1 问题描述
2 理论基础
2.1 门控循环单元
2.2 改进的麻雀搜索算法
3 BRWSSA算法优化GRU的实现过程
4 滑油系统故障诊断试验验证
表1 不同模型对测试数据集的诊断准确率 |
故障诊断模型 | 故障诊断准确率/% |
---|---|
GRU | 84 |
SSA-GRU | 91.3 |
BRWSSA-GRU | 94 |
Journal of Shenyang Aerospace University >
Fault diagnosis of aircraft engine lubricating oil system based on BRWSSAGRU
Received date: 2023-10-13
Online published: 2023-12-22
In response to the problem of unstable fault diagnosis performance of neural network caused by artificially selected parameters,as well as the problems of narrowing the optimization range and falling into the local optima caused by the randomness of sparrow search algorithm (SSA) population initialization,opposition-based learning (OBL) was used to optimize the initialization process of sparrow population in SSA algorithm and expand the search range. Combined with the random walk strategy (random walk,RW),the optimal sparrow in the optimization process was disturbed to improve the local search ability of the algorithm and reduce the risk of the algorithm falling into local optimum.On this basis,an improved BRWSSA algorithm was used to optimize the number of hidden layer nodes of gate recurrent unit (GRU),and a fault diagnosis model of engine oil system based on BRWSSA-GRU was designed. In order to verify the effectiveness of the fault diagnosis model,two fault diagnosis models,GRU and SSA-GRU,were also designed. Finally,comparative experiments were conducted to validate three different fault diagnosis models,GRU,SSA-GRU,and BRWSSA-GRU using the same lubricating oil system dataset. The results show that the diagnostic accuracy of the proposed BRWSSA-GRU fault diagnosis model is obviously better than that of GRU and SSA-GRU methods,which verifies the effectiveness of the designed BRWSSA-GRU fault diagnosis model.
Jianguo CUI , Wei XU , Xiao CUI , Mingyue YU , Yuqi WANG , Xiaochu TANG . Fault diagnosis of aircraft engine lubricating oil system based on BRWSSAGRU[J]. Journal of Shenyang Aerospace University, 2023 , 40(5) : 32 -37 . DOI: 10.3969/j.issn.2095-1248.2023.05.005
表1 不同模型对测试数据集的诊断准确率 |
故障诊断模型 | 故障诊断准确率/% |
---|---|
GRU | 84 |
SSA-GRU | 91.3 |
BRWSSA-GRU | 94 |
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