Electromechanical device nonlinear failure prediction method

Abstract

The invention relates to an electromechanical device nonlinear failure prediction method, comprising the following steps: 1, obtain data which can represent the running state of a device and select a section continuous vibration signal which has a long course and is sensitive to the failure to analyze; 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 noise reduction to the vibration signal by a lifting wavelet method; 4, decompose the vibration signal after the noise reduction to corresponding characteristic bandwidths; 5, obtain a low dimension manifold character by utilizing a typical predicted characteristic bandwidth and adopting a nonlinear manifold learning method through decoupling of topological mapping and non-failure energy information; 6, carry out intelligent failure prediction with long course trend in a time domain by utilizing a recurrent neural network which has the dynamic self-adaptive characteristic and a first dimension of the low dimension manifold character as a neural network input. The lifting wavelet method is adopted in the invention, the algorithm is simple, the arithmetic speed is high, and the used memory is less, thereby being suitable for the characteristic bandwidth abstraction of failure character. The electromechanical device nonlinear failure prediction method can be widely applied to the failure prediction of all kinds of electromechanical devices.

Description

technical field [0001] The invention relates to a method for predicting a failure of electromechanical equipment, in particular to a method for predicting a nonlinear failure of electromechanical equipment. Background technique [0002] Fault prediction is a key technology to ensure the long-term safety and full-load operation of electromechanical equipment, and it is one of the focuses of electromechanical fault diagnosis research. At present, the research and application of fault analysis technology for electromechanical equipment at home and abroad are mainly focused on fault diagnosis, and the main focus is on the state and degree of fault. few. In the fault prediction of electromechanical equipment, feature extraction is an important link and a difficult problem in fault prediction. The complex electromechanical system is a nonlinear system, and its operating state has nonlinear characteristics. Although the equipment operating data provides extremely rich and detaile...

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

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