AAKR model uncertainty calculation method and system based on resampling

Abstract

The invention discloses an AAKR model uncertainty calculation method and system based on resampling, and the method comprises the steps: dividing a historical state data set of a sensor into a training data set and a testing data set, carrying out the denoising on the training data set through a wavelet denoising method, calculating a noise variance, improving the data precision, randomly selecting and replacing the historical state data of the sensor to obtain a new training data set sample so as to optimize the AAKR model architecture and the change among the plurality of model prediction values to obtain the model prediction variance of the plurality of model prediction values, and calculating the mean square error between the prediction values and the test values by utilizing Bootstrapresampling training data. Model deviation is calculated by combining prototype model variance, 95% uncertainty value is formed, modeling calculation of a noise estimation value by an empirical distribution model is not needed, the resampling process is simplified, the calculation efficiency is improved, confidence interval deviation is reduced by combining a Jackknife method, the reliability is guaranteed, and the estimation efficiency is improved on the basis of keeping convergence performance.

Description

technical field [0001] The invention relates to a quantification method of AAKR model uncertainty, in particular to a resampling-based AAKR model uncertainty calculation method and system. Background technique [0002] The online condition monitoring system of key equipment in nuclear power plants can help reduce the risk of catastrophic failure and reduce the unnecessary cost caused by unnecessary regular maintenance. Among them, the condition monitoring method based on the empirical model does not depend on the in-depth understanding of the fault mechanism model. Starting from the historical operating data and operating experience of the equipment, it can be used to determine whether the equipment is abnormal. With the rapid development of the Internet of Things and big data technology, it is widely used. . However, when the empirical model is used to monitor key nuclear power equipment, it involves ill-posed problems that affect the stability of the model, and must be ac...

AI Technical Summary

Problems solved by technology

At present, there are few studies on the uncertainty analysis of model regression values. The traditional Monte Carlo uncertainty determination method uses probability distribution to simulate noise to obtain sampling data, which requires prior knowledge of the overall distribution and large enough sample data, which is inefficient and economical. High, unable to effectively ensure the prediction accuracy of key equipment sensor status

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