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The influence of the unbalanced magnetic pull on fault-induced rotor eccentricity in induction motors KU Leuven
When performing bearing fault measurements, the unbalanced magnetic pull’s (UMP) influence is inadvertently incorporated. The UMP’s influence distorts the measurements used for bearing fault size estimation, leading to inaccurate fault interpretations. In this paper, we combined a magnetic equivalent circuit motor model with a dynamic bearing model to isolate the UMP’s effect, which is unprecedented in the state of the art. The coupled model is ...
Case Study Analysis of STPA on an Industrial Cooperative Robot and an Autonomous Mobile Robot KU Leuven
Framework to Evaluate Deep Learning Algorithms for Edge Inference and Training KU Leuven
Edge computing is a paradigm in which data is intelligently processed close to its source. Along with advancements in deep learning, there is a growing interest in using deep neural networks at the edge for predictive analytics. Given the realistic constraints in computational resources of edge devices, this combination is challenging. In order to bridge the gap between deep learning models and efficient edge analytics, a container-based ...
Safety Case Conversion from Goal Structuring Notation to Structured Assurance Case Metamodel KU Leuven
Assurance cases are a valuable tool in communicating arguments for the justification of system properties. Graphical notations provide a clear way to presents these arguments. However, many commercial tools fail to implement recent standards and provide no support to the transition between notations. This paper presents an assurance case tool fit to the most recent guidelines, providing support for multiple notations with the abiliy to convert a ...