A CNN and LSTM-based rolling bearing residual service life prediction method

Abstract

The invention discloses a CNN and LSTM-based rolling bearing residual service life prediction method, and relates to the field of rolling bearing life prediction. The method aims to solve the problemthat residual service life (RUL) prediction of a rolling bearing is difficult in two modes of performance degradation gradual change faults and sudden faults. The method comprises the following stepsof: firstly, carrying out FFT (Fast Fourier Transform) on an original vibration signal of the rolling bearing, then carrying out normalization processing on a frequency domain amplitude signal obtained by preprocessing, and taking the frequency domain amplitude signal as the input of a CNN (Convolutional Neural Network); The CNN is used for automatically extracting data local abstract informationto mine deep features, and the problem that a traditional feature extraction method depends too much on expert experience is avoided. the deep features are input into an LSTM network, a trend quantitative health index is constructed, and a failure threshold value is determined at the same time; And finally, smoothing processing is carried out by using a moving average method, eliminating local oscillation, and a future failure moment is predicted by using polynomial curve fitting to realize rolling bearing RUL prediction. And the prediction result can be well close to the real life value.

Description

technical field [0001] The invention relates to a method for predicting the remaining service life of a rolling bearing, and relates to the field of predicting the remaining service life of the rolling bearing. Background technique [0002] At present, rolling bearings are widely used in many rotating machinery equipment. As one of the basic components of rotating machinery, its operating status plays a vital role in the safe and reliable operation of equipment. Once the rolling bearing fails, it will lead to a series of negative effects, such as prolonging the downtime, causing serious accidents and even casualties, etc. [1-3] . Therefore, accurate prediction of bearing remaining useful life (RUL) is of great significance for preventive maintenance decisions of rotating machinery [4,5] . [0003] Generally, existing fault prediction and health management methods can be divided into three categories: physical model-based methods, data-driven methods, and hybrid methods [...

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Problems solved by technology

[0006] The technical problem to be solved by the present invention is: In order to solve the problem that the existing rolling bearing vibration signal feature extraction method is too dependent on expert experience, and the remaining service life prediction method does not consider the two modes of rolling bearing performance degradation gradual failure and sudden failure, there are The trend of health indicators cannot take into account the two modes at the same time, which affects the prediction accuracy

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