Institute for Technologies and Management of Digital Transformation

Industrial Deep Learning

In the field of "Industrial Deep Learning", we research deep learning technologies for industrial applications in order to realise innovative solutions in production, logistics and the environment. We combine basic AI research with industrial practice and focus on three main areas

Visual inspection

Image-based methods for the automation of quality controls and the precise localisation of anomalies and damage.

Sensor-based situation and condition assessment

Processing and utilisation of transient and seasonal sensor data for condition monitoring, anomaly detection and forecasting.

Intelligent planning and process design

Learning methods for solving complex planning and optimisation problems and for evaluating and parameterising processes.

 

Our research addresses a broad spectrum of deep learning technologies, including different learning paradigms such as supervised and reinforcement learning, learning scenarios such as transfer learning, representation learning and explainable AI, as well as model architectures such as transformer networks, autoencoders and generative adversarial networks.

We work closely with industry partners, whether as part of publicly funded projects or direct R&D contracts. In doing so, we deal intensively with real challenges and always take the needs of end users and technical experts into account. This practical orientation ensures that our research results are not only theoretically sound, but also directly applicable in industrial practice and improve value creation.

2024
Langer, T., Meyes, R., & Meisen, T. (2024). "Guided Exploration of Industrial Sensor Data" , Computer Graphics Forum , 43 (1),
2023
Bulow, F., & Meisen, T. (2023). "A review on methods for state of health forecasting of lithium-ion batteries applicable in real-world operational conditions" , Journal of Energy Storage , 57 , 105978.
Steiniger, Y., Bueno, A., Kraus, D., & Meisen, T. (2023). "Tackling data scarcity in sonar image classification with hybrid scattering neural networks" in OCEANS 2023 - Limerick , IEEE 1--7.
Benkert, J., Maack, R., & Meisen, T. (2023). "Chances and Challenges: Transformation from a Laser-Based to a Camera-Based Container Crane Automation System" , Journal of Marine Science and Engineering , 11 (9), 1718.
Alves-Gomes, M., & Meisen, T. (2023). "A review on customer segmentation methods for personalized customer targeting in e-commerce use cases" , Information Systems and e-Business Management .

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