Method for predicting residual service life of rolling bearing based on EEMD-MCNN-GRU

Abstract

The invention discloses a method for predicting the residual service life of a rolling bearing based on EEMD-MCNN-GRU. The method comprises the following steps: preprocessing full-life-cycle vibrationdata of the rolling bearing; utilizing the ensemble empirical mode to construct degradation characteristics of the bearing vibration signals on different time scales so as to construct a degradationcharacteristic data set of each rolling bearing; integrating a multi-scale convolutional neural network feature extraction layer and a gate control cycle unit neural network time sequence prediction layer to build a residual life prediction model; normalizing the degradation characteristic data set input into the prediction model; carrying out segmentation processing on the normalized data; dividing the degradation feature data set into a training set and a test set, and taking the training data as the input of the whole prediction network model; and smoothing the prediction model output by using a moving average method, and outputting an optimal life prediction result. The prediction method which is more reliable and higher in robustness and generalization is provided for analysis of theresidual service life of the rolling bearing.

Description

Technical field [0001] The invention relates to the field of failure prediction and health management of mechanical equipment, in particular to a method for predicting the remaining service life of a rolling bearing based on EEMD-MCNN-GRU. Background technique [0002] Equipment failure prediction and health management (Prognostics Health Management, PHM) is an important part of the normal operation and maintenance management of equipment, and remaining useful life (RUL) is one of the key technologies to realize PHM. Rolling bearings are widely used in various mechanical transmission components, and are one of the key components to ensure the reliable operation of equipment. Its remaining life is an important index to measure the performance of rolling bearings. Therefore, it is of great significance to carry out research on the life prediction of rolling bearings. The method for predicting the remaining life of equipment has been developed so far, and it is mainly divided into t...

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Problems solved by technology

[0008] The technical problem to be solved by the present invention is to provide an EEMD-MCNN based EEMD-MCNN that can effectively solve the problems of incomplete feature extraction, weak prediction model robustness, and low prediction accuracy existing in the existing rolling bearing remaining service life prediction research. -GRU's prediction method for the remaining service life of rolling bearings

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