Research on Life Prediction Method of Motor Bearings

被引：0

作者：

Pang, Shaomeng ^{[1
]}

Tong, Qingbin ^{[1
,2
]}

Lu, Feiyu ^{[1
]}

Feng, Ziwei ^{[1
]}

Wan, Qingzhu ^{[3
]}

An, Guoping ^{[1
,4
]}

Cao, Junci ^{[1
]}

Guo, Tao ^{[5
]}

机构：

[1] Beijing Jiaotong Univ, Sch Elect Engn, Beijing 100044, Peoples R China

[2] Beijing Rail Transit Elect Engn Technol Res Ctr, Beijing 100044, Peoples R China

[3] North China Univ Technol, Sch Elect & Control Engn, Beijing 100144, Peoples R China

[4] Natl Railway Adm Peoples Republ China, Ctr Safety Technol, Beijing 100160, Peoples R China

[5] CRRC Tangshan Locomot & Rolling Stock Co Ltd, Bogie Technol Ctr, Tangshan 064000, Peoples R China

来源：

PROCEEDINGS OF THE 3RD INTERNATIONAL SYMPOSIUM ON NEW ENERGY AND ELECTRICAL TECHNOLOGY | 2023年 / 1017卷

基金：

北京市自然科学基金;

关键词：

Motor bearings; Life prediction; Reliability analysis; SFAM neural network;

D O I：

10.1007/978-981-99-0553-9_8

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

In order to better achieve the life prediction and reliability analysis of motor bearings, a bearing life prediction method based on deep learning is proposed in this paper. The method firstly extracts features from the original vibration signal of the bearing and uses Weibull distribution to fit the extracted features. Then the fitted features are applied to the training phase of the SFAM(Simplified Fuzzy ARTMAP) neural network, and the extracted original features are applied to the testing phase, after SFAM neural network classification, a category representing the bearing degradation rate is given for each input vector. Finally, the classification results are made more continuous by a smoothing algorithm. The results show that this method can realize the life prediction and reliability analysis of motor bearings, and it is universal.

引用

页码：60 / 67

页数：8

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