Electric vehicle speed reducer remaining service life prediction method based on LSTM

Abstract

The invention discloses an electric vehicle speed reducer remaining service life prediction method based on LSTM. The method comprises the steps of: preprocessing full-life-cycle vibration data of an electric vehicle speed reducer; constructing degradation characteristics of reducer vibration signals on a time domain, a frequency domain and a time-frequency domain, thereby constructing a degradation characteristic data set of each electric vehicle reducer, and constructing a residual life prediction model by using an LSTM neural network; normalizing the degradation characteristic data set input into the prediction model; carrying out segmentation processing on the normalized data; dividing the degradation characteristic data set into a training set and a test set, and taking training data as the input of the whole prediction network model; and performing network parameter adjustment by using a single variable method, and outputting an optimal life prediction result. The prediction method which is more reliable and higher in generalization is provided for analysis of the remaining service life of the speed reducer of the electric vehicle.

Description

technical field [0001] The invention belongs to the technical field of prediction of the remaining service life of an electric vehicle reducer, and in particular relates to an LSTM-based method for predicting the remaining service life of an electric vehicle reducer. Background technique [0002] Electric vehicle reducer is the most critical component in electric vehicles, and its working condition is closely related to the normal operation of the vehicle. Therefore, it is of great significance to predict the remaining life of the electric vehicle reducer. At present, in the field of remaining life prediction, there are mainly mechanism-based analysis and shallow machine learning (1) based on mechanism analysis, there are difficulties in modeling and the shortcomings of not considering the local deformation of the specimen, and the prediction accuracy is not high; (2) shallow machine learning Compared with mechanism analysis, learning uses a data-driven method to make predi...

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the deficiencies of the prior art, the present invention provides a method for predicting the remaining service life of electric vehicle reducers based on LSTM. Based on the field of deep learning, the proposed LSTM prediction model has advantages in processing time series data and solves the problem of time Long-run dependency problems in sequences

Images