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
收藏
页码：3036 / 3041
页数：6
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