Degradation data missing interpolation method based on support vector machine and RBF neural network

Abstract

The invention discloses a degradation data missing interpolation method based on a support vector machine and an RBF neural network. The method includes the following steps that firstly, a degeneration data trend model is established by means of the support vector machine; secondly, residual error sequences of observed degradation data are calculated; thirdly, the RBF neural network is set up, and the network is trained by means of the residual error sequences of the observed degradation data; fourthly, residual error sequences of missing data are estimated through the trained RBF neural network; fifthly, trend terms of the missing data and estimation results of the residual error sequences are merged, so that a degradation data interpolation result is obtained. A support vector machine method and an RBF neural network method are combined to obtain the degradation data missing interpolation method, and the problem of interpolation of performance missing degradation data in an accelerated degradation test is solved.

Description

technical field [0001] The invention relates to a degraded data missing interpolation method based on a support vector machine and an RBF neural network, and belongs to the technical field of accelerated degraded tests. Background technique [0002] In the data collection of accelerated degradation tests, due to the failure of monitoring equipment or the fault of manual recording personnel, the collected performance degradation data is often missing. The lack of data has caused difficulties in subsequent performance degradation data processing. In the data processing and evaluation of accelerated degradation tests, fault prediction or life prediction, complete data is required as input. In addition, many traditional performance degradation data processing methods cannot handle There are missing data for statistical analysis. For example, some algorithms about time series require the input data to be a complete equidistant data set. In life prediction or failure prediction, ...

AI Technical Summary

Problems solved by technology

[0010] The purpose of the present invention is to solve the missing interpolation problem of performance degradation data, and propose a kind of degraded data missing interpolation method based on support vector machine and RBF neural network with strong versatility

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