Fault diagnosis of aircraft fuel pump based on transfer learning

被引：1

作者：

Qiu, Zaihui ^{[1
]}

Miao, Yang ^{[2
]}

Hong, Wei ^{[3
]}

Jiang, Yuncheng ^{[1
]}

Liu, Yi ^{[4
]}

Pan, Jun ^{[4
]}

Li, Xin ^{[5
]}

机构：

[1] Beijing Univ Technol, Dept Mat & Mfg, Beijing, Peoples R China

[2] Beijing Univ Technol, Beijing Key Lab Adv Mfg Technol, Beijing, Peoples R China

[3] Huazhong Univ Sci & Technol, Sch Phys, Wuhan, Peoples R China

[4] Aviat Ind Corp China, Nanjing Electromech Hydraul Engn Res Ctr, Beijing, Peoples R China

[5] North China Univ Technol, Beijing, Peoples R China

来源：

PROCEEDINGS OF 2021 7TH INTERNATIONAL CONFERENCE ON CONDITION MONITORING OF MACHINERY IN NON-STATIONARY OPERATIONS (CMMNO) | 2021年

基金：

中国国家自然科学基金;

关键词：

transfer learning; diagnosis; fuel pump; broad-band frequency; tradaboost;

D O I：

10.1109/CMMNO53328.2021.9467576

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

Sometimes there could be sufficient data in used aircraft fuel pump. A few data in a specific new pump, which low accuracy of diagnosis, are obtained. In this paper, a fault diagnosis method of centrifugal aircraft fuel pump based on migration learning is proposed. The features from the fault data of similar pumps, which are used as auxiliary data set, are extracted. Then, the training data set, which is composed of auxiliary data and a few amount of diagnostic target data, is established and is trained by TrAdaboost transfer learning algorithm. Finally, compared with traditional machine learning, it shows that the transfer learning has obvious diagnostic advantages in the situation of insufficient diagnostic target data.

引用

页码：171 / 175

页数：5

共 50 条

[21] Fault diagnosis of seawater pump based on transfer kernel locality preserving projection
Zhu, Zhiyu
Cui, Shiyu
2020 IEEE 18TH INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN), VOL 1, 2020, : 822 - 827
[22] Mechanical fault diagnosis based on deep transfer learning: a review
Yang, Dalian
Zhang, Wenbin
Jiang, Yongzheng
MEASUREMENT SCIENCE AND TECHNOLOGY, 2023, 34 (11)
[23] Multiscale Transfer Learning Based Fault Diagnosis of Rolling Bearings
Tang, Rong
Sun, Xinjie
Wang, Shubiao
Chen, Zhe
ARTIFICIAL INTELLIGENCE AND ROBOTICS, ISAIR 2023, 2024, 1998 : 366 - 375
[24] A bearing fault diagnosis based on similarity measurement for transfer learning
Xu Y.
Ma J.
Chen L.
Shen C.
Li Q.
Kong L.
Zhendong yu Chongji/Journal of Vibration and Shock, 2022, 41 (16): : 217 - 223
[25] A Novel Approach to Transformer Fault Diagnosis Based on Transfer Learning
Chao, Su
Hao, Bai
Chen, Wenquan
RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING, 2022, 15 (01) : 41 - 50
[26] A fault diagnosis method of bearings based on deep transfer learning
Huang, Min
Yin, Jinghan
Yan, Shumin
Xue, Pengcheng
SIMULATION MODELLING PRACTICE AND THEORY, 2023, 122
[27] Diesel engine fault diagnosis based on deep transfer learning
Song Y.
Ma G.
Pei G.
Zhang J.
Zhendong yu Chongji/Journal of Vibration and Shock, 2023, 42 (21): : 219 - 226
[28] A Fault Diagnosis Approach for Airborne Fuel Pump Based on EMD and Probabilistic Neural Networks
Jiao, Xiaoxuan
Jing, Bo
Huang, Yifeng
Liang, Wei
Xu, Guangyue
2016 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-CHENGDU), 2016,
[29] Research on fault diagnosis of airborne fuel pump based on EMD and probabilistic neural networks
Jiao, Xiaoxuan
Jing, Bo
Huang, Yifeng
Li, Juan
Xu, Guangyue
MICROELECTRONICS RELIABILITY, 2017, 75 : 296 - 308
[30] Diesel Engine PT Pump Fault Diagnosis Based on the Characteristics of Its Fuel Pressure
Wang, Xin-jun
Cai, Yan-ping
Lin, Xu-ze
INTERNATIONAL CONFERENCE ON APPLIED COMPUTING, COMPUTER SCIENCE, AND COMPUTER ENGINEERING (ICACC 2013), 2014, 7 : 84 - 89

← 1 2 3 4 5 →