Electromechanical device neural network failure trend prediction method

Abstract

The invention relates to an electromechanical device neural network failure trend prediction method, comprising the following steps: (1) obtain a section continuous vibration signal which is sensitive to the failure and is output by a measuring point sensor; (2) respectively carry out exceptional value elimination and missing data filling to the vibration data by a 3 sigma method and an interpolation method; (3) carry out a normalization process to a vibration data sequence; (4) calculate a vibration data sequence which is entropy-weighted according to the sequence which is carried out the normalization process; (5) carry out a time-weighted calculation to the vibration data sequence which is entropy-weighted by utilizing time weight due to the influence of time factor; (6) build a nonlinear dynamic recurrent neural network prediction model by utilizing the data sequence which is obtained by step (5) and determine a hidden layer optimal node number by utilizing a golden section method; (7) carry out normalization process to a trend prediction result and obtain a actual prediction result. A dynamic recurrent neural network model is adopted to carry out prediction in the invention, therefore, the failure prediction reliability is increased. The electromechanical device neural network failure trend prediction method can be widely applied to the failure prediction and analysis of all kinds of electromechanical devices.

Description

technical field [0001] The invention relates to a mechanical fault prediction method, in particular to a neural network fault trend prediction method for electromechanical equipment based on information entropy weighting and time factor weighting. Background technique [0002] Facing the nonlinear and non-stationary dynamic problems of electromechanical equipment failure prediction, traditional linearization processing methods are not effective, and some dynamic system analysis and processing methods that are nonlinear in nature have application prospects in fault prediction. The neural network prediction method has the function of self-learning, as well as the characteristics of nonlinearity, non-locality, and unsteadiness. By properly selecting the network level and the number of hidden layer units, it can approximate any continuous nonlinear function and its various orders with arbitrary precision. Derivative properties, thus widely used in fault prediction. At present, ...

AI Technical Summary

Problems solved by technology

In practical applications, the number of hidden layer nodes in the network structure generally relies on the method of trial calculation, but this method has a large amount of calculation, and it is not easy to determine the pros and cons of the obtained prediction model structure

Images