A python program to model and analyze wind speed data

doi:10.30534/ijeter/2021/24962021

Cited by 1 publication

(1 citation statement)

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“…Although the model is based on physics instead of being purely data-driven, each parameter has its actual physical meaning, so the magnitude of the parameter can be roughly deduced before the optimization process to make the optimization parameter more specific and reduce the amount of calculation. Then, an optimization program to minimize error compared to actual leakage data based on Python [ 34 ] is used to optimize the unknown parameters in the equation obtained in step (1).…”

Section: Structure Of the Integrated Methodsmentioning

confidence: 99%

An integrated method for the leakage fault mode diagnosis and life prediction of the reactor coolant pump

Shao,

Kang,

et al. 2024

PLoS ONE

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The reactor coolant pump is a key equipment in a nuclear power plant. If the leakage exceeds a certain threshold, it may cause reactor overheating and shutdown. The reactor coolant pump leakage fault usually has two problems: corrosion and scaling. Accurately and efficiently diagnosing the leakage fault mode as early as possible and predicting its remaining useful life (RUL) are important for taking timely maintenance measures. In this paper, an integrated method is proposed. First, the cross-sectional area of the first seal is extracted as a fault indicator. The motivation is that corrosion may enlarge the cross-sectional area, and scaling may reduce the cross-sectional area. Based on the fluid mechanics theory, an integrated model with several uncertain parameters is established among the cross-sectional area, temperature, and leakage at the inlet and outlet of the first seal. In the diagnosing process, a modified change-detection method is proposed to detect the starting point of degradation. Then, the unknown parameters in the previous relation are estimated, and the degrading data before the starting point of degradation are used to diagnose the leakage fault mode. Second, a time-series model of the autoregressive integrated moving average (ARIMA) is established to predict the remaining useful life based on the degrading data after the starting point of degradation. Finally, the leakage degrading data from six reactor coolant pumps of a nuclear power plant is used to perform the leakage fault mode diagnosis and life prediction with degradation point detection error rates not exceeding 4%, fault mode diagnosis correction rates 100% and practical RUL predicting results, which proves that the proposed integrated method is accurate and efficient. The proposed integrated method combines the advantages of both the physical model diagnosis and the data-driven model diagnosis and innovatively make use of the quantity of flow from the output side of the primary pump as the monitoring indicator and the cross-sectional area as the characteristic index together to diagnose the leakage fault mode happened to the seal and predict its RUL, which can meet the needs of actual operation and maintenance to ensure a healthy and stable operation of the pump and prevent unexpected shutdowns of nuclear power plants and serious accidents.

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Section: Structure Of the Integrated Methodsmentioning

confidence: 99%