TY - CHAP A1 - Lodes, Lukas A1 - Schiendorfer, Alexander ED - Do, Phuc ED - Michau, Gabriel ED - Ezhilarasu, Cordelia T1 - Certainty Groups: A Practical Approach to Distinguish Confidence Levels in Neural Networks T2 - Proceedings of the European Conference of the PHM Society 2022 N2 - Machine Learning (ML), in particular classification with deep neural nets, can be applied to a variety of industrial tasks. It can augment established methods for controlling manufacturing processes such as statistical process control (SPC) to detect non-obvious patterns in high-dimensional input data. However, due to the widespread issue of model miscalibration in neural networks, there is a need for estimating the predictive uncertainty of these models. Many established approaches for uncertainty estimation output scores that are difficult to put into actionable insight. We therefore introduce the concept of certainty groups which distinguish the predictions of a neural network into the normal group and the certainty group. The certainty group contains only predictions with a very high accuracy that can be set up to 100%. We present an approach to compute these certainty groups and demonstrate our approach on two datasets from a PHM setting. UR - https://doi.org/10.36001/phme.2022.v7i1.3331 KW - machine Learning KW - classification KW - uncertainty KW - estimaton KW - neural network Y1 - 2022 UR - https://doi.org/10.36001/phme.2022.v7i1.3331 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-31307 SN - 978-1-936263-36-3 SP - 294 EP - 305 PB - PHM Society CY - State College ER - TY - CHAP A1 - Lodes, Lukas A1 - Schiendorfer, Alexander ED - Facchinetti, Tullio ED - Cenedese, Angelo ED - Lo Bello, Lucia ED - Vitturi, Stefano ED - Sauter, Thilo ED - Tramarin, Federico T1 - A multi-layer machine learning architecture for near real-time inference in manufacturing based on Apache Kafka and selective classification T2 - 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA61755.2024.10711030 Y1 - 2024 UR - https://doi.org/10.1109/ETFA61755.2024.10711030 SN - 979-8-3503-6123-0 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Lodes, Lukas A1 - Hass, Erik A1 - Dachtler, Kristina A1 - Schiendorfer, Alexander T1 - SmartManPy – Open Source Synthetic Manufacturing Data JF - Procedia Computer Science N2 - Data Science projects in manufacturing often suffer from difficult access to the necessary high-quality data, such as sensor readings or product quality records. In addition, strict rules due to data protection and intellectual property concerns hinder broad use of the provided data for innovative research, especially in the academic context. To enable the research and evaluation of approaches for a variety of Industry 4.0 use cases, we developed SmartManPy, an extension to the ManPy discrete event simulation environment. We extended the scope of the project to meet our needs and are now able to simulate production lines and the resulting data in just a few lines of code. SmartManPy (Smart Manufacturing Python) includes complex synthetic feature generation, quality control and failures based on recorded features, and data drift simulation. We additionally added a basic environment to train AI agents using reinforcement learning. Our code is released to the public on GitHub. UR - https://doi.org/10.1016/j.procs.2025.01.245 Y1 - 2025 UR - https://doi.org/10.1016/j.procs.2025.01.245 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-57650 SN - 1877-0509 VL - 2025 IS - 253 SP - 1830 EP - 1839 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Lodes, Lukas A1 - Schiendorfer, Alexander ED - Kinnaird, Katherine M. ED - Steinbach, Peter ED - Guhr, Oliver T1 - A Deep Learning Bootcamp for Engineering & Management Students T2 - Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop Y1 - 2023 UR - https://proceedings.mlr.press/v207/lodes23a.html SP - 32 EP - 36 PB - PMLR CY - [s. l.] ER -