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

A technology for automobile reducer and life prediction, which is applied in neural learning methods, special data processing applications, biological neural network models, etc., and can solve problems such as long-term dependence of time series

Pending Publication Date: 2021-09-14
ZHEJIANG UNIV OF TECH
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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. B

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

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Embodiment Construction

[0033] The present invention will be further described below in conjunction with the accompanying drawings.

[0034] Figure 1 to Figure 5 , an LSTM-based method for predicting the remaining service life of electric vehicle reducers, including the following steps:

[0035] S1: The construction of the degradation characteristic data set of the electric vehicle reducer, the process is as follows: extract the characteristics of 9 electric vehicle reducer degradation signals, including the root mean square value, standard deviation, and variance in the time domain; the root mean square value in the frequency domain , average frequency, center of gravity frequency; the energy spectrum of the fourth frequency band of the wavelet packet, the energy spectrum of the sixth frequency band of the wavelet packet, and the energy spectrum of the first EMD component in the time-frequency domain, such as Figure 4 shown.

[0036] S2: Normalize the characteristic data so that its characterist...

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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...

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Application Information

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IPC IPC(8): G06F30/15G06F30/27G06N3/04G06N3/08G06F119/04
CPCG06F30/15G06F30/27G06N3/08G06F2119/04G06N3/044
Inventor 吕冰海徐家豪赵文宏陈锋邓乾发段世祥祝佳俊傅琳
Owner ZHEJIANG UNIV OF TECH
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