Application of dynamic ontology modeling techniques in power equipment fault prediction

Xinyao Feng; Yingwei Liang; Shaoguang Liu; Xiaolu Li; Hanyang Xie

doi:10.1117/12.2671923

28 February 2023 Application of dynamic ontology modeling techniques in power equipment fault prediction

Xinyao Feng, Yingwei Liang, Shaoguang Liu, Xiaolu Li, Hanyang Xie

Author Affiliations +

Proceedings Volume 12596, International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022); 125960E (2023) https://doi.org/10.1117/12.2671923
Event: International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022), 2022, Changsha, China

Abstract

Power equipment failure prediction method has the problem of high cumulative deterioration, and a power equipment failure prediction method based on dynamic ontology modeling technology is designed to solve the above problem. It evaluates the health status of power equipment, clarifies the performance degradation range of equipment according to the characteristics reflected in different stages, constructs a residual life judgment model by combining the mechanism of reliability function, clarifies the performance degradation conditions and failure threshold of power equipment, and optimizes the fault prediction process by using dynamic ontology modeling technology. The test results showed that the mean values of cumulative degradation of the power equipment failure prediction method in the paper and three other power equipment failure prediction methods are 1.612, 3.263, 3.207, and 3.234, respectively, indicating that the power equipment failure prediction method designed after incorporating dynamic ontology modeling technique has higher use value.

Citation Download Citation

Xinyao Feng, Yingwei Liang, Shaoguang Liu, Xiaolu Li, and Hanyang Xie "Application of dynamic ontology modeling techniques in power equipment fault prediction", Proc. SPIE 12596, International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022), 125960E (28 February 2023); https://doi.org/10.1117/12.2671923

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