Analysis and fault diagnosis in point absorber wave energy conversion systems using fault tree and Bayesian networks

• Autorzy: Guemmour Mohamed Boutkhil , Negadi Karim , Araria Rabah , Bey Mohamed

Identyfikatory

DOI

10.29354/diag/204573

• Języki publikacji: EN

Abstrakty

EN

This paper presents a comprehensive analysis and fault diagnosis approach for wave energy conversion (WEC) systems, specifically focusing on point absorber technology, using Bayesian Networks (BNs). The main objective of this work is to develop a probabilistic framework that enhances fault detection and diagnosis by modeling the interdependencies between key subsystems, including the power take-off (PTO) mechanism, mooring lines, and electrical components. Wave energy conversion systems offer a promising solution for sustainable energy generation, but fault detection remains a critical challenge in ensuring continuous and efficient operation. The proposed approach enables a probabilistic evaluation of failure modes and their impact on overall system performance by modeling the complex interdependencies between system components. By integrating environmental factors, historical failure logs, and operational data, the Bayesian network allows real-time dynamic updates of fault probabilities, facilitating predictive maintenance techniques. The proposed approach aims to improve system reliability, reduce downtime, and optimize maintenance strategies. Case studies are provided to validate the approach, demonstrating significant improvements in early fault detection. The results underscore the potential of Bayesian networks as a powerful tool for enhancing the operational resilience and sustainability of wave energy conversion systems. The analysis focuses on key subsystems, including the power take-off mechanism, mooring lines, and electrical components, where failures are most likely to occur due to harsh marine conditions.

Słowa kluczowe

EN

wave energy converters; Bayesian networks; point absorber; fault tree analysis; probabilistic modeling

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2025
• Tom: Vol. 26, No. 2
• Strony: art. no. 202509
• Opis fizyczny: Bibliogr. 36 poz., rys., tab.

Twórcy

autor

Guemmour Mohamed Boutkhil

• Industrial Technologies Research Laboratory, Department of Mechanical Engineering, of Applied Science, University of Tiaret, Algeria

• https://orcid.org/0009-0002-4151-1256

autor

Negadi Karim

• L2GEGI Laboratory, Department of Electrical Engineering, Faculty of Applied Science, University of Tiaret, Algeria

• https://orcid.org/0000-0002-3377-0402

autor

Araria Rabah

• L2GEGI Laboratory, Department of Electrical Engineering, Faculty of Applied Science, University of Tiaret, Algeria

• https://orcid.org/0000-0002-6772-8245

autor

Bey Mohamed

• L2GEGI Laboratory, Department of Electrical Engineering, Faculty of Applied Science, University of Tiaret, Algeria

• https://orcid.org/0009-0007-5751-962X

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