FEA-Assisted Test Bench to Enhance the Comprehension of Vibration Monitoring in Electrical Machines—A Practical Experiential Learning Case Study

Rotating electrical machine maintenance is a core component of engineering education curricula worldwide. Within this context, vibration monitoring represents a widespread methodology for electrical rotating machinery monitoring. However, the multi-physical nature of vibration monitoring presents a complex learning scenario, including concepts from both mechanical and electrical engineering domains. This article proposes a novel knowledge-based educational experience design leveraging an integrated FEA-assisted test bench aimed at comprehensively addressing the electromechanical link between stator current and frame vibration. To this aim, a Finite Element Analysis (FEA) model is utilized to link excitation electrical signals with airgap radial forces acting in the stator. The subsequent correlation of these FEA predictions with measured frame vibrations on a physical test bench provides students with the theoretical concepts and practical tools to adequately comprehend this complex multi-physical phenomenon of wide application in real industrial scenarios. The pedagogical potential of the method also includes the development of critical thinking and problem-solving soft skills, and foundational understanding for digital twin concepts. A Delphi-style expert survey conducted with 25 specialists yielded strong support for the pedagogical robustness and relevance of the method, with mean ratings between 4.32 and 4.64 out of 5 across key dimensions. These results confirm the potential to enhance deep understanding and practical skills in vibration-based electrical machine diagnosis.

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Publication Details

Journal
Knowledge
Published
2025-08-12
DOI
https://doi.org/10.3390/knowledge5030016
Primary Topic
Experimental Learning in Engineering
Type
article
Field-Weighted Citation Impact
0.00

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article

FEA-Assisted Test Bench to Enhance the Comprehension of Vibration Monitoring in Electrical Machines—A Practical Experiential Learning Case Study

José E. Ruiz-Sarrió, Jose A. Antonino‐Daviu, Carlos Madariaga
Knowledge
Experimental Learning in Engineering
article

FEA-Assisted Test Bench to Enhance the Comprehension of Vibration Monitoring in Electrical Machines—A Practical Experiential Learning Case Study

José E. Ruiz-Sarrió, Jose A. Antonino‐Daviu, Carlos Madariaga
article en

Abstract

Rotating electrical machine maintenance is a core component of engineering education curricula worldwide. Within this context, vibration monitoring represents a widespread methodology for electrical rotating machinery monitoring. However, the multi-physical nature of vibration monitoring presents a complex learning scenario, including concepts from both mechanical and electrical engineering domains. This article proposes a novel knowledge-based educational experience design leveraging an integrated FEA-assisted test bench aimed at comprehensively addressing the electromechanical link between stator current and frame vibration. To this aim, a Finite Element Analysis (FEA) model is utilized to link excitation electrical signals with airgap radial forces acting in the stator. The subsequent correlation of these FEA predictions with measured frame vibrations on a physical test bench provides students with the theoretical concepts and practical tools to adequately comprehend this complex multi-physical phenomenon of wide application in real industrial scenarios. The pedagogical potential of the method also includes the development of critical thinking and problem-solving soft skills, and foundational understanding for digital twin concepts. A Delphi-style expert survey conducted with 25 specialists yielded strong support for the pedagogical robustness and relevance of the method, with mean ratings between 4.32 and 4.64 out of 5 across key dimensions. These results confirm the potential to enhance deep understanding and practical skills in vibration-based electrical machine diagnosis.

KnowledgeVol. 5(3)
University of Concepción (CL), Instituto Tecnológico de la Energía (ES)
European Commission
Quality Education
Openalex Percentile: Top 72%
Experimental Learning in Engineering
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