Model self-matching fusion health prediction method

Abstract

The invention discloses a model-self-matching and fusion health forecasting method. The method includes the steps that 1, a forecasting-model total base is constructed according to a plurality of existing mature forecasting models; then the forecasting models in the forecasting-model total base are subjected to forecasting-result error testing based on time series data of the health parameters of a to-be-tested system, multiple forecasting models with forecasting-result errors meeting normal distribution are selected to serve as combinational forecasting models, one matched forecasting model with the forecasting-result errors meeting the tolerance requirement is further determined to be used for forecasting the health parameters, the forecasting results of the matched forecasting model are fused, and finally the forecasting value of the health parameters of the to-be-tested system is obtained. By means of the model-self-matching and fusion health forecasting method, newest observation data can be tracked in real time, automatic selecting and matching of the multiple forecasting models are carried out, the forecasting results of the multiple forecasting models are automatically selected and fused according to the characteristics of the data, and the model-self-matching and fusion health forecasting method is particularly suitable for long-term accurate forecasting of a system with the unobvious change regularity or the complex change regularity.

Description

technical field [0001] The invention relates to the technical field of failure prediction and health management, in particular to a model self-matching fusion health prediction method. Background technique [0002] In the process of equipment or system health prediction, it is usually assumed that the degradation law remains unchanged throughout the prediction period, so a single prediction model is used for health prediction. For equipment or systems with specific or fixed degradation laws, a single prediction model can show better prediction results. However, in the actual degradation process of most equipment or systems in the health state, the degradation law or degradation mode is often constantly changing. There are differences in the degradation state in different degradation modes. Even a single prediction model is a model with adaptive parameter adjustment capabilities. Due to the limitations of the model itself, it is difficult to apply to different degradation mo...

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Problems solved by technology

However, in the actual degradation process of most equipment or systems in the health state, the degradation law or degradation mode is often constantly changing. There are differences in the degradation state in different degradation modes. Even a single prediction model is a model with adaptive parameter adjustment capabilities. Due to the limitations of the model itself, it is difficult to apply to different degradation modes

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