A method for predicting the remaining life of rolling bearings based on dropout-sae and bi-lstm

Abstract

A rolling bearing RUL prediction method based on dropout‑SAE and Bi‑LSTM belongs to the field of bearing operating state prediction. The present invention aims to solve the problems of long model training time and low prediction accuracy existing in the existing rolling bearing RUL prediction method. The present invention proposes an improved SAE, that is, dropout-SAE performs unsupervised deep feature self-adaptive extraction on rolling bearing vibration signals. The network uses a new Tan activation function to replace the original sigmoid activation function, and uses the dropout method to achieve Its sparsity; at the same time, considering that the prediction method of the remaining service life of rolling bearings generally only considers past information and ignores future information, it is proposed to introduce a bidirectional long-short-term memory network as a prediction model of rolling bearing RUL. The experimental results on the two bearing data sets show that the proposed prediction method can not only improve the convergence speed of the model but also have a high accuracy rate.

Description

technical field [0001] The invention relates to a method for predicting the remaining life of a rolling bearing, which belongs to the field of predicting the running state of the bearing. Background technique [0002] As the most commonly used and easily damaged key components of rotating equipment, rolling bearings often directly affect the performance of the entire equipment. [1] . Therefore, it is of great practical significance to predict the remaining useful life (RUL) of rolling bearings. [0003] Feature extraction is an important prerequisite for rolling bearing RUL prediction. In recent years, deep learning has attracted widespread attention due to its powerful adaptive feature extraction capability and nonlinear function representation capability, and has provided a new solution for feature extraction of rolling bearing vibration signals. [2] . Reference [3] proposes an improved deep belief network, which directly uses the original vibration signal of rolling b...

AI Technical Summary

Problems solved by technology

[0007] In order to solve the problems of long model training time and low prediction accuracy in the existing rolling bearing RUL prediction method, the present invention further proposes a rolling bearing RUL prediction method based on dropout-SAE and Bi-LSTM

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