Multi-attribute group decision making method based on 2-dimension 2-tuple linguistic information

Sun, Yanhao; Zhang, Qi; Li, Zhi; Ren, Yumou; Zhang, Yunpeng; Yuan, Zhiming

doi:10.1109/icaiis49377.2020.9194838

Cited by 2 publications

(1 citation statement)

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“…Failure mode and effects analysis (FMEA) is a popular technique that focuses on predicting the equipment's potential failure modes and analyzing the possible impact of improving its reliability and safety. Recently, with the development of FMEA-related theories [1][2][3] , its application range has become broader. It has been widely used in various industries, including aerospace, automotive, electronics and so on.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised learning based failure modes occurrence measurement method of hydroturbine

Xu,

Cao,

Zhang

et al. 2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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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.

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Section: Introductionmentioning

confidence: 99%