5th International Conference on System-Integrated Intelligence - Intelligent, Flexible and Connected Systems in Products and Production

Learning Damage Event Discriminator Functions with Distributed Multi-instance RNN/LSTM Machine Learning - Mastering the Challenge

Common Structural Health Monitoring systems are used to detect past damages occurred in structures with sensor networks and external sensor data processing. The time of the damage creation event is commonly unknown. Numerical methods and Machine Learning are used to extract relevant damage information from sensor signals that is characterised by a high data volume and dimension. In this work, distributed multi-instance learning applied to raw time-series of sensor data is deployed to predict the event of the occurrence of a hidden damage in a mechanical structure using typical vibrations of the structure. The sensor processing and learning is performed locally on sensor node level with a global fusion of prediction results to estimate the damage location and the time of the damage creation. Recurrent neural networks with a long-short-term memory architecture are considered implementing a damage discriminator function. The sensor data required for the evaluation of the proposed approach is generated by a multi-body physics simulation approximating material properties.


Self-adaptive Traffic and Logistics Flow Control using Learning Agents and Ubiquitous Sensors

Traffic flow optimisation is a distributed complex problem. Traditional traffic and logistics flow control algorithms operate on a system level and address mostly switching cycle adaptation of traffic signals and lights. This work addresses traffic flow optimisation by self-adaptive micro-level control by combining Reinforcement Learning and rule-based agent models for action selection with a new hybrid agent architecture. I.e., long-range routing is performed by agents that adapt their decision making for re-routing on local environmental sensors. Agent-based modelling and simulation are used to study emergence effects on urban city traffic flows with learning agents. The approach and the proposed agent architecture can be generalised and applied to a broader range of application fields, e.g., logistics and general transport phenomena.