Electric gate valve remaining service life prediction method and system

Abstract

The invention discloses a method and system for predicting the remaining service life of an electric gate valve. The method comprises the steps: obtaining a to-be-predicted time sequence length and service life monitoring data of a to-be-predicted electric gate valve in the to-be-predicted time sequence length; forming first to-be-predicted two-dimensional data from the to-be-predicted time sequence length and the feature parameters corresponding to the to-be-predicted time sequence length; converting the first to-be-predicted two-dimensional data into to-be-predicted three-dimensional data according to the sliding time window; and inputting the to-be-predicted three-dimensional data into the trained electric gate valve remaining service life prediction model to obtain the remaining service life of the electric gate valve. According to the electric gate valve remaining service life prediction method and system, the time sequence characteristics contained in the life prediction characteristic data are fully considered, the time sequence characteristics in the degradation process can be better reflected, and the method and system have the advantage that the remaining service life prediction accuracy can be improved.

Description

technical field [0001] The invention relates to the technical field of predicting the remaining service life of electric gate valves, in particular to a method and system for predicting the remaining service life of electric gate valves. Background technique [0002] The remaining useful life (Remaining Useful Life, RUL) is the length from the current time to the end of the service life. The main task of life prediction is to predict the remaining time of the machine before losing its operating capacity based on the condition monitoring information. At present, life prediction research is divided into four categories in terms of research methods. The first category is multivariate statistical analysis methods, which mainly combine reliability analysis and probability theory to carry out research on RUL prediction technology, including autoregressive models, random coefficients, etc. model, Wiener process model, Gamma model, Markov model and filtering algorithm; the second ca...

AI Technical Summary

Problems solved by technology

The artificial neural network algorithm can learn more complex nonlinear relationships, but it cannot accurately describe the timing change process of component degradation, and its accuracy and applicability are poor

The fuzzy neural network algorithm makes full use of the advantages of expert knowledge and neural network, but still does not consider the timing characteristics of the degradation process, and the prediction accuracy for long timing problems is poor

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