基于KLDA-IDBO-BP的装甲车发动机故障诊断

doi:10.12382/bgxb.2024.0083

摘要/Abstract

摘要：

润滑油在发动机中发挥作用时携带着大量关于发动机的状态信息,能够对发动机产生的故障进行表征,可利用其对发动机进行故障诊断。以某型装甲车辆发动机为研究对象,提出一种基于核线性判别和改进的蜣螂优化算法优化反向传播(Back Propagation,BP)神经网络的故障诊断方法。对获取的润滑油数据通过核线性判别分析进行降维处理,降维后的数据作为BP神经网络的输入,通过引入最优拉丁超立方、权重因子以及Levy飞行策略对蜣螂优化算法进行改进,进一步对BP神经网络的关键参数进行优化,建立故障诊断模型,实现对测试数据的故障预测。实验结果验证了新方法在进行故障诊断预测方面的有效性,为装甲车辆发动机的维护和修理提供了科学依据。

关键词: 润滑油信息, 发动机, 故障诊断, 蜣螂优化算法, 反向传播神经网络, 核线性判别分析

Abstract:

Lubricating oil plays a pivotal role in engines due to carrying a wealth of information about the engine state,and is crucial for characterizing the faults of engine.The engine of an armored vehicle is studied,and a fault diagnosis method for the engine is proposed,which leverages the kernel linear discriminant analysis (KLDA) and an improved dung beetle optimization (DBO) algorithm to optimize a back propagation (BP) neural network.The dimensionality reduction of the acquired lubricating oil data is performed through KLDA,and the dimensionality reduced data is taken as input for the BP neural network.The DBO algorithm is then enhanced by integrating optimal Latin hypercube sampling method,weighting factors and Levy flight strategy in order to further optimize the key parameters of neural network.A fault diagnosis model is established to predict the faults in test data effectively.Experimental results affirm the proposed method’s efficacy in rapidly and accurately predicting the faults,providing a scientific basis for the maintenance and repair of engines in armored vehicles.

Key words: lubricating oil information, engine, fault diagnosis, dung beetle optimization algorithm, back propagation neural network, kernel linear discriminant analysis

中图分类号:

TJ811

李英顺, 于昂, 李茂, 贺喆, 刘师铭. 基于KLDA-IDBO-BP的装甲车发动机故障诊断[J]. 兵工学报, 2025, 46(3): 240083-.

LI Yingshun, YU Ang, LI Mao, HE Zhe, LIU Shiming. Fault Diagnosis of Armored Vehicle Engine Based on KLDA-IDBO-BP[J]. Acta Armamentarii, 2025, 46(3): 240083-.

图/表 11

参考文献 22

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故障类型	油液温度	油液压力	伴随现象
润滑油泵故障	不变	偏低	油液水分、密度产生变化,伴随异响
润滑油滤器阻塞	偏高	偏低	油液污染度、磨粒含量上升
润滑油冷却器故障	偏高	不变	油液黏度下降, 变稀
磨损机械故障	不变	偏高	油液磨粒含量上升,水分、密度产生变化

故障类型	油液温度	油液压力	伴随现象
润滑油泵故障	不变	偏低	油液水分、密度产生变化,伴随异响
润滑油滤器阻塞	偏高	偏低	油液污染度、磨粒含量上升
润滑油冷却器故障	偏高	不变	油液黏度下降, 变稀
磨损机械故障	不变	偏高	油液磨粒含量上升,水分、密度产生变化

故障类型编号	故障名称	训练集数据组数	测试集数据组数
1	润滑油冷却器故障	65	35
2	润滑油泵故障	65	35
3	润滑油滤器阻塞	65	35
4	磨损机械故障	65	35
5	其他故障	65	35

故障类型编号	故障名称	训练集数据组数	测试集数据组数
1	润滑油冷却器故障	65	35
2	润滑油泵故障	65	35
3	润滑油滤器阻塞	65	35
4	磨损机械故障	65	35
5	其他故障	65	35

水分/ %	密度/ (kg·m^-3)	温度/ ℃	黏度/ (mm²·s^-1)	污染度级	磨粒浓度	故障类型
0.16	841.52	88.93	70.86	2.23	3.28	1
0.15	842.36	68.92	27.36	2.26	3.35	1
0.11	690.63	31.13	86.98	1.95	2.89	2
0.18	850.42	55.37	26.87	2.15	1.43	2
0.09	870.27	20.81	89.45	1.93	2.18	3
0.14	673.46	54.74	46.46	1.85	4.55	3
0.21	899.05	89.37	62.21	2.21	5.29	4
0.15	880.73	38.39	90.75	2.63	4.36	4
0.18	697.30	50.54	61.56	2.23	3.98	5
0.06	828.33	20.35	70.86	2.26	2.85	5