A Fault Diagnosis Method of Aircraft Hydraulic System Based on SSA-DBN

被引：3

作者：

Cui, Jianguo ^{[1
]}

Song, Xue ^{[1
]}

Cui, Xiao ^{[2
]}

Du, Wenyou ^{[1
]}

Liu, Dong ^{[1
]}

Yu, Mingyue ^{[1
]}

Jiang, Liying ^{[1
]}

Wang, Jinglin ^{[3
]}

机构：

[1] Shenyang Aerosp Univ, Sch Automat, Shenyang 110136, Peoples R China

[2] AVIC Aerodynam Res Inst, Model Balance & Wind Tunnel Equipment Dept 5, Shenyang 110034, Peoples R China

[3] Aviat Key Lab Sci & Technol Fault Diag & Hlth Man, Shanghai 201601, Peoples R China

来源：

2022 34TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC | 2022年

关键词：

Aircraft Hydraulic System; Fault Diagnosis; Deep Belief Network (DBN); Salp Swarm Algorithm (SSA);

D O I：

10.1109/CCDC55256.2022.10034292

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The hydraulic system is one of the key systems on the aircraft, with the rapid development of aviation technology, the structure of the aircraft hydraulic system is becoming more and more complex, and there are many types of faults, which make it difficult to perform effective fault diagnosis. Therefore, in order to improve the accuracy of fault diagnosis of aircraft hydraulic system, this paper proposes a fault diagnosis method of aircraft hydraulic system based on SSA-DBN. Firstly, it adopts the status monitoring data of a certain type of aircraft hydraulic system, the Deep Belief Network (DBN) fault diagnosis network is established. On this basis, the number of nodes in the hidden layer of DBN network is optimized by using Salp Swarm Algorithm (SSA). According to the obtained optimal optimization parameters, the optimal DBN fault diagnosis model of aircraft hydraulic system is established. The fault technology of aircraft hydraulic system is studied by using the established optimal DBN fault diagnosis model of aircraft hydraulic system. The results show that the diagnostic accuracy of the SSA-DBN fault diagnosis model is obviously better than that of DBN, and it has a good application prospect.

引用

页码：3036 / 3041

页数：6

共 50 条

[1] Fault Diagnosis of Bearings Using Wavelet Packet Energy Spectrum and SSA-DBN
Qu, Jinglei
Cheng, Xueli
Liang, Ping
Zheng, Lulu
Ma, Xiaojie
PROCESSES, 2023, 11 (07)
[2] Stall Warning Algorithm of Axial Compressor Based on SSA-DBN
Qiu, Xiao-Hong
Chen, Jia-Li
Ao, Zi-Ying
Journal of Computers (Taiwan), 2022, 33 (03) : 59 - 71
[3] Intelligent classification method of water faults for proton exchange membrane fuel cell based on improved SSA-DBN
Liu X.
Han Y.
Chen W.
Li Q.
Yang Z.
Dianli Zidonghua Shebei/Electric Power Automation Equipment, 2024, 44 (04): : 18 - 24
[4] Condition Monitoring of Rolling Bearing Based on Multi-Order FRFT and SSA-DBN
Ma, Jie
Li, Shule
Wang, Xinyu
SYMMETRY-BASEL, 2022, 14 (02):
[5] Rolling bearing fault diagnosis based on SSA optimized self-adaptive DBN
Gao, Shuzhi
Xu, Lintao
Zhang, Yimin
Pei, Zhiming
ISA TRANSACTIONS, 2022, 128 : 485 - 502
[6] A Method of Fault Diagnosis Based on DE-DBN
Wang, Yajun
Zhang, Jia
Deng, Fang
PROCEEDINGS OF 2017 CHINESE INTELLIGENT AUTOMATION CONFERENCE, 2018, 458 : 209 - 217
[7] Vibration Fault Prediction Method of Hydraulic Turbine System Based on Deep Learning LSTM‑DBN
Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis, 2022, 42 (06): : 1233 - 1238
[8] Research on Fault Diagnosis System for Aircraft Hydraulic Power Carts Based on Fuzzy Diagnosis Theory
Zhao, Weiqiang
Liu, Yongxian
Lu, Mowu
ADVANCES IN MANUFACTURING TECHNOLOGY, PTS 1-4, 2012, 220-223 : 607 - +
[9] Fault diagnosis and location of hydraulic system of domestic civil aircraft based on logic data
Feng Y.
Pan W.
Lu C.
Liu J.
Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University, 2022, 40 (04): : 732 - 738
[10] Research of fault diagnosis system in hydraulic system based on fuzzy fault tree analysis method
Zuo, Jianmin
Wang, Shucheng
Nanjing Hangkong Hangtian Daxue Xuebao/Journal of Nanjing University of Aeronautics & Astronautics, 1999, 31 (06): : 716 - 721

← 1 2 3 4 5 →