{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T16:27:37Z","timestamp":1784996857482,"version":"3.55.0"},"reference-count":66,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,4,2]],"date-time":"2022-04-02T00:00:00Z","timestamp":1648857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>As a substantial amount of multivariate time series data is being produced by the complex systems in smart manufacturing (SM), improved anomaly detection frameworks are needed to reduce the operational risks and the monitoring burden placed on the system operators. However, building such frameworks is challenging, as a sufficiently large amount of defective training data is often not available and frameworks are required to capture both the temporal and contextual dependencies across different time steps while being robust to noise. In this paper, we propose an unsupervised Attention-Based Convolutional Long Short-Term Memory (ConvLSTM) Autoencoder with Dynamic Thresholding (ACLAE-DT) framework for anomaly detection and diagnosis in multivariate time series. The framework starts by pre-processing and enriching the data, before constructing feature images to characterize the system statuses across different time steps by capturing the inter-correlations between pairs of time series. Afterwards, the constructed feature images are fed into an attention-based ConvLSTM autoencoder, which aims to encode the constructed feature images and capture the temporal behavior, followed by decoding the compressed knowledge representation to reconstruct the feature images\u2019 input. The reconstruction errors are then computed and subjected to a statistical-based, dynamic thresholding mechanism to detect and diagnose the anomalies. Evaluation results conducted on real-life manufacturing data demonstrate the performance strengths of the proposed approach over state-of-the-art methods under different experimental settings.<\/jats:p>","DOI":"10.3390\/make4020015","type":"journal-article","created":{"date-parts":[[2022,4,3]],"date-time":"2022-04-03T02:59:52Z","timestamp":1648954792000},"page":"350-370","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":67,"title":["An Attention-Based ConvLSTM Autoencoder with Dynamic Thresholding for Unsupervised Anomaly Detection in Multivariate Time Series"],"prefix":"10.3390","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2254-2797","authenticated-orcid":false,"given":"Tareq","family":"Tayeh","sequence":"first","affiliation":[{"name":"ECE Department, Western University, London, ON N6A 3K7, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4082-6352","authenticated-orcid":false,"given":"Sulaiman","family":"Aburakhia","sequence":"additional","affiliation":[{"name":"ECE Department, Western University, London, ON N6A 3K7, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryan","family":"Myers","sequence":"additional","affiliation":[{"name":"National Research Council Canada, London, ON N6G 4X8, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2887-0350","authenticated-orcid":false,"given":"Abdallah","family":"Shami","sequence":"additional","affiliation":[{"name":"ECE Department, Western University, London, ON N6A 3K7, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1803","DOI":"10.1109\/TNSM.2020.3014929","article-title":"Multi-stage optimized machine learning framework for network intrusion detection","volume":"18","author":"Injadat","year":"2020","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Moubayed, A., Aqeeli, E., and Shami, A. (September, January 30). Ensemble-based feature selection and classification model for dns typo-squatting detection. Proceedings of the 2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), London, ON, Canada.","DOI":"10.1109\/CCECE47787.2020.9255697"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"56046","DOI":"10.1109\/ACCESS.2018.2872784","article-title":"Data mining techniques in intrusion detection systems: A systematic literature review","volume":"6","author":"Salo","year":"2018","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Injadat, M., Salo, F., Nassif, A.B., Essex, A., and Shami, A. (2018, January 9\u201313). Bayesian optimization with machine learning algorithms towards anomaly detection. Proceedings of the 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/GLOCOM.2018.8647714"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Moubayed, A., Injadat, M., Shami, A., and Lutfiyya, H. (2018, January 9\u201313). Dns typo-squatting domain detection: A data analytics & machine learning based approach. Proceedings of the 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/GLOCOM.2018.8647679"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/JIOT.2017.2756025","article-title":"Recursive principal component analysis-based data outlier detection and sensor data aggregation in IoT systems","volume":"4","author":"Yu","year":"2017","journal-title":"IEEE Internet Things J."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liu, J., Guo, J., Orlik, P., Shibata, M., Nakahara, D., Mii, S., and Tak\u00e1\u010d, M. (2018, January 4\u20138). Anomaly detection in manufacturing systems using structured neural networks. Proceedings of the 2018 13th World Congress on Intelligent Control and Automation (WCICA), Changsha, China.","DOI":"10.1109\/WCICA.2018.8630692"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Thakur, N., and Han, C.Y. (2021). An ambient intelligence-based human behavior monitoring framework for ubiquitous environments. Information, 12.","DOI":"10.3390\/info12020081"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1080\/00207543.2017.1351644","article-title":"Smart manufacturing","volume":"56","author":"Kusiak","year":"2018","journal-title":"Int. J. Prod. Res"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s40684-016-0015-5","article-title":"Smart manufacturing: Past research, present findings, and future directions","volume":"3","author":"Kang","year":"2016","journal-title":"Int. J. Precis. Eng. Manuf.-Green Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1108\/JBIM-06-2016-0129","article-title":"The combined effect of product returns experience and switching costs on B2B customer re-purchase intent","volume":"32","author":"Russo","year":"2017","journal-title":"J. Bus. Ind. Mark."},{"key":"ref_12","unstructured":"Progress (2022, March 08). Anomaly Detection & Prediction Decoded: 6 Industries, Copious Challenges, Extraordinary Impact. Available online: https:\/\/www.progress.com\/docs\/default-source\/datarpm\/progress_datarpm_cadp_ebook_anomaly_detection_in_6_industries.pdf?sfvrsn=82a183de_2."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yang, L., Moubayed, A., Hamieh, I., and Shami, A. (2019, January 9\u201313). Tree-based intelligent intrusion detection system in internet of vehicles. Proceedings of the 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA.","DOI":"10.1109\/GLOBECOM38437.2019.9013892"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kieu, T., Yang, B., and Jensen, C.S. (2018, January 25\u201328). Outlier detection for multidimensional time series using deep neural networks. Proceedings of the 2018 19th IEEE International Conference on Mobile Data Management (MDM), Aalborg, Denmark.","DOI":"10.1109\/MDM.2018.00029"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Aburakhia, S., Tayeh, T., Myers, R., and Shami, A. (2020, January 4\u20137). A Transfer Learning Framework for Anomaly Detection Using Model of Normality. Proceedings of the 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada.","DOI":"10.1109\/IEMCON51383.2020.9284916"},{"key":"ref_16","unstructured":"Guo, Y., Liao, W., Wang, Q., Yu, L., Ji, T., and Li, P. (2018, January 14\u201316). Multidimensional time series anomaly detection: A gru-based gaussian mixture variational autoencoder approach. Proceedings of the Asian Conference on Machine Learning, Beijing, China."},{"key":"ref_17","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.jmsy.2018.01.003","article-title":"Deep learning for smart manufacturing: Methods and applications","volume":"48","author":"Wang","year":"2018","journal-title":"J. Manuf. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6481","DOI":"10.1109\/JIOT.2019.2958185","article-title":"Anomaly detection for IoT time-series data: A survey","volume":"7","author":"Cook","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chalapathy, R., and Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv.","DOI":"10.1145\/3394486.3406704"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1613\/jair.3623","article-title":"Toward supervised anomaly detection","volume":"46","author":"Kloft","year":"2013","journal-title":"J. Artif. Intell. Res."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kawachi, Y., Koizumi, Y., and Harada, N. (2018, January 15\u201320). Complementary set variational autoencoder for supervised anomaly detection. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8462181"},{"key":"ref_23","unstructured":"Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., and Woo, W.C. (2015, January 11\u201312). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Proceedings of the Twenty-ninth Conference on Neural Information Processing Systems, Montr\u00e9al, QC, Canada."},{"key":"ref_24","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.neucom.2020.07.061","article-title":"On hyperparameter optimization of machine learning algorithms: Theory and practice","volume":"415","author":"Yang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"9231","DOI":"10.1109\/JSEN.2021.3053039","article-title":"Autocorrelation Integrated Gaussian Based Anomaly Detection using Sensory Data in Industrial Manufacturing","volume":"21","author":"Saci","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Huo, W., Wang, W., and Li, W. (2019, January 8\u201313). Anomalydetect: An online distance-based anomaly detection algorithm. Proceedings of the International Conference on Web Services, Milan, Italy.","DOI":"10.1007\/978-3-030-23499-7_5"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"106919","DOI":"10.1016\/j.asoc.2020.106919","article-title":"Clustering-based anomaly detection in multivariate time series data","volume":"100","author":"Li","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1109\/TSP.2017.2784390","article-title":"Online anomaly detection with minimax optimal density estimation in nonstationary environments","volume":"66","author":"Gokcesu","year":"2017","journal-title":"IEEE Trans. Signal Process"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1661","DOI":"10.1109\/TPDS.2012.261","article-title":"Scalable hypergrid k-NN-based online anomaly detection in wireless sensor networks","volume":"24","author":"Xie","year":"2012","journal-title":"Ieee Trans. Parallel Distrib. Syst."},{"key":"ref_31","unstructured":"Wang, Y., Wong, J., and Miner, A. (2004, January 10\u201311). Anomaly intrusion detection using one class SVM. Proceedings of the Proceedings from the Fifth Annual IEEE SMC Information Assurance Workshop, West Point, NY, USA."},{"key":"ref_32","first-page":"943","article-title":"Intrusion detection with neural networks","volume":"10","author":"Ryan","year":"1998","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/TNSRE.2019.2948798","article-title":"Anomaly detection of moderate traumatic brain injury using auto-regularized multi-instance one-class SVM","volume":"28","author":"Rasheed","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1038\/544023a","article-title":"Smart manufacturing must embrace big data","volume":"544","author":"Kusiak","year":"2017","journal-title":"Nature"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Tayeh, T., Aburakhia, S., Myers, R., and Shami, A. (2020, January 4\u20137). Distance-Based Anomaly Detection for Industrial Surfaces Using Triplet Networks. Proceedings of the 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada.","DOI":"10.1109\/IEMCON51383.2020.9284921"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Feng, C., Li, T., and Chana, D. (2017, January 26\u201329). Multi-level anomaly detection in industrial control systems via package signatures and LSTM networks. Proceedings of the 2017 47th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks (DSN), Denver, CO, USA.","DOI":"10.1109\/DSN.2017.34"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"e12564","DOI":"10.1111\/exsy.12564","article-title":"Real time detection of acoustic anomalies in industrial processes using sequential autoencoders","volume":"38","author":"Bayram","year":"2021","journal-title":"Expert Syst."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Benkabou, S.E., Benabdeslem, K., Kraus, V., Bourhis, K., and Canitia, B. (2021). Local Anomaly Detection for Multivariate Time Series by Temporal Dependency Based on Poisson Model. IEEE Trans. Neural Netw. Learn. Syst.","DOI":"10.1109\/TNNLS.2021.3083183"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Choi, T., Lee, D., Jung, Y., and Choi, H.J. (2022, January 12\u201315). Multivariate Time-series Anomaly Detection using SeqVAE-CNN Hybrid Model. Proceedings of the 2022 International Conference on Information Networking (ICOIN), Jeju-si, Korea.","DOI":"10.1109\/ICOIN53446.2022.9687205"},{"key":"ref_40","unstructured":"Zhang, C., Song, D., Chen, Y., Feng, X., Lumezanu, C., Cheng, W., Ni, J., Zong, B., Chen, H., and Chawla, N.V. (February, January 27). A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.matcom.2020.04.031","article-title":"Application of the residue number system to reduce hardware costs of the convolutional neural network implementation","volume":"177","author":"Valueva","year":"2020","journal-title":"Math. Comput. Simul."},{"key":"ref_42","first-page":"1793","article-title":"Statistical normalization and back propagation for classification","volume":"3","author":"Jayalakshmi","year":"2011","journal-title":"Int. J. Comput. Sci. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"879736","DOI":"10.1155\/2014\/879736","article-title":"Time series outlier detection based on sliding window prediction","volume":"2014","author":"Yu","year":"2014","journal-title":"Math. Probl. Eng."},{"key":"ref_44","unstructured":"Gupta, M., Sharma, A.B., Chen, H., and Jiang, G. (2013, January 2\u20134). Context-Aware Time Series Anomaly Detection for Complex Systems. Proceedings of the SDM Workshop on Data Mining for Service and Maintenance, Austin, TX, USA."},{"key":"ref_45","unstructured":"Guo, C., and Berkhahn, F. (2016). Entity embeddings of categorical variables. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Hallac, D., Vare, S., Boyd, S., and Leskovec, J. (2017, January 13\u201317). Toeplitz inverse covariance-based clustering of multivariate time series data. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada.","DOI":"10.1145\/3097983.3098060"},{"key":"ref_47","unstructured":"Claesen, M., Simm, J., Popovic, D., Moreau, Y., and De Moor, B. (2014). Easy hyperparameter search using optunity. arXiv."},{"key":"ref_48","unstructured":"Z\u00f6ller, M.A., and Huber, M.F. (2019). Benchmark and survey of automated machine learning frameworks. arXiv."},{"key":"ref_49","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified linear units improve restricted boltzmann machines. Proceedings of the ICML, Haifa, Israel."},{"key":"ref_50","unstructured":"Maas, A.L., Hannun, A.Y., and Ng, A.Y. (2013, January 16\u201321). Rectifier nonlinearities improve neural network acoustic models. Proceedings of the ICML, Atlanta, GA, USA."},{"key":"ref_51","unstructured":"Clevert, D.A., Unterthiner, T., and Hochreiter, S. (2015). Fast and accurate deep network learning by exponential linear units (elus). arXiv."},{"key":"ref_52","unstructured":"Klambauer, G., Unterthiner, T., Mayr, A., and Hochreiter, S. (2017). Self-normalizing neural networks. arXiv."},{"key":"ref_53","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_54","first-page":"26","article-title":"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude","volume":"4","author":"Tieleman","year":"2012","journal-title":"Coursera Neural Netw. Mach. Learn."},{"key":"ref_55","unstructured":"Zeiler, M.D. (2012). Adadelta: An adaptive learning rate method. arXiv."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1214\/aoms\/1177729392","article-title":"Stochastic estimation of the maximum of a regression function","volume":"23","author":"Kiefer","year":"1952","journal-title":"Ann. Math. Stat."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1541880.1541882","article-title":"Survey of anomaly detection","volume":"41","author":"Chandola","year":"2009","journal-title":"ACM Comput. Surv."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Kovalenko, I., Saez, M., Barton, K., and Tilbury, D. (2017). SMART: A system-level manufacturing and automation research testbed. Smart Sustain. Manuf. Syst., 1.","DOI":"10.1520\/SSMS20170006"},{"key":"ref_59","unstructured":"Sun, S. (2022, March 08). CNC Mill Tool Wear. Available online: https:\/\/www.kaggle.com\/shasun\/tool-wear-detection-in-cnc-mill."},{"key":"ref_60","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). Tensorflow: A system for large-scale machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), Savannah, GA, USA."},{"key":"ref_61","unstructured":"Chollet, F. (2022, March 08). Keras. Available online: https:\/\/github.com\/fchollet\/keras."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Kumar, S., Kolekar, T., Kotecha, K., Patil, S., and Bongale, A. (2022). Performance evaluation for tool wear prediction based on Bi-directional, Encoder\u2013Decoder and Hybrid Long Short-Term Memory models. Int. J. Qual. Reliab. Manag.","DOI":"10.1108\/IJQRM-08-2021-0291"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"3803","DOI":"10.1007\/s00170-021-08448-7","article-title":"A hybrid CNN-BiLSTM approach-based variational mode decomposition for tool wear monitoring","volume":"119","author":"Bazi","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1497","DOI":"10.1007\/s10845-019-01526-4","article-title":"A hybrid information model based on long short-term memory network for tool condition monitoring","volume":"31","author":"Cai","year":"2020","journal-title":"J. Intell. Manuf."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"3217","DOI":"10.1007\/s00170-018-2420-0","article-title":"Tool condition monitoring using spectral subtraction and convolutional neural networks in milling process","volume":"98","author":"Aghazadeh","year":"2018","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"3647","DOI":"10.1007\/s00170-019-04090-6","article-title":"Tool wear classification using time series imaging and deep learning","volume":"104","author":"Terrazas","year":"2019","journal-title":"Int. J. Adv. Manuf. Technol."}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/4\/2\/15\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:49:11Z","timestamp":1760136551000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/4\/2\/15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,2]]},"references-count":66,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["make4020015"],"URL":"https:\/\/doi.org\/10.3390\/make4020015","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,2]]}}}