Acta Armamentarii ›› 2025, Vol. 46 ›› Issue (3): 240083-.doi: 10.12382/bgxb.2024.0083
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LI Yingshun1,*(), YU Ang1, LI Mao2, HE Zhe2, LIU Shiming1
Received:
2024-01-26
Online:
2025-03-26
Contact:
LI Yingshun
CLC Number:
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-.
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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 |
Table 1 Failure factors of lubrication system
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 |
Table 2 Classification table of fault data
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 |
Table 3 Engine lubricant partial data
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 |
诊断模型 | 运行时间/s | 测试准确率/% | 均方误差 |
---|---|---|---|
BP神经网络 | 4.25 | 63.4 | 0.0327 |
SSA-BP神经网络 | 3.68 | 86.5 | 0.0175 |
DBO-BP神经网络 | 3.54 | 89.1 | 0.0159 |
IDBO-BP神经网络 | 2.83 | 96.9 | 0.0103 |
Table 4 Comparative analysis of four fault diagnosis models
诊断模型 | 运行时间/s | 测试准确率/% | 均方误差 |
---|---|---|---|
BP神经网络 | 4.25 | 63.4 | 0.0327 |
SSA-BP神经网络 | 3.68 | 86.5 | 0.0175 |
DBO-BP神经网络 | 3.54 | 89.1 | 0.0159 |
IDBO-BP神经网络 | 2.83 | 96.9 | 0.0103 |
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