https://doi.org/10.1080/00224065.1999.11979944
Article
Google Scholar
Woodall, W. H., Spitzner, D. J., Montgomery, D. C., & Gupta, S. (2004). Using control charts to monitor process and product quality profiles. Journal of Quality Technology, 36(3), 309–320. https://doi.org/10.1080/00224065.2004.11980276
Article
Google Scholar
Wu, J-.C., Chen, D-.J., Fuh, C-.S. & Liu, T-.L. (2021). Learning unsupervised metaformer for anomaly detection. In International conference on computer vision (pp. 4369–4378).
Wu, Z., Li, Y., Tsung, F., & Pan, E. (2021). Real-time monitoring and diagnosis scheme for IOT-enabled devices using multivariate SPC techniques. IISE Transactions, 55(4), 348–362. https://doi.org/10.1080/24725854.2021.2000681
Article
Google Scholar
**e, X., & Peihua, Q. (2022). Machine learning control charts for monitoring serially correlated data. In Control charts and machine learning for anomaly detection in manufacturing. Springer.
Ye, Z., Chen, Y. & Zheng, H. (2021). Understanding the effect of bias in deep anomaly detection. International joint conference on artificial intelligence (Vol. 3, pp. 3314–3320). https://doi.org/10.24963/ijcai.2021/456
Yin, S., Ding, S. X., **e, X., & Luo, H. (2014). A review on basic data-driven approaches for industrial process monitoring. IEEE Transactions on Industrial Electronics, 61(11), 6418–6428. https://doi.org/10.1109/TIE.2014.2301773
Article
Google Scholar
Yu, J., Liu, X., & Ye, L. (2021). Convolutional long short-term memory autoencoder-based feature learning for fault detection in industrial processes. IEEE Transactions on Instrumentation and Measurement, 70, 1–15. https://doi.org/10.1109/TIM.2020.3039614
Article
Google Scholar
Yu, J., & Zhang, C. (2020). Manifold regularized stacked autoencoders-based feature learning for fault detection in industrial processes. Journal of Process Control, 92, 119–136. https://doi.org/10.1016/j.jprocont.2020.06.001
Yu, J., & Zhang, Y. (2023). Challenges and opportunities of deep learning-based process fault detection and diagnosis: A review. Neural Computing and Applications, 35(1), 211–252. https://doi.org/10.1007/s00521-022-08017-3
Article
Google Scholar
Zhang, C., Song, D., Chen, Y., Feng, X., Lumezanu, C., Cheng, W. & Chawla, N.V. (2019). A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data. In AAAI conference on artifical intelligence (pp. 1409–1416).
Zhang, C., Tsung, F., & Zou, C. (2015). A general framework for monitoring complex processes with both in-control and out-of-control information. Computers & Industrial Engineering, 85, 157–168. https://doi.org/10.1016/j.cie.2015.03.007
Article
Google Scholar
Zhang, C., Yu, J., & Wang, S. (2021). Fault detection and recognition of multivariate process based on feature learning of one-dimensional convolutional neural network and stacked denoised autoencoder. International Journal of Production Research, 59(8), 2426–2449. https://doi.org/10.1080/00207543.2020.1733701
Article
Google Scholar
Zong, B., Song, Q., Renqiang Min, M., Cheng, W., Lumezanu, C., Cho, D. & Chen, H. (2018). Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In International conference on learning representations.