{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:26:30Z","timestamp":1760232390184,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,28]],"date-time":"2022-10-28T00:00:00Z","timestamp":1666915200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Smart factories are complex; with the increased complexity of employed cyber-physical systems, the complexity evolves further. Cyber-physical systems produce high amounts of data that are hard to capture and challenging to analyze. Real-time recording of all data is not possible due to limited network capabilities. Limited network capabilities are the reason for a chain of faults introduced via active surveillance during fault diagnosis. These introduced faults may slow down production or lead to an outage of the production line. Here, we present a novel approach to automatically select production-relevant shop floor parameters to decrease the number of surveyed variables and, at the same time, maintain quality in fault diagnosis without overloading the network. We were able to achieve higher throughput, mitigate communication losses and prevent the disruption of factory instructions. Our approach uses an autoencoder ensemble via minority voting to differentiate between normal\u2014always on\u2014variables and production variables that may yield a higher entropy. Our approach has been tested in a production-equal smart factory and was cross-validated by a domain expert.<\/jats:p>","DOI":"10.3390\/s22218259","type":"journal-article","created":{"date-parts":[[2022,10,30]],"date-time":"2022-10-30T10:47:57Z","timestamp":1667126877000},"page":"8259","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Autoencoder-Ensemble-Based Unsupervised Selection of Production-Relevant Variables for Context-Aware Fault Diagnosis"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9411-6781","authenticated-orcid":false,"given":"Lukas","family":"Kaupp","sequence":"first","affiliation":[{"name":"Faculty of Computer Science, Darmstadt University of Applied Sciences, Haardtring 100, 64295 Darmstadt, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7805-1981","authenticated-orcid":false,"given":"Bernhard","family":"Humm","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Darmstadt University of Applied Sciences, Haardtring 100, 64295 Darmstadt, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2907-2740","authenticated-orcid":false,"given":"Kawa","family":"Nazemi","sequence":"additional","affiliation":[{"name":"Research Group Human-Computer Interaction and Visual Analytics, Faculty of Media, Darmstadt University of Applied Sciences, Haardtring 100, 64295 Darmstadt, Germany"}]},{"given":"Stephan","family":"Simons","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Information Technology, Darmstadt University of Applied Sciences, Haardtring 100, 64295 Darmstadt, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1007\/s00287-021-01343-1","article-title":"Machine intelligence today: Applications, methodology, and technology","volume":"44","author":"Humm","year":"2021","journal-title":"Inform. Spektrum"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kaupp, L., Nazemi, K., and Humm, B. (2020, January 7\u201311). An Industry 4.0-Ready Visual Analytics Model for Context-Aware Diagnosis in Smart Manufacturing. Proceedings of the 24th International Conference Information Visualisation (IV), Melbourne, Australia.","DOI":"10.1109\/IV51561.2020.00064"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1007\/978-3-030-93119-3_16","article-title":"Context-Aware Diagnosis in Smart Manufacturing: TAOISM, An Industry 4.0-Ready Visual Analytics Model","volume":"Volume 1014","author":"Kovalerchuk","year":"2022","journal-title":"Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Iarovyi, S., Lastra, J.L.M., Haber, R., and del Toro, R. (2015, January 22\u201324). From artificial cognitive systems and open architectures to cognitive manufacturing systems. Proceedings of the 2015 IEEE 13th International Conference on Industrial Informatics (INDIN), Cambridge, UK.","DOI":"10.1109\/INDIN.2015.7281910"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.promfg.2017.04.023","article-title":"Learning in the AutFab\u2014The Fully Automated Industrie 4.0 Learning Factory of the University of Applied Sciences Darmstadt","volume":"9","author":"Simons","year":"2017","journal-title":"Procedia Manuf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1016\/j.procs.2021.01.265","article-title":"CONTEXT: An Industry 4.0 Dataset of Contextual Faults in a Smart Factory","volume":"180","author":"Kaupp","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1007\/978-3-540-71080-6_6","article-title":"Visual Analytics: Scope and Challenges","volume":"Volume 4404","author":"Hutchison","year":"2008","journal-title":"Visual Data Mining"},{"key":"ref_8","unstructured":"Kaupp, L., Beez, U., Humm, B.G., and H\u00fclsmann, J. (2017, January 22\u201323). From Raw Data to Smart Documentation: Introducing a Semantic Fusion Process for Cyber-Physical Systems. Proceedings of the CERC2017 Collaborative European Research Conference, Karlsruhe, Germany."},{"key":"ref_9","first-page":"163","article-title":"Context-Aware Documentation in the Smart Factory","volume":"Volume 23","author":"Hoppe","year":"2018","journal-title":"Semantic Applications"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/978-3-030-20257-6_5","article-title":"Outlier Detection in Temporal Spatial Log Data Using Autoencoder for Industry 4.0","volume":"Volume 1000","author":"Macintyre","year":"2019","journal-title":"Engineering Applications of Neural Networks"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Putnings, M., Neuroth, H., and Neumann, J. (2021). Datenvisualisierung. Praxishandbuch Forschungsdatenmanagement, De Gruyter Saur. De Gruyter Praxishandbuch.","DOI":"10.1515\/9783110657807"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kreimel, P., Eigner, O., and Tavolato, P. (September, January 29). Anomaly-Based Detection and Classification of Attacks in Cyber-Physical Systems. Proceedings of the ARES 2017: The 12th International Conference on Availability, Reliability and Security, Reggio Calabria, Italy.","DOI":"10.1145\/3098954.3103155"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1164","DOI":"10.1109\/TDSC.2019.2914035","article-title":"A Machine Learning-Based Intrusion Detection System for Securing Remote Desktop Connections to Electronic Flight Bag Servers","volume":"18","author":"Bitton","year":"2019","journal-title":"IEEE Trans. Dependable Secur. Comput."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kim, J., Yun, J.H., and Kim, H.C. (2019). Anomaly Detection for Industrial Control Systems Using Sequence-to-Sequence Neural Networks. Computer Security, Springer.","DOI":"10.1007\/978-3-030-42048-2_1"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"119804","DOI":"10.1109\/ACCESS.2019.2936816","article-title":"Multivariate Gaussian-Based False Data Detection Against Cyber-Attacks","volume":"7","author":"An","year":"2019","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"8471","DOI":"10.1109\/JIOT.2019.2919635","article-title":"Attack Detection for Securing Cyber Physical Systems","volume":"6","author":"Yan","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Bernieri, G., Conti, M., and Turrin, F. (2019, January 8\u201310). Evaluation of Machine Learning Algorithms for Anomaly Detection in Industrial Networks. Proceedings of the 2019 IEEE International Symposium on Measurements & Networking (M&N), Catania, Italy.","DOI":"10.1109\/IWMN.2019.8805036"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Potluri, S., and Diedrich, C. (2019, January 22\u201326). Deep Learning based Efficient Anomaly Detection for Securing Process Control Systems against Injection Attacks. Proceedings of the 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), Vancouver, BC, Canada.","DOI":"10.1109\/COASE.2019.8843140"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Macas, M., and Wu, C. (2019, January 16\u201319). An Unsupervised Framework for Anomaly Detection in a Water Treatment System. Proceedings of the 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Boca Raton, FL, USA.","DOI":"10.1109\/ICMLA.2019.00212"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"101752","DOI":"10.1016\/j.cose.2020.101752","article-title":"A deep learning method with wrapper based feature extraction for wireless intrusion detection system","volume":"92","author":"Kasongo","year":"2020","journal-title":"Comput. Secur."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"65520","DOI":"10.1109\/ACCESS.2020.2985089","article-title":"IMPACT: Impersonation Attack Detection via Edge Computing Using Deep Autoencoder and Feature Abstraction","volume":"8","author":"Lee","year":"2020","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LSENS.2020.3007880","article-title":"Industrial Anomaly Detection: A Comparison of Unsupervised Neural Network Architectures","volume":"4","author":"Siegel","year":"2020","journal-title":"IEEE Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Inoue, J., Yamagata, Y., Chen, Y., Poskitt, C.M., and Sun, J. (2017, January 18\u201321). Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning. Proceedings of the 17th IEEE International Conference on Data Mining Workshops, New Orleans, LA, USA.","DOI":"10.1109\/ICDMW.2017.149"},{"key":"ref_24","unstructured":"Eiteneuer, B., and Niggemann, O. (2018, January 27\u201330). LSTM for Model-Based Anomaly Detection in Cyber-Physical Systems. Proceedings of the 29th International Workshop on Principles of Diagnosis DX\u201918, Warsaw, Poland."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"74933","DOI":"10.1109\/ACCESS.2020.2988797","article-title":"A Time Convolutional Network Based Outlier Detection for Multidimensional Time Series in Cyber-Physical-Social Systems","volume":"8","author":"Meng","year":"2020","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"59406","DOI":"10.1109\/ACCESS.2021.3072916","article-title":"Deep Learning Anomaly Detection for Cellular IoT with Applications in Smart Logistics","volume":"9","author":"Savic","year":"2021","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ketonen, V., and Blech, J.O. (2021, January 10\u201312). Anomaly Detection for Injection Molding Using Probabilistic Deep Learning. Proceedings of the 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), Online.","DOI":"10.1109\/ICPS49255.2021.9468190"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Balogh, A., Mehta, D., Sobonski, P., Mady, A., and Vuppala, S. (2019, January 19\u201323). Learning Constraint-Based Model for Detecting Malicious Activities in Cyber Physical Systems. Proceedings of the 2019 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), Leicester, UK.","DOI":"10.1109\/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00253"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5790","DOI":"10.1109\/TII.2020.3047675","article-title":"Siamese Neural Network Based Few-Shot Learning for Anomaly Detection in Industrial Cyber-Physical Systems","volume":"17","author":"Zhou","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Cui, J., Zhou, R., and Wang, Z. (2021, January 10\u201312). Time Frequency Feature Analysis of Rolling Bearing Fault Based on Deep Transfer Learning. Proceedings of the 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), Online.","DOI":"10.1109\/ICPS49255.2021.9468216"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Li, C., Guo, L., Gao, H., Yang, J., Dong, X., and You, Z. (2021, January 10\u201312). A Transfer Learning Based Method for Incipient Fault Detection. Proceedings of the 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), Online.","DOI":"10.1109\/ICPS49255.2021.9468218"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Arman, A., Krishnan, V.V.G., Srivastava, A., Wu, Y., and Sindhu, S. (2018, January 9\u201311). Cyber physical security analytics for transactive energy systems using ensemble machine learning. Proceedings of the 2018 North American Power Symposium (NAPS), Fargo, ND, USA.","DOI":"10.1109\/NAPS.2018.8600639"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hu, C., Yan, J., and Wang, C. (2019, January 16\u201318). Robust Feature Extraction and Ensemble Classification Against Cyber-Physical Attacks in the Smart Grid. Proceedings of the 2019 IEEE Electrical Power and Energy Conference (EPEC), Montreal, QC, Canada.","DOI":"10.1109\/EPEC47565.2019.9074827"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Dutta, V., Chora\u015b, M., Pawlicki, M., and Kozik, R. (2020). A Deep Learning Ensemble for Network Anomaly and Cyber-Attack Detection. Sensors, 20.","DOI":"10.3390\/s20164583"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1007\/978-3-030-30577-2_77","article-title":"Ensembled Deep Learning Approach for Maritime Anomaly Detection System","volume":"Volume 605","author":"Hoque","year":"2020","journal-title":"Proceedings of the ICETIT 2019: International Conference on Emerging Trends in Information Technology"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Al-Abassi, A., Sakhnini, J., and Karimipour, H. (2020, January 11\u201314). Unsupervised Stacked Autoencoders for Anomaly Detection on Smart Cyber-physical Grids. Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Toronto, ON, Canada.","DOI":"10.1109\/SMC42975.2020.9283064"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Feng, C., and Tian, P. (2021, January 14\u201318). Time Series Anomaly Detection for Cyber-physical Systems via Neural System Identification and Bayesian Filtering. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, Singapore.","DOI":"10.1145\/3447548.3467137"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Cai, F., and Koutsoukos, X. (2020, January 21\u201325). Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systems. Proceedings of the 2020 ACM\/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS), Sydney, Australia.","DOI":"10.1109\/ICCPS48487.2020.00024"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Tertytchny, G., and Michael, M.K. (September, January 31). Dataset Reduction Framework For Intelligent Fault Detection In IoT-based Cyber-Physical Systems Using Machine Learning Techniques. Proceedings of the 2020 International Conference on Omni-layer Intelligent Systems (COINS), Barcelona, Spain.","DOI":"10.1109\/COINS49042.2020.9191393"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8259\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:04:46Z","timestamp":1760144686000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8259"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,28]]},"references-count":39,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22218259"],"URL":"https:\/\/doi.org\/10.3390\/s22218259","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,10,28]]}}}