Improved approximation of arbitrary shapes in dem simulations with multi-spheres

DEM simulations are originally made for spherical particles only. But most of real particles are anything but not spherical. Due to this problem, the multi-sphere method was invented. It provides the possibility to clump several spheres together to create complex shape structures. The proposed algorithm offers a novel method to create multi-sphere clumps for the given arbitrary shapes. Especially the use of modern clustering algorithms, from the field of computational intelligence, achieve satisfactory results. The clustering is embedded into an optimisation algorithm which uses a pre-defined criterion. A mostly unaided algorithm with only a few input and hyperparameters is able to approximate arbitrary shapes

Quaternion Backpropagation

Quaternion valued neural networks experienced rising popularity and interest from researchers in the last years, whereby the derivatives with respect to quaternions needed for optimization are calculated as the sum of the partial derivatives with respect to the real and imaginary parts. However, we can show that product- and chain-rule does not hold with this approach. We solve this by employing the GHRCalculus and derive quaternion backpropagation based on this. Furthermore, we experimentally prove the functionality of the derived quaternion backpropagation

Reinforcement Learning on Job Shop Scheduling Problems Using Graph Networks

This paper presents a novel approach for job shop scheduling problems using deep reinforcement learning. To account for the complexity of production environment, we employ graph neural networks to model the various relations within production environments. Furthermore, we cast the JSSP as a distributed optimization problem in which learning agents are individually assigned to resources which allows for higher flexibility with respect to changing production environments. The proposed distributed RL agents used to optimize production schedules for single resources are running together with a co-simulation framework of the production environment to obtain the required amount of data. The approach is applied to a multi-robot environment and a complex production scheduling benchmark environment. The initial results underline the applicability and performance of the proposed method.Comment: 8 pages, pre-prin

Information Fusion for Assistance Systems in Production Assessment

We propose a novel methodology to define assistance systems that rely on information fusion to combine different sources of information while providing an assessment. The main contribution of this paper is providing a general framework for the fusion of n number of information sources using the evidence theory. The fusion provides a more robust prediction and an associated uncertainty that can be used to assess the prediction likeliness. Moreover, we provide a methodology for the information fusion of two primary sources: an ensemble classifier based on machine data and an expert-centered model. We demonstrate the information fusion approach using data from an industrial setup, which rounds up the application part of this research. Furthermore, we address the problem of data drift by proposing a methodology to update the data-based models using an evidence theory approach. We validate the approach using the Benchmark Tennessee Eastman while doing an ablation study of the model update parameters.Comment: 21 Pages, 10 Figure

Active fault tolerant control of an electro-hydraulic servo axis with a duplex-valve-system

In this paper fault detection, fault diagnosis and active fault tolerant control of an electro-hydraulic servo-axis with a duplex-valve-system are described. The fault detection is based on parity equations. The semi-physical models allow the detection of even small faults in the hydraulic system. The fault diagnosis used on the testbed is based on fuzzy-logic. In order to tolerate a failed hydraulic proportional valve, a duplex-valve-system built up with standard direct-driven proportional valves is applied. The fault management module allows the supervision of the hydraulic servo axis and decides on reconfiguration and fault accomodation of the control-loop. The Internal Model Control (IMC)-tracking control structure used for reconfiguration allows bumpless transfer between controllers. Experimental results show the industrial applicability of the approach