Scalable anomaly detection in manufacturing systems using an interpretable deep learning approach

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561 · see citing works

The mythos of model interpretability

Lipton, Z. C. (2018) · Communications of the ACM · doi:10.1145/3233231 · Cited by 5,427 · see citing works

Deep Learning for IoT Big Data and Streaming Analytics: A Survey

Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2844341 · Cited by 1,471 · see citing works

Deep Learning for Anomaly Detection: A Survey

Chalapathy, R., & Chawla, S. (2019) · arXiv · doi:10.48550/arxiv.1901.03407 · Cited by 1,240 · see citing works

A review of unsupervised feature learning and deep learning for time-series modeling

Längkvist, M., Karlsson, L., & Loutfi, A. (2014) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.01.008 · Cited by 1,234 · see citing works

Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models

Samek, W., Wiegand, T., & Müller, K.-R. (2017) · arXiv · doi:10.48550/arxiv.1708.08296 · Cited by 1,080 · see citing works

Visual interpretability for deep learning: a survey

Zhang, Q., & Zhu, S. C. (2018) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1700808 · Cited by 975 · see citing works

DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series

Munir, M., Siddiqui, S. A., Dengel, A. R., & Ahmed, S. (2018) · IEEE Access · doi:10.1109/access.2018.2886457 · Cited by 710 · see citing works

Challenges in deploying machine learning: A survey of case studies

Paleyes, A., Urma, R.-G., & Lawrence, N. D. (2023) · ACM Computing Surveys · doi:10.1145/3533378 · Cited by 638 · see citing works

Intelligible models for classification and regression

Lou, Y., Caruana, R., & Gehrke, J. (2012) · Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2339530.2339556 · Cited by 547 · see citing works

Robust Deep Learning Methods for Anomaly Detection

Chalapathy, R., Khoa, N. L. D., & Chawla, S. (2020) · ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD) · doi:10.1145/3394486.3406704 · Cited by 503 · see citing works

Interpretability of deep learning models: A survey of results

Chakraborty, S., Tomsett, R., Raghavendra, R., Harborne, D., Alzantot, M., Cerutti, F., Srivastava, M. B., Preece, A. D., Julier, S., Rao, R., et al. (2017) · IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) · doi:10.1109/uic-atc.2017.8397411 · Cited by 469 · see citing works

Generic and Scalable Framework for Automated Time-series Anomaly Detection

Laptev, N., Amizadeh, S., & Flint, I. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2788611 · Cited by 455 · see citing works

The unreasonable effectiveness of deep learning in artificial intelligence

Sejnowski, T. J. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1907373117 · Cited by 352 · see citing works

Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations

Hohman, F., Park, H., Robinson, C., & Chau, D. H. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2934659 · Cited by 217 · see citing works

Challenges in the Deployment and Operation of Machine Learning in Practice

Baier, L., Jöhren, F., & Seebacher, S. (2019) · Repository KITopen (Karlsruhe Institute of Technology) · doi:10.5445/ir/1000095028 · Cited by 110 · see citing works

Unsupervised Online Anomaly Detection on Multivariate Sensing Time Series Data for Smart Manufacturing

Hsieh, R.-J., Chou, J., & Ho, C.-H. (2019) · IEEE International Conference on Service-Oriented Computing and Applications (SOCA) · doi:10.1109/soca.2019.00021 · Cited by 106 · see citing works

Learning Hierarchical Features from Deep Generative Models

Zhao, S., Song, J., & Ermon, S. (2017) · International Conference on Machine Learning · Cited by 86 · see citing works

Anomaly detection in discrete manufacturing using self-learning approaches

Lindemann, B., Fesenmayr, F., Jazdi, N., & Weyrich, M. (2019) · Procedia CIRP · doi:10.1016/j.procir.2019.02.073 · Cited by 85 · see citing works

Anomaly Detection in Manufacturing Systems Using Structured Neural Networks

Liu, J., Guo, J., Orlik, P. V., Shibata, M., Nakahara, D., Mii, S., & Takáč, M. (2018) · World Congress on Intelligent Control and Automation · doi:10.1109/wcica.2018.8630692 · Cited by 57 · see citing works

Squeezed Convolutional Variational AutoEncoder for unsupervised anomaly detection in edge device industrial Internet of Things

Kim, D., Yang, H., Chung, M., Cho, S., Kim, H., Kim, M., Kim, K., & Kim, E.-S. (2018) · International Conference on Information and Computer Technologies (ICICT) · doi:10.1109/infoct.2018.8356842 · Cited by 49 · see citing works

A Deep Learning-based Approach to Anomaly Detection with 2-Dimensional Data in Manufacturing

Maggipinto, M., Beghi, A., & Susto, G. A. (2019) · 2022 IEEE 20th International Conference on Industrial Informatics (INDIN) · doi:10.1109/indin41052.2019.8972027 · Cited by 18 · see citing works

Continuous Process Monitoring Through Ensemble-Based Anomaly Detection

Deuse, J., Wiegand, M., & Weisner, K. (2019) · Studies in classification, data analysis, and knowledge organization · doi:10.1007/978-3-030-25147-5_18 · Cited by 4 · see citing works

A Survey on Explainable Anomaly Detection

Li, Z., Zhu, Y., & Van Leeuwen, M. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3609333 · Cited by 139 · see citing works

Time-series pattern recognition in Smart Manufacturing Systems: A literature review and ontology

Farahani, M. A., McCormick, M. R., Gianinny, R., Hudacheck, F., Harik, R., Liu, Z., & Wuest, T. (2023) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2023.05.025 · Cited by 80 · see citing works

RFE Based Feature Selection and KNNOR Based Data Balancing for Electricity Theft Detection Using BiLSTM-LogitBoost Stacking Ensemble Model

Pamir, Javaid, N., Almogren, A., Adil, M., Javed, M. U., & Zuair, M. (2022) · IEEE Access · doi:10.1109/access.2022.3215532 · Cited by 28 · see citing works

A Comprehensive Review of Machine Learning Approaches for Anomaly Detection in Smart Homes: Experimental Analysis and Future Directions

Rahman, M. M., Gupta, D., Bhatt, S., Shokouhmand, S., & Faezipour, M. (2024) · Future Internet · doi:10.3390/fi16040139 · Cited by 26 · see citing works

A comprehensive survey on techniques, challenges, evaluation metrics and applications of deep learning models for anomaly detection

Kohli, M., & Chhabra, I. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-07312-7 · Cited by 19 · see citing works

Trustworthy Generative Few-Shot Learning-Based Intrusion Detection Method in Internet of Things

Zhang, Z., Wang, P., Zhang, T., Liu, M., & Zhou, X. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3473304 · Cited by 12 · see citing works

Toward Explainable AI in Spacecraft Health Monitoring: Comparative Benchmarking of Anomaly Detection Models and Open-Source Datasets

Tahir, H., Jabbarpour, M. R., Vo, Q. B., Kowalczyk, R., Barr, J., & Bessell, T. (2025) · IEEE Access · doi:10.1109/access.2025.3639551 · Cited by 7 · see citing works

A Passive Testing Approach using a Semi-Supervised Intrusion Detection Model for SCADA Network Traffic

Mühlburger, H., & Wotawa, F. (2022) · IEEE International Conference on Artificial Intelligence Testing (AITest) · doi:10.1109/aitest55621.2022.00015 · Cited by 3 · see citing works

Predictive analysis of wind power using bi-directional permutation enhanced LSTM-RNN on SCADA dataset

Sridhar, S., Hiremath, S., Kumar, N. K., Rahul, S. G., Teekaraman, Y., & Amirthalakshmi, T. M. (2026) · Array · doi:10.1016/j.array.2026.100772 · Cited by 2 · see citing works

Predictive Maintenance Based on Deep Learning: Early Identification of Failures in Heavy Machinery Components

Melgar, P. C., Chambi, L. H., & Torres, R. S. (2025) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2025.0160577 · Cited by 2 · see citing works

Fuzz Testing Molecular Representation Using Deep Variational Anomaly Generation

Nogueira, V. H. R., Sharma, R., Guido, R. V. C., & Keiser, M. J. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01876 · Cited by 1 · see citing works

Making Systems Fail-Aware: A Semi-Supervised Machine Learning Approach for Identifying Failures by Learning the Correct Behavior of a System

Mühlburger, H., & Wotawa, F. (2024) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2024.07.185 · Cited by 1 · see citing works

Descoberta de novos inibidores para doenças infecciosas: estudos integrados de modelagem molecular e aprendizado de máquina para a malária e o COVID-19

Nogueira, V. H. R. (2024) · doi:10.11606/t.76.2024.tde-23042024-120251

Intelligent Quality Management in Industrial Screwing Processes: From Rule-Based Control to Data-Driven Paradigms

West, N., Henkies, M., Mura, R., & Deuse, J. (2025) · doi:10.1109/ieem63636.2025.11357665

Persistence-Based Absolute Relative Error for Alarm-Centric Monitoring Under Low-Frequency Manufacturing

Song, J., & Kim, H. S. (2026) · Mathematics · doi:10.3390/math14050868

An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive ManufacturingPreprint

Jin, B., Tan, Y., Nettekoven, A., Chen, Y., Topcu, U., Yue, Y., & Sangiovanni-Vincentelli, A. L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.11778 · Cited by 13 · see citing works

Explainable anomaly detection for Hot-rolling industrial process

Jakubowski, J., Stanisz, P., Bobek, S., & Nalepa, G. J. (2021) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa53316.2021.9564228 · Cited by 31 · see citing works

DeepTimeAnomalyViz: A Tool for Visualizing and Post-processing Deep Learning Anomaly Detection Results for Industrial Time-SeriesPreprint

Leporowski, B., Hansen, C., & Iosifidis, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.10082

Anomaly detection in discrete manufacturing using self-learning approaches

Lindemann, B., Fesenmayr, F., Jazdi, N., & Weyrich, M. (2019) · Procedia CIRP · doi:10.1016/j.procir.2019.02.073 · Cited by 85 · see citing works

Development of a deep learning-based anomaly detection model using multivariate time series manufacturing data

Ku, M., & Lim, S. (2022) · Scholarworks@UNIST (Ulsan National Institute of Science and Technology)