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13 May 2024 Unsupervised learning based failure modes occurrence measurement method of hydroturbine
Ge Xu, Zhongyao Cao, Chunhui Zhang, Shixin Jiang, Zhenguo Liu
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Proceedings Volume 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023); 131599M (2024) https://doi.org/10.1117/12.3024650
Event: Eighth International Conference on Energy System, Electricity and Power (ESEP 2023), 2023, Wuhan, China
Abstract
Failure modes occurrence measurement, one of the necessary steps of FMEA, has a significant impact on the accuracy of it. However, the existing occurrence measurement methods have strong dependence of subjectivity and label data. To solve the above problems, an unsupervised learning based failure modes occurrence measurement method of hydroturbine is proposed in this paper. The self-organizing map is used to establish the baseline model. And then the minimum quantitative error calculated by the baseline model is used to quantitatively evaluate the fault probability and the occurrence can be calculated by this probability. A case of tile burning fault is studied to described the proposed method in detail. The result shows that the proposed methods can efficiently calculate the occurrence.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Ge Xu, Zhongyao Cao, Chunhui Zhang, Shixin Jiang, and Zhenguo Liu "Unsupervised learning based failure modes occurrence measurement method of hydroturbine", Proc. SPIE 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023), 131599M (13 May 2024); https://doi.org/10.1117/12.3024650
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KEYWORDS
Data modeling
Machine learning
Failure analysis
Feature extraction
Risk assessment
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