Carla Terron-Santiago

The rapid integration of generative artificial intelligence (AI) into higher education raises questions about its pedagogical value and its role in sustainability-oriented learning. This study investigates the use of AI in a Challenge-Based Learning (CBL) engineering hackathon (INDUSHack) focused on the Sustainable Development Goals (SDGs), examining patterns of AI adoption, perceived benefits and risks, and its contribution to SDG integration in project design. A descriptive exploratory design was employed, collecting questionnaire data from 60 undergraduate participants and complementing these with expert committee evaluations of project outcomes. Results indicate widespread AI use, primarily for idea generation, information synthesis, and project structuring. Participants reported perceived improvements in efficiency, creativity, and the clarity of SDG alignment; however, difficulties persisted in translating sustainability concepts into measurable, technically grounded criteria. The findings suggest that while AI can support learning processes and sustainability framing in CBL environments, its effective use requires structured guidance and critical AI literacy to ensure meaningful integration of SDGs.

Purpose: The main objective of this purpose is to provide a general and systematic method for obtaining the inductance matrices of induction machines under rotor asymmetry faults without requiring a full redefinition of the winding model for each case. Design/methodology/approach: This paper combines the winding tensor formalism with the use of connection tensors to automatically generate the inductances corresponding to any configuration of broken bars. Findings: Firstly, it introduces a unified mathematical framework capable of modelling arbitrary rotor asymmetries using only the inductances of the healthy machine. Secondly, it demonstrates how connection tensors can encode the structural effects of faults in a compact and algebraically tractable manner. Thirdly, it provides a scalable and computationally efficient procedure that avoids the cumbersome derivation of fault-specific inductance expressions, thereby facilitating both numerical simulation and the development of diagnostic tools. Originality/value: The methodology presented in this paper offers a significant simplification of the modelling process while preserving the accuracy required for the analysis of fault-induced harmonic phenomena.

The reliability of wind turbines largely depends on the ability to detect electrical and mechanical faults under variable operating conditions. This paper applies the Non-steady-state Harmonic Order Tracking Analysis (NsHOTA) method to the diagnosis of doubly-fed induction generators (DFIGs) in real wind turbines. Unlike other steady-state and transient techniques, NsHota stabilizes and enhances fault components in any operating regime, allowing for more in-depth analysis. Therefore, this method enables highly accurate fault diagnosis, allowing the measurement and analysis of small degradations over time. The method is validated using eight months of field data from an 850 kW DFIG previously diagnosed with mixed eccentricity. The results demonstrate that NsHOTA improves the consistency and quality of fault feature extraction, reduces background noise, and avoids false negatives under steady and non-steady regimes. In the real data test, NsHOTA is also compared with the steady-state HOTA (SsHOTA) method. These findings confirm the robustness of NsHOTA for real-world wind turbine condition monitoring and highlight its potential integration into predictive maintenance systems.

This paper presents an innovative two-level modeling approach for accurately simulating the inductance matrix of induction machines (IMs) under fault conditions. The first level uses Finite Element Method (FEM) simulations to extract the inductance matrix $L(\theta)$ of the healthy machine, achieving high spatial resolution with minimal computational resources. The second level enables the introduction of rotor asymmetry faults using tensor algebra, thus eliminating the need for repeated FEM simulations. This approach enhances the efficiency of fault simulations, making it suitable for real-time diagnostic applications. The model is validated through both steady-state and transient analysis, demonstrating its effectiveness in reproducing fault-related harmonics and offering significant improvements in computational performance compared to traditional time-stepping FEM models.