Fault diagnosis and prediction model of building lightning protection system based on BIM and machine learning

Jing Zhao; Xiuqian Yang

doi:10.1117/12.2691946

23 August 2023 Fault diagnosis and prediction model of building lightning protection system based on BIM and machine learning

Jing Zhao, Xiuqian Yang

Author Affiliations +

Proceedings Volume 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023); 127842X (2023) https://doi.org/10.1117/12.2691946
Event: 2023 2nd International Conference on Applied Statistics, Computational Mathematics and Software Engineering (ASCMSE 2023), 2023, Kaifeng, China

Abstract

The current conventional fault diagnosis method of building lightning protection system mainly analyzes the fault diagnosis feature quantity to realize the fault type, and the lack of effective analysis of the fault signal waveform leads to poor diagnosis effect. In this regard, a fault diagnosis and prediction model of building lightning protection system based on BIM and machine learning is proposed. The 3D model of the building lightning protection system is constructed by combining BIM technology, and the vertices in the generated 3D model are reduced by the triangular network simplification method. The current signal flowing through the building lightning protector is collected and amplified by circuit design, and a fault diagnosis and prediction model is constructed based on the support vector machine algorithm. In the experiments, the diagnostic performance of the proposed model is verified. The experimental results show that the fault diagnosis model of the building lightning protection system constructed by the proposed method has a high recall rate and has a more desirable diagnostic effect.

Citation Download Citation

Jing Zhao and Xiuqian Yang "Fault diagnosis and prediction model of building lightning protection system based on BIM and machine learning", Proc. SPIE 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023), 127842X (23 August 2023); https://doi.org/10.1117/12.2691946

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